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Best content from the best source handpicked by Shyam. The source include The Harvard University, MIT, Mckinsey & Co, Wharton, Stanford,and other top educational institutions. domains include Cybersecurity, Machine learning, Deep Learning, Bigdata, Education, Information Technology, Management, others.

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    Lessons From the Frontier: How to Make Blended Learning Truly Work

















    Four years ago, Aspire Public Schools senior leadership decided that we should explore the use of blended learning in our schools to find out if it was “right” for us and asked me to lead the effort. I was not a particularly techie educator, but I did believe that it was time for us to figure out what role technology should play in our instructional model, which I cared deeply about, having coached Aspire teachers for years.

    I was what colleagues politely called a “healthy skeptic.” I had spent six years in the classroom as a teacher, and then another 10 years coaching teachers and developing curriculum. Like my colleagues at Aspire, I deeply believed that we needed to teach the individual student--or to use the industry jargon, that “differentiated and individualized instruction” would help our students.

    But we had yet to be sold on the power of technology in our instructional program for increasing student achievement. Models hadn’t been proven; software had little research behind it. The enthusiasm of national and family foundations (which have traditionally funded different teaching and learning initiatives) was both a blessing and a curse: They were excited about funding us to try changing the ways in which education could be delivered, yet many were also very interested in blended learning as a cost-cutting device, which in California with its low per pupil funding amount, added an extra burden to this work.

    And yet, there was that tantalizing promise: We recognized that as adults, our lives depend heavily on technology. We knew that our students who we sent off to college each year would use technology in everything they did in college. And even though we knew our teaching practices were really good, they were not getting all of our students adequately prepared to succeed in and graduate from college--and that we needed new strategies and tools to increase our students’ achievement. 

    Blended learning was unproven, yes. But it just might offer us a way to give students greater access and experience with technology, learning experiences that were truly differentiated to their level with real-time feedback, and give teachers more immediate and individualized student data. On many days, I felt something like a cowgirl out on the cutting edge of the untamed prairie or an astronaut on the distant edge of the universe.

    I wasn’t alone, however. At schools scattered across the US, teams of other teachers and instructional design coaches were beginning to work through the same problems. I began connecting with folks from the KIPP Foundation, Summit Public Schools, Rocketship Education, E. L. Haynes Public Charter School, the Alliance College-Ready Public Schools, Cornerstone Charter Schools, Highline Public Schools (Washington), Mastery Schools and FirstLine Schools. 

    We started slowly--sharing with each other our software choices and lessons learned. We spent a great deal of time commiserating over the phone. Gradually, we began meeting twice a year to dig more deeply into the work with each other: our models, our teacher training plans, our human capital strategies, our scaling plans. We started to believe that technology could deliver on its promise within our instructional programs.

    As our teachers started piloting the work, they began providing powerful examples of how “going blended” was changing the work of teaching for them. “I feel my job has been more purposeful,” shared Nancy Castro, kindergarten teacher at Aspire Titan Academy. “The data that is provided through the software has allowed me to focus more on my guided reading groups and also target my student needs.

     I feel blended learning has allowed me to use my guided reading and math time more effectively. I get to spend a little more time with my guided reading groups, I get to teach my math lessons during computer time, and I get to pull small groups to work with my low students.”

    The most fundamental lesson we learned was that blended learning is a model that puts student learning at the center of learning. That makes blended learning a critical step towards whatever the future of teaching and learning in our schools will be. Blended learning sets up teachers and students to continually adapt and iterate in ways that previous school reforms have not.

     It uses technology but keeps a strong emphasis on learning. Realistically, it means not “going blended” because you think it’s a good idea. Rather, you’re going blended because you want to figure out how technology can help you solve a targeted problem: increase student achievement given your context, school culture, and the overall willingness of your teachers to tackle the challenges your students face.

    These days, I’m still thinking about how to improve on this work as more Aspire schools adopt the blended learning model. And I’ve pulled together the lessons and observations from our teachers and the dozens of other cowboys and cowgirls who have been trying to tame blended learning into a new book, Go Blended!

     A Handbook for Blending Technology in Schools. Part of Aspire’s mission is to share promising practices with other forward-thinking educators, and this book allows us to share what we’re learning in a much broader way.up to speed? How do school leaders train teachers and identify when teachers and classrooms are ready for blended learning?

    And I’m excited that I’ll be working with EdSurge as part of its Education Leader Day sessions in its Summits to share these lessonswith educators across the country.

    I hope you’ll take this journey with us, and share back your lessons, so we can learn from you, too.


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    How to Get Your Students to Come to Class Prepared



    Imagine a world where students came to class prepared. Class time would be so much more productive and enjoyable for teachers and students alike. We would have informed class discussions and focus on students applying, analyzing, and evaluating the material under our expert guidance.
    Prepared students are not a mirage. Students will come to class prepared, but it requires a different course design. Consider a course that uses class preparation assignments (CPAs) to inform and stimulate class discussion and a definitional grading system that makes being prepared for class non-negotiable.
    The CPAs are reading assignments accompanied by informal writing assignments consisting of four to eight questions. The CPA questions serve as a guide to the students in their reading, prepares them for class, and serves as a basis for class discussion.
    The CPAs are graded pass-fail only. Students bring two copies of their CPA answers to class—one that they place on the front desk as they come into class and the other that they keep for class discussion. To earn credit for a CPA, a student needs to show a good faith effort on their answers to each question and they need to attend class to contribute to class discussion.
    In a definitional grading system the pedagogical assumption is that different categories of work are each important, and the teacher does not want one category to compensate for the other in any way. In the table below there are two distinct categories of work: the CPAs, and the exams and quizzes.
    table for class preparation assignments
    For a student to get a particular course grade, she must meet or exceed the standard for each category of work. If a student gets an A average on the exams and quizzes but earns credit for only 75 percent of the CPAs, she receives a C for her grade. If a student earns credit for 90 percent or more of the CPAs, but gets a C average on the exams and quizzes, she receives a C for her course grade. The definition of an A student is one who not only does A work on the exams and quizzes, but who also comes to class prepared at least 90 percent of the time.
    If you adopt this course design, students will come to class prepared. Therefore, you won’t have to lecture as if the students are seeing the material for the first time. Instead, you can engage the students with active learning strategies that go after higher-level learning and skill development.
    Grading the CPAs is easy. By the end of class the students’ CPA answers are dated, having been used and responded to in class. We simply scan the CPAs for whether the student showed a good faith effort.
    The level of difficulty of the CPA questions has to be chosen with care. Make the questions too simple and the students will scan the reading for the answers. Make the questions too tough and the students will become frustrated and feel that the course design is unfair. Additionally, if the questions are too difficult, students will come to your office for help before class and you will lose the efficiency of the students working together on the harder aspects of the material in class and your ability to respond to their answers as a whole in class.
    Pay attention to what you name the preparation assignments. We specifically chose not to call them homework assignments because students are accustomed to getting credit on homework assignments without coming to class. With our CPAs, students only get credit if they come to class.
    Be sure to use the CPAs as the foundation for class discussion. Early in the semester, it is generally a good idea to cover the CPA questions in a linear fashion. But later, there can be magic in the classroom as the practice of preparation allows for the discussion of ideas in a non-linear fashion.
    The CPA-definitional grading system design has worked well across different course levels from introductory courses to graduate courses, and across different institutions from large land-grant institutions to private liberal arts colleges. As we and others have learned, if you use this course design, students will come to class prepared and class time will be much more productive, dynamic, and fun for everyone.
    A tenet of this course design is that students with specific assignments can acquire the basic understanding of the material themselves before coming to class and that they need help primarily with critically applying and evaluating the material. In addition, so often college students are lectured to and talked at. Using the CPAs allows time and space for informed student voices. In the words of the classic Aretha Franklin song, we think that it shows students R-E-S-P-E-C-T.
    View at the original source

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    Changing Face Of Cricket, Experienced Through Social Data

    Sports is a passion for many across the globe. However, for Indians there is one sport in particular that is more than passion. It’s a religion that trumps all others and it’s called ‘Cricket’ – it’s a unifying force like no other. So why am I talking about Cricket given there’s so much information about this beautiful game already out there.
     Well, to start with the World Cup is currently on and the excitement all around is palpable. Of late, I’ve been intrigued about how Cricket is evolving for the viewer, organizer, players, advertisers, ground staff and everyone involved in the game. Yes, Cricket, Social Data and Analytics are coming together leading to possibilities never before imagined.
    Cricket has always been a breeding ground for data buffs with the multitude of statistics discussed and debated before, during and after a cricket match. This traditionally has been done using static data to come up with interesting infographics, blogs, charts. Today, we live in a social world like never before with the power and magnitude of social displayed through our networks, impact and influence. With the advent of social networks and social media feeds, we have unstructured data sources which combined with analytics opens a whole new window of possibilities.
    My social data gives me information on my favourite cricketers regularly based on what is said about them in the social world. What if this real time data is combined with sentiment analytics to help advertisers determine if a particular cricketer is marketable to a certain demography, segment, age group of viewers tuning in at a certain time and those are sure to lap up the advertisement? Advertisers would then know when a particular kind of viewer would most likely watch a particular advertisement and make changes real time to positively impact viewership.
    What if social data combined with information from IBM CrowdTracker together helps the World Cup tournament organizers understand where are the long queues to enter the stadium. Once they have this insight, they can immediately open additional gates, ticket scanners, crowd control management personnel, merchandise selling personnel to ensure long queues are a thing of history through real time data. Additionally, using social media information on best routes can be shared with fans who are yet to arrive at the stadium thereby ensuring they avoid crowds.

    Crowdtracker








    IBM being a leader in the space of Social and Analytics is right in the thick of things having been associated with major sporting events such as ‘The Masters’, US Open, Wimbledon to engage the audience, helping organizers build momentum coming into a tournament. In fact at the ‘Australian Open’ this year, the IBM CrowdTracker on IBM Analytics provided an enhanced fan experience and helped the organizers improve their operations and business success.
    With Watson analytics, IBM can now provide the cognitive analytics by understanding human interaction over social media to make informed suggestions based on unstructured social data. This also means fans can interact by asking questions to the Watson engine regarding the game, facilities, tournament. Organizers can prepare in advance, or make quick adjustments based on information coming out real time and lots more.
    This year, IBM India has tied up with Wisden where IBM’s cutting edge Analytics capabilities combined with Wisden’s proprietary ‘Impact Index’ is providing unique insights & experiences during this World Cup for the fans and organizers. There is a lot for us as fans and IBMers to contribute here.

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  • 02/18/15--05:27: The Discipline of Teams 0-18
  • The  Discipline  of  Teams




    It won’t surprise anyone to find an article on teams by Jon Katzenbach and Douglas Smith figuring into an issue devoted to high performance. While Peter Drucker may have been the first to point out that a team-based organization can be highly effective, Katzenbach and Smith’s work made it possible for companies to implement the idea.

    In this groundbreaking 1993 article, the authors say that if managers want to make better decisions about teams, they must be clear about what a team is. They define a team as “a small number of people with complementary skills who are committed to a common purpose, set of performance goals, and approach for which they hold themselves mutually accountable.” That definition lays down the discipline that teams must share to be effective.

    Katzenbach and Smith discuss the four elements—common commitment and purpose, performance goals, complementary skills, and mutual accountability—that make teams function. They also classify teams into three varieties—teams that recommend things, teams that make or do things, and teams that run things—and describe how each type faces different challenges.
    Early in the 1980s, Bill Greenwood and a small band of rebel railroaders took on most of the top management of Burlington Northern and created a multibillion-dollar business in “piggybacking” rail services despite widespread resistance, even resentment, within the company. The Medical Products Group at Hewlett-Packard owes most of its leading performance to the remarkable efforts of Dean Morton, Lew Platt, Ben Holmes, Dick Alberding, and a handful of their colleagues who revitalized a health care business that most others had written off. At Knight Ridder, Jim Batten’s “customer obsession” vision took root at theTallahassee Democrat when 14 frontline enthusiasts turned a charter to eliminate errors into a mission of major change and took the entire paper along with them.
    Such are the stories and the work of teams—real teams that perform, not amorphous groups that we call teams because we think that the label is motivating and energizing. The difference between teams that perform and other groups that don’t is a subject to which most of us pay far too little attention. Part of the problem is that “team” is a word and concept so familiar to everyone. (See the exhibit “Not All Groups Are Teams: How to Tell the Difference.”)

    Working Group

    • Strong, clearly focused leader
    • Individual accountability
    • The group’s purpose is the same as the broader organizational mission
    • Individual work products
    • Runs efficient meetings
    • Measures its effectiveness indirectly by its influence on others (such as financial performance of the business)
    • Discusses, decides, and delegates

    Team

    • Shared leadership roles
    • Individual and mutual accountability
    • Specific team purpose that the team itself delivers
    • Collective work products
    • Encourages open-ended discussion and active problem-solving meetings
    • Measures performance directly by assessing collective work products
    • Discusses, decides, and does real work together
    Or at least that’s what we thought when we set out to do research for our book The Wisdom of Teams(HarperBusiness, 1993). We wanted to discover what differentiates various levels of team performance, where and how teams work best, and what top management can do to enhance their effectiveness. We talked with hundreds of people on more than 50 different teams in 30 companies and beyond, from Motorola and Hewlett-Packard to Operation Desert Storm and the Girl Scouts.
    We found that there is a basic discipline that makes teams work. We also found that teams and good performance are inseparable: You cannot have one without the other. But people use the word “team” so loosely that it gets in the way of learning and applying the discipline that leads to good performance. For managers to make better decisions about whether, when, or how to encourage and use teams, it is important to be more precise about what a team is and what it isn’t.
    People use the word “team” so loosely that it gets in the way of learning and applying the discipline that leads to good performance.
    Most executives advocate teamwork. And they should. Teamwork represents a set of values that encourage listening and responding constructively to views expressed by others, giving others the benefit of the doubt, providing support, and recognizing the interests and achievements of others. Such values help teams perform, and they also promote individual performance as well as the performance of an entire organization. But teamwork values by themselves are not exclusive to teams, nor are they enough to ensure team performance. (See the sidebar “Building Team Performance.”)

    Nor is a team just any group working together. Committees, councils, and task forces are not necessarily teams. Groups do not become teams simply because that is what someone calls them. The entire workforce of any large and complex organization isnever a team, but think about how often that platitude is offered up.
    To understand how teams deliver extra performance, we must distinguish between teams and other forms of working groups. That distinction turns on performance results. A working group’s performance is a function of what its members do as individuals. A team’s performance includes both individual results and what we call “collective work products.” A collective work product is what two or more members must work on together, such as interviews, surveys, or experiments. Whatever it is, a collective work product reflects the joint, real contribution of team members.

    Teams differ fundamentally from working groups because they require both individual and mutual accountability. Teams rely on more than group discussion, debate, and decision, on more than sharing information and best-practice performance standards. Teams produce discrete work products through the joint contributions of their members. This is what makes possible performance levels greater than the sum of all the individual bests of team members. Simply stated, a team is more than the sum of its parts.
    Working groups are both prevalent and effective in large organizations where individual accountability is most important. The best working groups come together to share information, perspectives, and insights; to make decisions that help each person do his or her job better; and to reinforce individual performance standards. But the focus is always on individual goals and accountabilities. Working-group members don’t take responsibility for results other than their own. Nor do they try to develop incremental performance contributions requiring the combined work of two or more members.
    The first step in developing a disciplined approach to team management is to think about teams as discrete units of performance and not just as positive sets of values. Having observed and worked with scores of teams in action, both successes and failures, we offer the following. Think of it as a working definition or, better still, an essential discipline that real teams share: A team is a small number of people with complementary skills who are committed to a common purpose, set of performance goals, and approach for which they hold themselves mutually accountable.
    For managers to make better decisions about whether, when, or how to encourage and use teams, it is important to be more precise about what a team is and what it isn’t.
    The essence of a team is common commitment. Without it, groups perform as individuals; with it, they become a powerful unit of collective performance. This kind of commitment requires a purpose in which team members can believe. Whether the purpose is to “transform the contributions of suppliers into the satisfaction of customers,” to “make our company one we can be proud of again,” or to “prove that all children can learn,” credible team purposes have an element related to winning, being first, revolutionizing, or being on the cutting edge.
    Teams develop direction, momentum, and commitment by working to shape a meaningful purpose. Building ownership and commitment to team purpose, however, is not incompatible with taking initial direction from outside the team. The often-asserted assumption that a team cannot “own” its purpose unless management leaves it alone actually confuses more potential teams than it helps. In fact, it is the exceptional case—for example, entrepreneurial situations—when a team creates a purpose entirely on its own.
    Most successful teams shape their purposes in response to a demand or opportunity put in their path, usually by higher management. This helps teams get started by broadly framing the company’s performance expectation. Management is responsible for clarifying the charter, rationale, and performance challenge for the team, but management must also leave enough flexibility for the team to develop commitment around its own spin on that purpose, set of specific goals, timing, and approach.
    The best teams invest a tremendous amount of time and effort exploring, shaping, and agreeing on a purpose that belongs to them both collectively and individually. This “purposing” activity continues throughout the life of the team. By contrast, failed teams rarely develop a common purpose. For whatever reason—an insufficient focus on performance, lack of effort, poor leadership—they do not coalesce around a challenging aspiration.
    The best teams also translate their common purpose into specific performance goals, such as reducing the reject rate from suppliers by 50% or increasing the math scores of graduates from 40% to 95%. Indeed, if a team fails to establish specific performance goals or if those goals do not relate directly to the team’s overall purpose, team members become confused, pull apart, and revert to mediocre performance. By contrast, when purposes and goals build on one another and are combined with team commitment, they become a powerful engine of performance.
    Transforming broad directives into specific and measurable performance goals is the surest first step for a team trying to shape a purpose meaningful to its members. Specific goals, such as getting a new product to market in less than half the normal time, responding to all customers within 24 hours, or achieving a zero-defect rate while simultaneously cutting costs by 40%, all provide firm footholds for teams. There are several reasons:
    • Specific team-performance goals help define a set of work products that are different both from an organization-wide mission and from individual job objectives. As a result, such work products require the collective effort of team members to make something specific happen that, in and of itself, adds real value to results. By contrast, simply gathering from time to time to make decisions will not sustain team performance.
    • The specificity of performance objectives facilitates clear communication and constructive conflict within the team. When a plant-level team, for example, sets a goal of reducing average machine changeover time to two hours, the clarity of the goal forces the team to concentrate on what it would take either to achieve or to reconsider the goal. When such goals are clear, discussions can focus on how to pursue them or whether to change them; when goals are ambiguous or nonexistent, such discussions are much less productive.
    • The attainability of specific goals helps teams maintain their focus on getting results. A product-development team at Eli Lilly’s Peripheral Systems Division set definite yardsticks for the market introduction of an ultrasonic probe to help doctors locate deep veins and arteries. The probe had to have an audible signal through a specified depth of tissue, be capable of being manufactured at a rate of 100 per day, and have a unit cost less than a preestablished amount. Because the team could measure its progress against each of these specific objectives, the team knew throughout the development process where it stood. Either it had achieved its goals or not.

    • Specific goals allow a team to achieve small wins as it pursues its broader purpose. These small wins are invaluable to building commitment and overcoming the inevitable obstacles that get in the way of a long-term purpose. For example, the Knight Ridder team mentioned at the outset turned a narrow goal to eliminate errors into a compelling customer service purpose.
      As Outward Bound and other team-building programs illustrate, specific objectives have a leveling effect conducive to team behavior. 
    • When a small group of people challenge themselves to get over a wall or to reduce cycle time by 50%, their respective titles, perks, and other stripes fade into the background. The teams that succeed evaluate what and how each individual can best contribute to the team’s goal and, more important, do so in terms of the performance objective itself rather than a person’s status or personality.
    • Performance goals are compelling. They are symbols of accomplishment that motivate and energize. They challenge the people on a team to commit themselves, as a team, to make a difference. Drama, urgency, and a healthy fear of failure combine to drive teams that have their collective eye on an attainable, but challenging, goal. Nobody but the team can make it happen. It’s their challenge.
    The combination of purpose and specific goals is essential to performance. Each depends on the other to remain relevant and vital. Clear performance goals help a team keep track of progress and hold itself accountable; the broader, even nobler, aspirations in a team’s purpose supply both meaning and emotional energy.
    Virtually all effective teams we have met, read or heard about, or been members of have ranged between two and 25 people. For example, the Burlington Northern piggybacking team had seven members, and the Knight Ridder newspaper team had 14. The majority of them have numbered less than ten. Small size is admittedly more of a pragmatic guide than an absolute necessity for success. A large number of people, say 50 or more, can theoretically become a team. But groups of such size are more likely to break into subteams rather than function as a single unit.
    Why? Large numbers of people have trouble interacting constructively as a group, much less doing real work together. Ten people are far more likely than 50 to work through their individual, functional, and hierarchical differences toward a common plan and to hold themselves jointly accountable for the results.
    Large groups also face logistical issues, such as finding enough physical space and time to meet. And they confront more complex constraints, like crowd or herd behaviors, which prevent the intense sharing of viewpoints needed to build a team. As a result, when they try to develop a common purpose, they usually produce only superficial “missions” and well-meaning intentions that cannot be translated into concrete objectives. 
    They tend fairly quickly to reach a point when meetings become a chore, a clear sign that most of the people in the group are uncertain why they have gathered, beyond some notion of getting along better. Anyone who has been through one of these exercises understands how frustrating it can be. This kind of failure tends to foster cynicism, which gets in the way of future team efforts.
    In addition to finding the right size, teams must develop the right mix of skills; that is, each of the complementary skills necessary to do the team’s job. As obvious as it sounds, it is a common failing in potential teams. Skill requirements fall into three fairly self-evident categories.

    Technical or Functional Expertise.


    It would make little sense for a group of doctors to litigate an employment discrimination case in a court of law. Yet teams of doctors and lawyers often try medical malpractice or personal injury cases. Similarly, product development groups that include only marketers or engineers are less likely to succeed than those with the complementary skills of both.

    Problem-Solving and Decision-Making Skills.


    Teams must be able to identify the problems and opportunities they face, evaluate the options they have for moving forward, and then make necessary trade-offs and decisions about how to proceed. Most teams need some members with these skills to begin with, although many will develop them best on the job.

    Interpersonal Skills.


    Common understanding and purpose cannot arise without effective communication and constructive conflict, which in turn depend on interpersonal skills. These skills include risk taking, helpful criticism, objectivity, active listening, giving the benefit of the doubt, and recogniz-ing the interests and achievements of others.
    Obviously, a team cannot get started without some minimum complement of skills, especially technical and functional ones. Still, think about how often you’ve been part of a team whose members were chosen primarily on the basis of personal compatibility or formal position in the organization, and in which the skill mix of its members wasn’t given much thought.
    It is equally common to overemphasize skills in team selection. Yet in all the successful teams we’ve encountered, not one had all the needed skills at the outset. The Burlington Northern team, for example, initially had no members who were skilled marketers despite the fact that their performance challenge was a marketing one. In fact, we discovered that teams are powerful vehicles for developing the skills needed to meet the team’s performance challenge. Accordingly, team member selection ought to ride as much on skill potential as on skills already proven.
    Effective teams develop strong commitment to a common approach; that is, to how they will work together to accomplish their purpose. Team members must agree on who will do particular jobs, how schedules will be set and adhered to, what skills need to be developed, how continuing membership in the team is to be earned, and how the group will make and modify decisions. This element of commitment is as important to team performance as the team’s commitment to its purpose and goals.
    Agreeing on the specifics of work and how they fit together to integrate individual skills and advance team performance lies at the heart of shaping a common approach. It is perhaps self-evident that an approach that delegates all the real work to a few members (or staff outsiders) and thus relies on reviews and meetings for its only “work together” aspects, cannot sustain a real team. Every member of a successful team does equivalent amounts of real work; all members, including the team leader, contribute in concrete ways to the team’s work product. This is a very important element of the emotional logic that drives team performance.
    When individuals approach a team situation, especially in a business setting, each has preexisting job assignments as well as strengths and weaknesses reflecting a variety of talents, backgrounds, personalities, and prejudices. Only through the mutual discovery and understanding of how to apply all its human resources to a common purpose can a team develop and agree on the best approach to achieve its goals. 
    At the heart of such long and, at times, difficult interactions lies a commitment-building process in which the team candidly explores who is best suited to each task as well as how individual roles will come together. In effect, the team establishes a social contract among members that relates to their purpose and guides and obligates how they must work together.
    No group ever becomes a team until it can hold itself accountable as a team. Like common purpose and approach, mutual accountability is a stiff test. Think, for example, about the subtle but critical difference between “the boss holds me accountable” and “we hold ourselves accountable.” The first case can lead to the second, but without the second, there can be no team.
    Companies like Hewlett-Packard and Motorola have an ingrained performance ethic that enables teams to form organically whenever there is a clear performance challenge requiring collective rather than individual effort. In these companies, the factor of mutual accountability is commonplace. “Being in the boat together” is how their performance game is played.
    At its core, team accountability is about the sincere promises we make to ourselves and others, promises that underpin two critical aspects of effective teams: commitment and trust. Most of us enter a potential team situation cautiously because ingrained individualism and experience discourage us from putting our fates in the hands of others or accepting responsibility for others. Teams do not succeed by ignoring or wishing away such behavior.
    Mutual accountability cannot be coerced any more than people can be made to trust one another. But when a team shares a common purpose, goals, and approach, mutual accountability grows as a natural counterpart. Accountability arises from and reinforces the time, energy, and action invested in figuring out what the team is trying to accomplish and how best to get it done.
    When people work together toward a common objective, trust and commitment follow. Consequently, teams enjoying a strong common purpose and approach inevitably hold themselves responsible, both as individuals and as a team, for the team’s performance. This sense of mutual accountability also produces the rich rewards of mutual achievement in which all members share. What we heard over and over from members of effective teams is that they found the experience energizing and motivating in ways that their “normal” jobs never could match.
    On the other hand, groups established primarily for the sake of becoming a team or for job enhancement, communication, organizational effectiveness, or excellence rarely become effective teams, as demonstrated by the bad feelings left in many companies after experimenting with quality circles that never translated “quality” into specific goals. 
    Only when appropriate performance goals are set does the process of discussing the goals and the approaches to them give team members a clearer and clearer choice: They can disagree with a goal and the path that the team selects and, in effect, opt out, or they can pitch in and become accountable with and to their teammates.
    The discipline of teams we’ve outlined is critical to the success of all teams. Yet it is also useful to go one step further. Most teams can be classified in one of three ways: teams that recommend things, teams that make or do things, and teams that run things. In our experience, each type faces a characteristic set of challenges.

    Teams That Recommend Things.


    These teams include task forces; proj-ect groups; and audit, quality, or safety groups asked to study and solve particular problems. Teams that recommend things almost always have predetermined completion dates. Two critical issues are unique to such teams: getting off to a fast and constructive start and dealing with the ultimate handoff that’s required to get recommendations implemented.
    The key to the first issue lies in the clarity of the team’s charter and the composition of its membership. In addition to wanting to know why and how their efforts are important, task forces need a clear definition of whom management expects to participate and the time commitment required. 
    Management can help by ensuring that the team includes people with the skills and influence necessary for crafting practical recommendations that will carry weight throughout the organization. Moreover, management can help the team get the necessary cooperation by opening doors and dealing with political obstacles.
    Missing the handoff is almost always the problem that stymies teams that recommend things. To avoid this, the transfer of responsibility for recommendations to those who must implement them demands top management’s time and attention. The more top managers assume that recommendations will “just happen,” the less likely it is that they will. The more involvement task force members have in implementing their recommendations, the more likely they are to get implemented.
    To the extent that people outside the task force will have to carry the ball, it is critical to involve them in the process early and often, certainly well before recommendations are finalized. Such involvement may take many forms, including participating in interviews, helping with analyses, contributing and critiquing ideas, and conducting experiments and trials. At a minimum, anyone responsible for implementation should receive a briefing on the task force’s purpose, approach, and objectives at the beginning of the effort as well as regular reviews of progress.

    Teams That Make or Do Things.


    These teams include people at or near the front lines who are responsible for doing the basic manufacturing, development, operations, marketing, sales, service, and other value-adding activities of a business. With some exceptions, such as new-product development or process design teams, teams that make or do things tend to have no set completion dates because their activities are ongoing.
    In deciding where team performance might have the greatest impact, top management should concentrate on what we call the company’s “critical delivery points”—that is, places in the organization where the cost and value of the company’s products and services are most directly determined. Such critical delivery points might include where accounts get managed, customer service performed, products designed, and productivity determined. If performance at critical delivery points depends on combining multiple skills, perspectives, and judgments in real time, then the team option is the smartest one.
    When an organization does require a significant number of teams at these points, the sheer challenge of maximizing the performance of so many groups will demand a carefully constructed and performance-focused set of management processes. The issue here for top management is how to build the necessary systems and process supports without falling into the trap of appearing to promote teams for their own sake.
    The imperative here, returning to our earlier discussion of the basic discipline of teams, is a relentless focus on performance. If management fails to pay persistent attention to the link between teams and performance, the organization becomes convinced that “this year, we are doing ‘teams’.”
     Top management can help by instituting processes like pay schemes and training for teams responsive to their real time needs, but more than anything else, top management must make clear and compelling demands on the teams themselves and then pay constant attention to their progress with respect to both team basics and performance results. This means focusing on specific teams and specific performance challenges. Otherwise “performance,” like “team,” will become a cliché.

    Teams That Run Things.


    Despite the fact that many leaders refer to the group reporting to them as a team, few groups really are. And groups that become real teams seldom think of themselves as a team because they are so focused on performance results. Yet the opportunity for such teams includes groups from the top of the enterprise down through the divisional or functional level. Whether it is in charge of thousands of people or just a handful, as long as the group oversees some business, ongoing program, or significant functional activity, it is a team that runs things.
    The main issue these teams face is determining whether a real team approach is the right one. Many groups that run things can be more effective as working groups than as teams. The key judgment is whether the sum of individual bests will suffice for the performance challenge at hand or whether the group must deliver substantial incremental performance requiring real joint work products. Although the team option promises greater performance, it also brings more risk, and managers must be brutally honest in assessing the trade-offs.
    Members may have to overcome a natural reluctance to trust their fate to others. The price of faking the team approach is high: At best, members get diverted from their individual goals, costs outweigh benefits, and people resent the imposition on their time and priorities. At worst, serious animosities develop that undercut even the potential personal bests of the working-group approach.
    Working groups present fewer risks. Effective working groups need little time to shape their purpose, since the leader usually establishes it. Meetings are run against well-prioritized agendas. And decisions are implemented through specific individual assignments and accountabilities. 
    Most of the time, therefore, if performance aspirations can be met through individuals doing their respective jobs well, the working-group approach is more comfortable, less risky, and less disruptive than trying for more elusive team performance levels. Indeed, if there is no performance need for the team approach, efforts spent to improve the effectiveness of the working group make much more sense than floundering around trying to become a team.
    Having said that, we believe the extra level of performance teams can achieve is becoming critical for a growing number of companies, especially as they move through major changes during which company performance depends on broad-based behavioral change. When top management uses teams to run things, it should make sure the team succeeds in identifying specific purposes and goals.
    This is a second major issue for teams that run things. Too often, such teams confuse the broad mission of the total organization with the specific purpose of their small group at the top. The discipline of teams tells us that for a real team to form, there must be a team purpose that is distinctive and specific to the small group and that requires its members to roll up their sleeves and accomplish something beyond individual end products.
     If a group of managers looks only at the economic performance of the part of the organization it runs to assess overall effectiveness, the group will not have any team performance goals of its own.
    While the basic discipline of teams does not differ for them, teams at the top are certainly the most difficult. The complexities of long-term challenges, heavy demands on executive time, and the deep-seated individualism of senior people conspire against teams at the top. At the same time, teams at the top are the most powerful. At first we thought such teams were nearly impossible. 
    That is because we were looking at the teams as defined by the formal organizational structure; that is, the leader and all his or her direct reports equals the team. Then we discovered that real teams at the top were often smaller and less formalized: Whitehead and Weinberg at Goldman Sachs; Hewlett and Packard at HP; Krasnoff, Pall, and Hardy at Pall Corporation; Kendall, Pearson, and Calloway at Pepsi; Haas and Haas at Levi Strauss; Batten and Ridder at Knight Ridder. They were mostly twos and threes, with an occasional fourth.
    Nonetheless, real teams at the top of large, complex organizations are still few and far between. Far too many groups at the top of large corporations needlessly constrain themselves from achieving real team levels of performance because they assume that all direct reports must be on the team, that team goals must be identical to corporate goals, that the team members’ positions rather than skills determine their respective roles, that a team must be a team all the time, and that the team leader is above doing real work.
    As understandable as these assumptions may be, most of them are unwarranted. They do not apply to the teams at the top we have observed, and when replaced with more realistic and flexible assumptions that permit the team discipline to be applied, real team performance at the top can and does occur. Moreover, as more and more companies are confronted with the need to manage major change across their organizations, we will see more real teams at the top.
    Every company faces specific performance challenges for which teams are the most practical and powerful vehicle at top management’s disposal.
    We believe that teams will become the primary unit of performance in high-performance organizations. But that does not mean that teams will crowd out individual opportunity or formal hierarchy and process. Rather, teams will enhance existing structures without replacing them. A team opportunity exists anywhere hierarchy or organizational boundaries inhibit the skills and perspectives needed for optimal results.
     Thus, new-product innovation requires preserving functional excellence through structure while eradicating functional bias through teams. And frontline productivity requires preserving direction and guidance through hierarchy while drawing on energy and flexibility through self-managing teams.
    A team opportunity exists anywhere hierarchy or organizational boundaries inhibit the skills and perspectives needed for optimal results.

                    We are convinced that every company faces specific performance challenges for which teams are the most practical and powerful vehicle at top management’s disposal. The critical role for senior managers, therefore, is to worry about company performance and the kinds of teams that can deliver it. This means top management must recognize a team’s unique potential to deliver results, deploy teams strategically when they are the best tool for the job, and foster the basic discipline of teams that will make them effective. By doing so, top management creates the kind of environment that enables team as well as individual and organizational performance.


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    How Big Data from Space helps  Life on Earth


    As an oceanographer and former NASA astronaut, I am particularly well placed to appreciate the perspectives space can give us on life on earth. My first glimpse of our blue planet stole my breath and has never let it go.
    I have been working to deepen our understanding of and appreciation for this planet since. Key to that understanding are the observational data – much of it from satellites – that feed our knowledge of this planet. Among other things, observations from satellites help us to understand our changing climate, predict hazardous weather and provide early warning of potential crop failures or freshwater shortages.
    The big data revolution could lead to currently unimagined uses for the data we receive from satellites. Entrepreneurs could come up with new applications and ideas for mashing up data. But the data itself should, I believe, be regarded as a public good. How to guarantee this, in a world where public budgets are squeezed and space exploration is becoming increasingly affordable for private players, is a question that deserves serious thought and active engagement.
    From fish in Peru to drought in Australia
    It is worth reflecting on the sobering fact that we are the first generation of humans that could even have this conversation. Just over four decades ago, nobody would even have thought to connect variations in the catch of Peruvian fisheries, say, with unseasonably dry spells in central Australia. It was only with the availability of snapshots from satellites in the 1970s that we could identify and begin to understand the phenomenon that linked them: El Nino.
    Since then our uses of data from space have become increasingly sophisticated. It is bordering on miraculous, for example, that we can have a reasonable degree of confidence in long-range weather forecasts. Weather patterns are so complex, chaos ought to overwhelm predictability once we look just a day or two ahead. But by analyzing patterns from thousands of different kinds of daily observations over the years, we have become better able to tease out the likeliest patterns.
    No single satellite can make all the observations necessary to compile a reliable weather forecast. Indeed, no single country’s satellites can do so. There has developed, therefore, a convention of data sharing among government-run space programmes to enable each country’s meteorological offices to access all the information they need to predict the weather.
    Data as a public good
    This is what I mean by regarding data as a public good. The ability to forecast hurricanes, typhoons, droughts and heatwaves is clearly of benefit to humanity as a whole, and the data on which it relies is deservedly regarded as part of the global commons.
    I believe we should take the same approach to all kinds of “environmental intelligence” represented by satellite data, in combination with sensors on the ground, whenever it has implications that transcend national borders – where population’s lives and livelihoods are at stake. By analyzing the reflections of microwaves beamed at forests, for example, we can tell when their ecosystems are under stress; measurements of ocean temperatures help us to predict where fish will be; observations from space can warn about problems with soil conditions that could help the world to prepare for poor harvests.
    As technology advances, so does the capacity to generate actionable intelligence. In recent years, for instance, satellites have allowed us to map differences in gravity on the Earth’s surface so precisely that we can calculate how much groundwater is stored in aquifers – something never before possible. Given the potential of freshwater shortages to impact everything from food security to energy supplies and geopolitical tensions, it is clearly beneficial for this knowledge to be in the public domain.
    Katchy Sullivan
    “The price could be paid in human lives”
    The question of how to ensure space-based knowledge is used for the common good has become pressing with the dawning of a new space age, in which satellites have become affordable for private interests. At the same time, public finances in countries which have traditionally funded major space programmes have come under stress. Increasingly, there is pressure on governments to buy in data from private providers rather than fund satellite programmes themselves.
    At first glance, this makes sense. But some changes in the private sector’s role in space raise troubling hypotheticals. Imagine that a commodity trader, for example, monopolized data that enabled harvests to be predicted. A killing could be made on the futures markets – but the price could be paid in human lives, if exclusion from that data hindered public agencies from preparing for famine.
    As private satellites proliferate and the big data revolution advances, we need to debate public and private roles in space. One model to consider is the Monsanto-owned Climate Corporation. It takes publicly available data and adds value by analyzing it in ways that generate guidance individuals will pay for: when a farmer should irrigate a field, for example.  The underlying public data remain freely available – even viewable on a the free level of the company’s web service – and so continue to serve the general public via advanced warning of severe drought or accurate forecasts of seasonal flooding.
    In the coming decades, new technologies and business models will radically expand the data available from satellites and the uses to which it can be put. Our challenge is to ensure that observations about our planet benefit everyone who lives on it.

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    Where the Digital Economy Is Moving the Fastest



    The transition to a global digital economy in 2014 was sporadic – brisk in some countries, choppy in others. By year’s end, the seven biggest emerging markets were larger than the G7, in purchasing power parity terms. Plus, consumers in the Asia-Pacific regionwere expected to spend more online last year than consumers in North America. The opportunities to serve the e-consumer were growing – if you knew where to look.

    These changing rhythms in digital commerce are more than a China, or even an Asia, story. Far from Silicon Valley, Shanghai, or Singapore, a German company, Rocket Internet, has been busy launching e-commerce start-ups across a wide range of emerging and frontier markets. Their stated mission: To become the world’s largest internet platform outside the U.S. and China. Many such “Rocket” companies are poised to become the Alibabas and Amazons for the rest of the world: Jumia, which operates in nine countries across Africa; Namshi in the Middle East; Lazada and Zalora in ASEAN; Jabong in India; and Kaymu in 33 markets across Africa, Asia, Europe, and the Middle East.

    Private equity and venture capital money have been concentrating in certain markets in ways that mimic the electronic gold rush in Silicon Valley. During the summer of 2014 alone $3 billion poured into India’s e-commerce sector, where, in addition to local innovators like Flipkart and Snapdeal, there are nearly 200 digital commerce startups flush with private investment and venture capital funds. This is happening in a country where online vendors largely operate on a cash-on-delivery (COD) basis. Credit cards or PayPal are rarely used; according to the Reserve Bank of India, 90% of all monetary transactions in India are in cash. Even Amazon localized its approach in India to offer COD as a service. India and other middle-income countries such as Indonesia and Colombia all have high cash dependence. But even where cash is still king, digital marketplaces are innovating at a remarkable pace. Nimble e-commerce players are simply working with and around the persistence of cash.

    To understand more about these types of changes around the world, we developed an “index” to identify how a group of countries stack up against each other in terms of readiness for a digital economy. Our Digital Evolution Index (DEI), created by the Fletcher School at Tufts University (with support from Mastercard and DataCash), is derived from four broad drivers: 

    supply-side factors (including access, fulfillment, and transactions infrastructure); 

    demand-side factors (including consumer behaviors and trends, financial and Internet and social media savviness); 

    innovations (including the entrepreneurial, technological and funding ecosystems, presence and extent of disruptive forces and the presence of a start-up culture and mindset); 

    and institutions (including government effectiveness and its role in business, laws and regulations and promoting the digital ecosystem). The resulting index includes a ranking of 50 countries, which were chosen because they are either home to most of the current 3 billion internet users or they are where the next billion users are likely to come from.

    As part of our research, we wanted to understand who was changing quickly to prepare for the digital marketplace and who wasn’t. Perhaps not surprisingly, developing countries in Asia and Latin America are leading in momentum, reflecting their overall economic gains. But our analysis revealed other interesting patterns. Take, for example, Singapore and The Netherlands. Both are among the top 10 countries in present levels of digital evolution. But when we consider the momentum – i.e., the five-year rate of change from 2008 to 2013 – the two countries are far apart. Singapore has been steadily advancing in developing a world-class digital infrastructure, through public-private partnerships, to further entrench its status as a regional communications hub. 

    Through ongoing investment, it remains an attractive destination for start-ups and for private equity and venture capital. The Netherlands, meanwhile, has been rapidly losing steam. The Dutch government’s austerity measures beginning in late 2010 reduced investment into elements of the digital ecosystem. Its stagnant, and at times slipping, consumer demand led investors to seek greener pastures.

    Based on the performance of countries on the index during the years 2008 to 2013, we assigned them to one of four trajectory zones: Stand Out, Stall Out, Break Out, and Watch Out.

    • Stand Out countries have shown high levels of digital development in the past and continue to remain on an upward trajectory.
    • Stall Out countries have achieved a high level of evolution in the past but are losing momentum and risk falling behind.
    • Break Out countries have the potential to develop strong digital economies. Though their overall score is still low, they are moving upward and are poised to become Stand Out countries in the future.
    • Watch Out countries face significant opportunities and challenges, with low scores on both current level and upward motion of their DEI. Some may be able to overcome limitations with clever innovations and stopgap measures, while others seem to be stuck.
    W150210_CHAKRAVORTI_COUNTRIESBUILDINGDIGITAL
    Break Out countries such as India, China, Brazil, Vietnam, and the Philippines are improving their digital readiness quite rapidly. But the next phase of growth is harder to achieve. Staying on this trajectory means confronting challenges like improving supply infrastructure and nurturing sophisticated domestic consumers.

    Watch Out countries like Indonesia, Russia, Nigeria, Egypt, and Kenya have important things in common like institutional uncertainty and a low commitment to reform. They possess one or two outstanding qualities — predominantly demographics — that make them attractive to businesses and investors, but they expend a lot of energy innovating around institutional and infrastructural constraints. Unclogging these bottlenecks would let these countries direct their innovation resources to more productive uses.

    Most Western and Northern European countries, Australia, and Japan have been Stalling Out. The only way they can jump-start their recovery is to follow what Stand Out countries do best: redouble on innovation and continue to seek markets beyond domestic borders. Stall Out countries are also aging. Attracting talented, young immigrants can help revive innovation quickly.

    What does the future hold? The next billion consumers to come online will be making their digital decisions on a mobile device – very different from the practices of the first billion that helped build many of the foundations of the current e-commerce industry. There will continue to be strong cross-border influences as the competitive field evolves: even if Europe slows, a European company, such as Rocket Internet, can grow by targeting the fast-growing markets in the emerging world; giants out of the emerging world, such as Alibaba, with their newfound resources and brand, will look for markets elsewhere; old stalwarts, such as Amazon and Google will seek growth in new markets and new product areas.

     Emerging economies will continue to evolve differently, as will their newly online consumers. Businesses will have to innovate by customizing their approaches to this multi-speed planet, and in working around institutional and infrastructural constraints, particularly in markets that are home to the next billion online consumers.

    We may be on a journey toward a digital planet — but we’re all traveling at different speeds.


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    Data Scientist: The Sexiest Job of the 21st Century



    When Jonathan Goldman arrived for work in June 2006 at LinkedIn, the business networking site, the place still felt like a start-up. The company had just under 8 million accounts, and the number was growing quickly as existing members invited their friends and colleagues to join. But users weren’t seeking out connections with the people who were already on the site at the rate executives had expected. Something was apparently missing in the social experience. As one LinkedIn manager put it, “It was like arriving at a conference reception and realizing you don’t know anyone. So you just stand in the corner sipping your drink—and you probably leave early.”
    Goldman, a PhD in physics from Stanford, was intrigued by the linking he did see going on and by the richness of the user profiles. It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users. But LinkedIn’s engineering team, caught up in the challenges of scaling up the site, seemed uninterested. Some colleagues were openly dismissive of Goldman’s ideas. Why would users need LinkedIn to figure out their networks for them? The site already had an address book importer that could pull in all a member’s connections.
    Luckily, Reid Hoffman, LinkedIn’s cofounder and CEO at the time (now its executive chairman), had faith in the power of analytics because of his experiences at PayPal, and he had granted Goldman a high degree of autonomy. For one thing, he had given Goldman a way to circumvent the traditional product release cycle by publishing small modules in the form of ads on the site’s most popular pages.
    Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know—for example, people who had shared their tenures at schools and workplaces. He did this by ginning up a custom ad that displayed the three best new matches for each user based on the background entered in his or her LinkedIn profile. Within days it was obvious that something remarkable was taking place. The click-through rate on those ads was the highest ever seen. Goldman continued to refine how the suggestions were generated, incorporating networking ideas such as “triangle closing”—the notion that if you know Larry and Sue, there’s a good chance that Larry and Sue know each other. Goldman and his team also got the action required to respond to a suggestion down to one click.
    The shortage of data scientists is becoming a serious constraint in some sectors.
    It didn’t take long for LinkedIn’s top managers to recognize a good idea and make it a standard feature. That’s when things really took off. “People You May Know” ads achieved a click-through rate 30% higher than the rate obtained by other prompts to visit more pages on the site. They generated millions of new page views. Thanks to this one feature, LinkedIn’s growth trajectory shifted significantly upward.

    A New Breed

    Goldman is a good example of a new key player in organizations: the “data scientist.” It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data. The title has been around for only a few years. (It was coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook.) But thousands of data scientists are already working at both start-ups and well-established companies. Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before. If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.
    Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization. While those are important breakthroughs, at least as important are the people with the skill set (and the mind-set) to put them to good use. On this front, demand has raced ahead of supply. Indeed, the shortage of data scientists is becoming a serious constraint in some sectors. Greylock Partners, an early-stage venture firm that has backed companies such as Facebook, LinkedIn, Palo Alto Networks, and Workday, is worried enough about the tight labor pool that it has built its own specialized recruiting team to channel talent to businesses in its portfolio. “Once they have data,” says Dan Portillo, who leads that team, “they really need people who can manage it and find insights in it.”

    Who Are These People?

    If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive. None of those tasks is as straightforward as it is with other, established organizational roles. Start with the fact that there are no university programs offering degrees in data science. There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.
    The first step in filling the need for data scientists, therefore, is to understand what they do in businesses. Then ask, What skills do they need? And what fields are those skills most readily found in?
    More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.
    Data scientists realize that they face technical limitations, but they don’t allow that to bog down their search for novel solutions. As they make discoveries, they communicate what they’ve learned and suggest its implications for new business directions. Often they are creative in displaying information visually and making the patterns they find clear and compelling. They advise executives and product managers on the implications of the data for products, processes, and decisions.
    Given the nascent state of their trade, it often falls to data scientists to fashion their own tools and even conduct academic-style research. Yahoo, one of the firms that employed a group of data scientists early on, was instrumental in developing Hadoop. Facebook’s data team created the language Hive for programming Hadoop projects. Many other data scientists, especially at data-driven companies such as Google, Amazon, Microsoft, Walmart, eBay, LinkedIn, and Twitter, have added to and refined the tool kit.
    What kind of person does all this? What abilities make a data scientist successful? Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.

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    Leveraging Social Networks to Drive Collaboration and Improve Execution.



























    Tuck Executive Education partners with companies to address unique challenges they face. As part of a learning initiative with a custom client, Tuck Professors Pino Audia and Adam Kleinbaum designed, administered, and analyzed a network survey completed by over 1,000 directors and managers at the company. The respondents identified the managers, directors, and vice presidents they most regularly interfaced with in carrying out their role at the organization to generate survey data that showed how they saw their own network and how others perceived them. Summary data was used in faculty-led sessions to help participants understand the informal roles people play in organizations and to think what their results say about the roles they play. 

    Representative examples they discussed include: Participating executives explored the power of building relationships across divisions, functions, and levels and the benefits of different types of networks. For example, Sparse networks are useful for efficiently gathering and disseminating information Dense networks are useful for effectively coordinating work in a cohesive group   This is powerful learning for leaders of an organization that is committed to innovation. For example, someone with a large and sparse network is more likely to see innovation opportunities across the organization and promote the possibilities. 

    These “superconnectors.” have networks that are Large, in the sense that many other people cite them as contacts Sparse, in the sense that they are connected to people in disparate parts of the organization, who are not otherwise linked to each other Integrative, in the sense that they bring together contacts across divisional boundaries   When it comes to execution, someone with a dense network may be more likely to have the kind of deep relationships needed to bring together people and resources needed to implement the idea. In addition to sharing individual reports with program participants, network survey data can be used by human resources professionals to understand and improve collaboration across the organization, foster a “one-company” mindset, and leverage the strengths of different divisions and departments to drive innovation. Are functions or departments that are expected to work together well-connected through the networks of their members? Are individual high performers under- or overestimating their networks? 

    A social network survey can help the organization: Map which parts of the organization are isolated Identify where breaking down silos may improve collaboration Identify high-potentials who are powerful but not part of the formal power structure Identify superconnectors Promote and move around those who are good at networking to build a stronger organization Map where women and minorities are isolated in order to strengthen diversity and inclusion Identify mentors for key hires from outside the organization   Social network analysis can help break down silos by measuring individual networks and how they link disparate units.

    Aggregate data can provide division heads and functional heads with a way to assess alignment of individual networks to the needs of their unit and guide corrective actions; this is especially important for functions or divisions that are expected to work together to maximize effectiveness. If you are interested in working with us on a social network analysis initiative, some key steps include the following: Conduct an organizational assessment to get alignment on desired outcomes, participants to be included in the analysis, and an understanding of interdependence among units. Collect and analyze network data, using either survey data or electronic communication data, such as e-mail or instant messaging Produce reports at both the individual and organizational level Design and deliver leadership development sessions focused on the analysis Help create follow-up interventions.

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    Why We Pay to Save Time





    Feeling torn between conflicting goals makes people less inclined to fulfill either one.

    Many people consider holiday cheer synonymous with seasonal stress as they rush to finish up their shopping and decorating. But what exactly makes people feel so pressed for time? How do time constraints affect our behavior and the choices we make — not only during the holidays but year-round? And how much are people willing to pay to minimize the pressures?


    A new study co-authored by Stanford GSB professor Jennifer Aaker offers some intriguing answers to these questions. When people perceive goals to be in conflict — baking cookies, for instance, means putting off addressing the holiday cards — the ensuing anxiety makes them feel short on time, which affects not only how they spend that time but also how much they are willing to pay to save it.
     Furthermore, Aaker’s research — conducted in collaboration with Jordan Etkin of Duke’s Fuqua School of Business and Ioannis Evangelidis of Erasmus University’s Rotterdam School of Management — shows that people can reduce the stress of juggling competing goals simply by breathing slowly and learning to recast their anxiety as something more positive, like excitement.

    Being stretched to the limit increasingly seems like an inevitable condition of the modern age. | Illustration by Tricia Seibold



    Building on prior research demonstrating a close correlation between stress and time constraints, the researchers devised a series of experiments to demonstrate that consumers who see their goals as competing experience greater anxiety — and thus, more time pressures — than those who don’t. They also suspected that the stress of competing goals would make consumers not only less willing to wait — whether in a checkout line, for delivery of an online order, or to speak to a customer-service representative — but also more inclined to pay more to save time, as with expedited shipping.
    As they predicted, participants who noted a higher degree of conflict between their goals felt like they had less time. This held true regardless of the nature of the conflict; those who felt conflicted about money — should you save or buy nice things? — felt just as pressed for time as those weighing goals that directly competed for their time, such as staying late at work to build a successful career or coming home early to be a good parent.
    In another experiment, the researchers asked participants to choose between two goals they deemed important to them. Then, participants were told to pick one of four cars sporting variations in price, occupant survival rate, styling, and environmental friendliness. Drawing on prior research that identified safety and pollution as the two biggest consumer concerns, Aaker and her colleagues developed various scenarios to measure different levels of conflict. In the high-conflict condition, for instance, the car with the worst survival rate was the most eco-friendly while the safest car spewed the most pollutants, creating stress by forcing participants to make a trade-off. The low-conflict condition included one car that was clearly superior in both categories.
    As in the prior experiment, participants measured both the degree of conflict they felt and the amount of time they thought they had — and again, the results showed that those in the high-conflict group reported more stress and felt more time - constrained. But here the researchers added a new twist: They told the participants that their chosen car was not ready and asked how long they’d be willing to wait. Those in the high-conflict group who had to choose between competing preferences were willing to wait fewer days than those who had made no concessions on their dream car. In a similar scenario, goal-conflicted subjects who felt short on time were willing to pay 30 percent more for expedited shipping of a DVD from Amazon. Such results confirm the hypothesis that feeling pressed for time shortens patience and increases willingness to pay.
    To test whether reducing stress and anxiety would expand the perception of available time, Aaker and her colleagues created two simple interventions. As in the first experiment, they asked one group of subjects to list two goals and another to list two goals that they perceived to be “in conflict with one another.” Then they randomly assigned participants one of two sets of instructions: Half were told “to breathe so that each complete breath (inhale plus exhale) lasts 11 counts”; the others were told simply to count to 11.
    Those with conflicting goals who practiced slow breathing reported less anxiety and a more expansive view of time than those who simply counted. Likewise, conflicted subjects who were instructed to reappraise their anxiety as excitement (mainly by saying “I am excited!” repeatedly) regained a sense of control over their time. “Both interventions made participants feel they had as much time as when goal conflict seemed low,” the authors wrote.
    Our new study paints a parsimonious picture by demonstrating that conflicting goals can directly reduce subjective perceptions of time.
    Jennifer Aaker
    Being stretched to the limit increasingly seems like an inevitable condition of the modern age. Aaker and her colleagues help us understand why. “While previous research shows that stress influences time perceptions and that goal conflict can cause stress, our new study paints a parsimonious picture by demonstrating that conflicting goals can directly reduce subjective perceptions of time,” says Aaker. “That in turn impacts behavior.” Feeling torn between conflicting goals makes people less inclined to fulfill either one, limiting their patience and compelling them to pay to buy back some time.



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    Google launches 'smart' spoon to help steady shaking hands

    Hi-tech invention aims to help sufferers from essential tremors and Parkinson’s disease and can reduce shaking by 76%


















    Anupam Pathak, a senior hardware engineer at Google, shows off the prototype of the Liftware spoon he developed that helps people eat without spilling in Mountain View, California. Photograph: Eric Risberg/AP

    Drones, self-driving cars, robots, balloons providing internet access – Google is stretching a long way from search. Now the company has added a “smart” spoon to its portfolio of hi-tech products.
    Google has started promoting its Liftware spoon, a utensil that uses hundreds of algorithms to sense how a hand is shaking and makes instant adjustments to stay balanced.
    The product is aimed at people with essential tremors and Parkinson’s disease and, according to the company, can reduce shaking of the spoon bowl by an average of 76%.
    Essential tremors and Parkinson’s disease affect more than 10 million people worldwide, including Google co-founder Sergey Brin’s mother. Brin has also said he has a genetic mutation associated with higher rates of Parkinson’s. He has donated more than $50m to research for a cure.




    Google acquired Lift Lab, the spoon’s maker, earlier this year, and the Lift Lab founder, Anupam Pathak, now works for Google X’s life sciences division, which has made a number of purchases in recent years as the company has shown more interest in the medical field.
    The division also owns a stake in DNAnexus, a software company analysing genome sequencing to better understand the genetic factors of heart disease and ageing. It is also working on how nanoparticles in blood might help detect diseases and a smart contact lens that would measure glucose levels in tears to help diabetics track their blood sugar levels.
    The spoons are now available for $295.


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    MIT Invents A Social Network You Can Wear




    social network you can wear, alerting you when a friend is nearby, and lighting up around the like-minded to attract their attention.
    Fashion has always been a good way to break the ice. Spot someone at a party wearing something you like; go up and compliment them on it. Easy. But a new project called Social Textiles wants to turn fashion into a social network you can wear, alerting you when a friend is nearby, and lighting up around the like-minded to attract their attention.


    This is the latest joint from MIT's Tangible Media Group and Fluid Interface Group, a design team that between them has given us everything from reinvented power cords to shape shifting displays. The Social Textiles team started with a group of MIT students—Viirj Kan, Katsuya Fujii, Judith Amores, and Chang Long Zhu Jin—who wanted to try to solve a simple problem: how can tech make social media more tangible?



    "If you think about it, our Facebook and Twitter profiles reach and even impact thousands of people every day, but it doesn't feel like it," Kan, representing the group, tells me. "But while the way we represent ourselves in social media is intangible, what we wear isn't. We wanted to see if we could merge the two to create social catalysts."
    Right now, Social Textiles is a T-shirt, although theoretically, it could be any type of clothing. On the front of the shirt is a pattern printed in thermochromatic ink, with a thin circuit membrane underneath. Pairing to your smart phone thru Bluetooth, the Social Textile shirt detects when other people are in the room who share your interests, and sends a buzz through the shirt's collar when you're within 12 feet of each other.
    But it goes one step further than that. If you actually touch the person you're simpatico with, by clapping them on the shoulder or shaking their hand, a capacitive sensor in the T-shirt can tell. Then, the Social Textiles shirt lights up, revealing symbols on the front of the shirt showing what you and your new friend have in common.






    "Depending on how the ink pattern is designed, Social Textiles can communicate anything you want," Kan explains. "It could tell two people who have just met that they both like jazz, or that they both go to MIT." Going further, it could tell two people who just met if they were a match on OKCupid, were compatible organ donors, or more. The technology that drives the T-shirt is cheap and affordable; what Social Textiles could communicate is only limited by the designer working with it.
    For many, fashion is already something of a way of communicating to others that you're part of a secret club. Social Textiles could take that concept to the next level, buzzing and flashing on the club floor when a like-minded club kid bumps into you. But the Social Textiles team also sees their invention being useful at more structured events like freshman meet-and-greets, company picnics, industry conferences, and so on. After all, meeting new people is hard enough. Why not let your clothes do some of the work for you?


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    Dell and IIT Madras Join Hands to Drive Research in Next-Generation Infrastructure and Cloud Technologies






















    Dell, the world’s fastest-growing large integrated IT company, today announced its collaboration with IIT Madras (IIT-M) for joint research and development programme in the area of Next Generation Infrastructure and Cloud technologies.

    As part of the partnership, Dell and IIT Madras will undertake collaborative research projects for next-generation technology and business needs. Dell aims to leverage the capabilities at the Dell Networking Centre and the talent ecosystem available in Chennai towards strengthening research on the subject. This industry-academia partnership will address research and development of next-generation Cloud technologies that would play a key role in industry verticals such as Telecom and Healthcare.

    The partnership entails sponsoring of various research projects and funding of research scholars working in areas that coincide with Dell Research objectives. Additionally, the partnership provides internship opportunities for IIT-M students to experience industry environment and work on key technology challenges, pursued by Dell Research.

    Announcing the collaboration, Dr. Jai Menon, Vice President, Head of Research and Chief Research Officer for Dell said, “Dell’s heritage is all about listening to customers and using that insight to create innovative technology solutions that help them succeed. While innovation has always been at the core of Dell, we now want to focus on disruptive, futuristic technologies. We strongly believe that industry-academia partnerships are key to fostering advancements in technology. We are confident that our partnership with IIT-M will bring unique approaches and compelling innovations for the future.”

    Sreedhara Narayanaswamy, Executive Director & GM – Datacenter PD/PI Global Engineering & Chennai R&D said, “This partnership is very significant for Dell Networking center at Chennai to collaborate with IIT Madras. It allows bi-directional collaboration and research activities in which both the Chennai R&D team as well as the faculty and students of IIT-M realize long-term benefits.”

    Professor R. Nagarajan, Dean of International & Alumni Relations at IIT Madras added: “We are constantly looking for ways in which industry can partner with us to enhance our research eco-system, and make our campus the top choice for faculty, research scholars and companies. In this context, we are delighted to join hands with Dell, an innovator within the technology industry for over 30 years. Our partnership will provide young minds with a tremendous opportunity to contribute towards cutting-edge research in the field of next-generation technologies and solutions.”



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    Indian startup shows how the cloud can be used to transform education in rural India





















    Classle, a Chennai-based startup uses a potent combination of cloud, mobile and social technologies to enable students to access learning material free of cost through their basic low-cost mobile devices.


    Many a times we do things beyond a formal classroom without realizing that we are learning in the process. For example, an individual surfs and accesses material on the Internet in the form of audio, video, wiki and then goes ahead to even create and store information. 

    Understanding the immense power of peer-to-peer learning, Classle, a Chennai-based startup has developed a cloud based education system for rural India. Classle gets lakhs of people together and enables them to connect to each other in thousands of communities available on open social network of Classle. People can connect with these communities to collaborate and share exchange resources in their chosen areas of interest. There are many features to collaborate in addition to make learning fun oriented. 

    The cloud-based system enables students to access learning material free of cost, through their basic, low-cost mobile devices. The impact - more than 55 academic institutions have partnered with Classle-- almost all of them are engineering colleges predominantly in rural areas. Some of them are GLA University, Mathura; Madanapalle Institute of Technology and Madanapalleand Excel College of Engineering, Thiruchengodu. Classle’s Carry Along Cloud Campus consists of a virtual campus set up on the cloud -- each one is private to each institute -- through which students can access information and study material to further their own knowledge and interact with other members of their student communities, while teachers provide them with material and assignments. 

    These cloud campuses also monitor the interaction of the students, thus allowing companies to study them and identify talent for employment. The firm’s cloud-based system lets students access study material and assignments on an online cloud network. As it is present on the online cloud, it helps students learn even outside the physical campus and classroom. It also allows teachers to identify weaker students more easily through their submissions and interactions, and thus, provide them with extra help outside the classroom hours. 

    Cloud removes obstacles One of the biggest challenges faced in the initial set up of the company was in securing startup funding, especially given that this was a new idea and no entrepreneur in the past had proposed such a business idea for rural India. “Most people believed that it was far stretched and difficult to implement as a scalable and sustainable business,” says Vaidya Nathan, Founder and CEO, Classle Knowledge. With this background, one of the most important things for Classle Knowledge was to find a technology solution that was very cost-effective and yet highly scalable. 

    After evaluating potential technology options, the firm realized that the cloud was perfectly suited to its needs and selected a cloud platform from Amazon Web Services.  With Amazon Simple Storage Service (Amazon S3) and Reduced Redundancy Storage (RRS), the firm was able to reduce their costs by storing non-critical, reproducible data at lower levels of redundancy than Amazon S3’s standard storage.  

     “Our infrastructure has been built hundred percent on the AWS cloud platform since our inception. It was a strategic decision that we made from a long term business perspective,” says Nathan. With the AWS cloud platform, Classle saves on 30 – 35 percent of its costs, as the firm has to only pay per use. 

    “This model is vastly different from the old world of maintaining our own on-premise infrastructure whereby we had to worry about maintaining infrastructure, putting people and resources and spending time on all the undifferentiated heavy lifting that really does not contribute to the business. 

    With the cloud, there’s no need for capital expenditure at all,” adds Nathan. Cloud empowers ambitious dreams Classle is working on multimedia apps and is aiming at a target of acquiring 5 million students in 18 months. “We are growing rapidly as a social learning network where we create a ‘Closed’ learning environment for many professional and academic organizations. All these learning environments are based on Classle Cloud Campus, which runs on AWS services and plugs into the Classle Learning Bus. 

    So if we think about the scale needed for this rapid growth, we will require a sound foundation and architecture that can handle the traffic in a highly scalable manner. This is where the global AWS cloud platform comes in,” says Nathan. Secondly, in 3-4 months time Classle will be moving into ‘lifelong learning’ backed by robust academic analytics and domain learning services to acquire a learner at any point in their life and serve their learning needs at all their learning moments. This will be driven by a combination of predictive analytics and recommendation engines.

    The interconnections in social networking and the analysis of how customers interact with one another is an important development area for Classle. To do this, the firm leverages the cloud and is already doing prototypes using Amazon Elastic MapReduce (Amazon EMR) to serve this purpose in supporting its long term goal.


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    IS YOUR CEO OUT OF TOUCH OR BEING MISLED?




    A Troubling Result


    One specific result I’d like to delve into is the fact that CEOs have a much rosier picture of how data-driven their organizations are than do those down the chain. A few of the key statistics are:
    While 47 percent of CEOs believe that all employees have access to the data they need, only 27 percent of all respondents agree that they do.

    Similarly, 43 percent of CEOs think relevant data are captured and made available in real time, compared to 29 percent of all respondents.

    CEOs are also more likely to think that employees extract relevant insights from data – 38 percent of them hold this belief, as compared to 24 percent of all respondents and only 19 percent of senior vice presidents, vice presidents and directors.

    This set of findings seems to have struck a nerve. During every media interview regarding the survey, I was asked about these figures (see these pieces at Forbes and CIO.com). My initial reaction was that some CEOs may be a bit out of touch. Upon further reflection, however, I have decided that this conclusion is unfair in many situations. In fact, it may not be the CEOs that are the problem at all. I believe that in many cases the CEO is being misled.

    Is Your CEO Being Misled?


    Let me clarify right away that I am not suggesting that there is some vast conspiracy to mislead CEOs. I believe the disconnect comes about from the way that information naturally works its way up the corporate hierarchy.
    Imagine a director-level employee being provided a status report with a list of things that are going well and others going not so well across a range of initiatives. When passing the news up to the VP, the director is often going to naturally spin the good as positively as possible and downplay the bad. The director may even skip a bad point or two in the hopes that the situations can be remedied before anyone up the chain needs to worry.
    Next, the VP employs some similar cleansing and scrubbing before providing an update to whichever officer he or she reports to. That officer then applies a bit more scrubbing before talking to the CEO. The end result is that the CEO comes away with a more positive picture than is warranted even though nobody intended to be misleading.
    This phenomenon certainly isn’t unique to the processes around being a data-driven business. However, an organization can’t be truly data-driven until it is willing to look in an honest, unfiltered, unbiased way at whatever the facts are that data holds, both good and bad. In other words, in a data-driven organization, people should be comfortable providing the unvarnished truth to the CEO and the CEO should expect nothing less.

    Being Data-Driven Is All About Objective Facts


    There will certainly be instances where an individual’s pride or bonus will be harmed by the facts presented by the data and the analytics derived from that data. Part of being data-driven, however, is to prefer objective, factual assessments over subjective (often political) assessments. If my numbers are bad when reported factually and transparently, at least we all know how bad the numbers are and I know exactly how much I need to improve them. I’d prefer that to someone deciding my numbers are good or bad based on the mood they are in that day.
    As your organization continues down the path of being data-driven, consider a survey of how wide the gap is between your CEO, senior management, and the broader employee base when it comes to the use of data and analytics in the organization. If there is a gap, make it a priority for everyone to focus on closing it. After all, the CEO must provide the support needed to fix problems and build on success as the organization progresses down the road to becoming data-driven. This isn’t possible if the CEO is being misled about the current state of the organization.

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    A CONVERSATION WITH MEREDITH ROWE ABOUT 


    HOW TO LAY THE GROUNDWORK FOR YOUR CHILD’S VOCABULARY GROWTH



    To help your baby develop a large vocabulary — to give her the tools she’ll need to read, comprehend, and make sense of the world — it’s not just talk that’s important. It’s conversation.
    To be sure, parental talk of any kind is a good thing; the number of words that a child hears in infancy and toddlerhood is strongly predictive of future vocabulary growth. (Educators and policymakers have tuned in, launching initiatives that encourage parents to spend more time talking with their babies.)
    But for Associate Professor Meredith Rowe, an educational psychologist at the Harvard Graduate School of Education, the amount of words a child hears is just one factor, and not the most significant, in predicting future vocabulary growth.
    In an important 2012 paper [PDF] and in follow-up research [PDF] published just this year, Rowe found that diversity of words is more predictive of future language skills, especially as a baby grows through toddlerhood. And it’s not just using a wide assortment of words that’s important — it’s using complex words, interactive words, and words to tell stories, explain, and imagine.
    We asked Rowe to share some takeaways.
    It seems pretty well established that the quantity of parental words influences children’s rate of vocabulary growth. What was your interest in moving the conversation beyond quantity to look at quality?
    We’ve known for a while that the quantity of input matters. I think the shift to a focus on quality rather than quantity was a natural next step in the field. Many researchers, including myself, got to a point where we wanted to know more about themechanisms underlying language learning. Knowing that quantity of input matters is a start, but knowing more about which types of input are most useful at different ages is more informative about how children grow their vocabularies. And I think pitting the quantity and quality against each other is an important step in the conversation, and what I was trying to accomplish in that 2012 paper in Child Development. It is much easier to send a message about quantity, but if we know that quality trumps quantity, statistically, then perhaps we can really try and change the message to be more about having high-quality conversations with children rather than just “talking a lot.”
    Tell us about the kinds of “quality” talk you say you’re most interested in — the use of rare words and decontextualized words.
    I’m actually interested in a really wide range of what we call input quality measures, and I think one of the biggest challenges for the field is to pinpoint the specific features of input that are most beneficial for children’s language learning at different points in early childhood. Some of my work has focused on the importance of non-verbal input, specifically pointing a lot and at a lot of different things while you talk with your young children (ages 9 through 18 months).
    Then, some of the work I’ve done looking at input to toddlers and preschoolers has built off of [Professor]Catherine Snow’s previous research with the Home School Study of Language and Literacy Development to show that using rare or sophisticated vocabulary words and using talk that is abstract or beyond the here and now is very helpful at these ages.
    Can you give us an example of the “rare” and the “abstract”?
    Often these things go hand-in-hand. So a parent might have a conversation with a three-year-old about their recent trip to the children’s museum, and they might reminisce about how much fun they had putting balls through a chute and trying to line them up at the right angle so that it worked properly. This could lead to a discussion about gravity or many other related topics. [Ed: Italics signify rare words, within the context of a “beyond-the-here-and-now” conversation.]
    In that 2012 paper and in the paper that just came out in Developmental Psychology, we found that this type of decontextualized talk about the non-present predicted not only vocabulary but also children’s syntax and narrative development. And this particular type of talk with children in the toddler/preschool age range was more predictive of child language outcomes than the quantity of talk or other types of talk, and it wiped out the effect of quantity in the statistical models.
    You looked at parental input in a diverse group of 50 caregiver-child pairs, assessing data collected when the child was 18 months, 30 months, and 42 months of age. Did you find wide variance? 
    Parents varied widely in the quantity and quality of words they spoke to their children. For example, in a 90-minute interaction, the number of words that parents spoke to their children at 18 months ranged from 360 to over 9,200. Similarly, at 30 months, some parents did not produce any narrative utterances, whereas others produced over 250.
    What factors add to the variance?
    Primary caregiver education is positively related to both quantity and quality measures. On average, more highly educated parents use more words and more diverse vocabulary at each child age than less educated parents.
    But there were some areas where we did not see average social class differences in input. For example, with that beneficial narrative talk about the trip to the museum, the more educated parents did not use it more, on average, than the less educated parents.
    I think this is important. Different parents communicate with their children in different ways. Our goal is to inform parents and caregivers of the types of input that are most beneficial for young children’s language development. If parents are already communicating in these helpful ways, then it will be easier for them to continue to do so.
    As you look at the body of work you’ve done so far, what are the broad takeaways for parents and early educators?
    In a broad sense, our findings show that parents can scaffold their children’s vocabulary growth at different points in their development by providing them with exposure to different types of communication. The results suggest that beyond the quantity of their talk, parents should focus on the quality:
    • With infants, pointing and labeling a variety of objects
    • With toddlers, asking challenging questions and incorporating a diverse and sophisticated vocabulary
    • With toddlers and preschool children, having conversations about past or future events.

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    Big data redefines the traditional scientific methods used in medicine

    Healthcare professionals are applying big data and analytics to clinical challenges. This is just the beginning of a redefinition in the traditional scientific methods used in medicine. 
    Watson3.jpg
    IBM Watson
     Image: Bob Goldberg/courtesy of IBM
    Stanford University will host a big data in biomedicine conference May 20-22, 2015 for medical researchers hailing from colleges and universities, hospitals, government, and industry. The goals are to encourage collaboration, address challenges, and identify actionable steps for harnessing big data in healthcare.
    There are plenty of incentives. Whether through mega-scientific computing projects that process petabytes of data or through more informal ways of looking at data and analyzing it in new ways to reach outcomes that were previously unattainable, medicine is marching forward in applying big data and analytics to clinical challenges.
    For instance, at Lucile Packard Children's Hospital Stanford in 2011, a young girl from Reno, Nevada, was flown by helicopter to the hospital, where she was admitted to the intensive care unit. The girl had lupus, which attacks the body's healthy tissues and can cause permanent kidney damage. An interdisciplinary team of doctors had to weigh the risks of using a coagulant that could thin blood and help prevent clots against the counter risks of complicating surgery, causing a stroke or creating a bleed into an organ. The team needed data.
    A young physician named Jennifer Frankovich resorted to using a database of children with lupus that she had been helping to build. Part of the database work had entailed digitalizing charts and making data searchable with keywords. Through database searches, Dr. Frankovich was able to look at every pediatric lupus patient who had come through the hospital to see how many of them developed blood clots, and what the risk factors were. From there, she could calculate whether the risks of a blood clot in her current patient justified the risks of prescribing an anti-coagulant. The calculations indicated that the risk was worth taking, and the patient was given an anti-coagulant. The patient immediately showed signs of improvement.
    Atul Butte, an entrepreneur and associate professor of pediatrics at the Stanford School of Medicine, compared Dr. Frankovich's work to a "seismic shift" happening in medicine. "The idea here is, the scientific method itself is growing obsolete," Butte said.
    This scientific method as it has existed for decades and continues to exist in medicine today consists of a team of eminently qualified specialists from a variety of medical fields consulting with each other and sharing their collective experiences of treatment options and outcomes for the patient. In cases where unusual circumstances or risks present themselves, medical literature and empirical evidence that the scientific method demands are often missing. This happened in the Stanford lupus case, and this is where Dr. Frankovich was able to fill in the blanks with insights from data.
    Is this the end of the story? Not quite.
    Administrators at that hospital still feel it is safer to trust the wisdom of a team of doctors in urgent cases than to search medical records for data about what's worked in the past. In a January 2015 interview with NPR, Dr. Frankovich agreed, noting that "analyzing data is complicated and requires specific expertise. What if the search engine has bugs, or the records are transcribed incorrectly? There's just too much room for error....It's going to take a system to interpret the data, and that's what we don't have yet."
    This comes full circle to the upcoming big data conference at Stanford this spring. The conference announcement states, "While other industries have been far more successful at harnessing the value from large-scale integration and analysis of big data, health care is just getting its feet wet. Yes, providers and payers are increasingly investing in their analytical capabilities to help them make better sense of the changing health care environment, but it is still early days."
    True enough, but in healthcare settings like the Cleveland Clinic, doctors and medical practitioners are already making use of big data and analytics that diagnose conditions and prescribe treatments. What the analytics say is simply entered into the discussions that interdisciplinary teams of doctors have when they review patients. And while data quality and integration issues in healthcare continue to persist, there is an unmistakable beginning of a redefinition in the traditional scientific methods used in medicine.

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    How Twitter plans to move beyond its 288 million monthly active users

    With tweet syndication and video, the social media giant is looking to spread its influence across the wider web and this is good news for marketers.



    In a series of recent announcements, Twitter has started to flesh out its vision for commercialising its massive off-platform reach. Syndicated tweets will allowmarketers to take their real-time content efforts and spread them right across the wider web, while the increased focus on video hints at longer-term opportunities.Facebook managed to turn its mobile weakness into one of its biggest strengths, the question now is whether Twitter can capitalise on the opportunity that its wider cultural footprint offers it to do the same.
    It’s easy to admire Twitter’s vision to be the closest connection between people and what’s most important to them, but it takes something of a leap of faith to believe they’re going to do that for more of us than any other service. The 288 million monthly active users announced on their recent earnings call is a mightily impressive number, but less so when compared with Facebook’s 1.39 billion usersor the numbers for platforms you might never have heard of including QQ, QZone,WeChat or even Instagram, all of which have at least 300 million users.
    Far from being a distant pipe dream though, the most surprising fact is perhaps that Twitter may be pretty close to achieving its world leading ambitions. Around 500 million additional logged out users visit the site each month, and in Q3 2014 alone there were approximately 185bn impressions of tweets off of Twitter. These tweets appear in newspapers and magazines, get quoted on TV, are embedded across the internet, and even pop up in Google search results, in fact the only way to completely avoid Twitter’s wider reach is to avoid these other media outlets altogether.
    It’s a powerful story, and one enticing to marketers who know that ultimately they need to get their messages in front of as many of the right consumers as possible. This high visibility across other media, and heavy personal use within the industry, are some of the reasons brands have already begun heavily investing on the platform with its quarterly earnings hitting nearly $500m. Twitter’s challenge, and the reason Wall Street investors remain hesitant, is that to date there has been little opportunity to commercialise this off-platform reach.
    While the idea of brand messages spreading out to an audience of hundreds of millions of non-users is appealing to marketers, the unfortunate reality is that it isn’t their tweets currently benefiting from this scale. Around big sporting events for instance, mainstream media picks up messages from the players, sporting experts, or even whichever member of the Kardashians happened to attend – unfortunately they don’t typically also include your favourite car brand’s latest promotions. Traditional celebrities, established experts and a new world of social influencers are driving this scale, not the brands who most want to benefit from it.
    The new syndicated tweets product allows brands to take any of their Twitter content and push it out as native advertising to millions of consumers across a series of partners, starting with Flipboard and Yahoo! Japan. This presents a powerful opportunity to scale up all content campaigns and will help silence critics who in the past have used suggestions of Twitter’s low local user penetration to dismiss the platform.
    Real-time marketing isn’t right for every brand, but for those who do go down that route, being able to break that activity out of social jail makes it a lot more disruptive and impactful. Brands looking to respond to big events have struggled, with very few exceptions, to get much share of voice but being able to take their latest content and push it right across the internet will help drive truly meaningful scale.
    Of course just as Twitter themselves have started to talk about the broader opportunities on the native platform, syndicated Tweets will also do much more – helping drive direct response initiatives, CRM work and more general campaign & sponsorship extensions. When you consider how Twitter has evolved the tweet over the past couple of years (with cards bringing a range of functionality from website previews to interactive elements) you start to see the beginnings of a vision for a completely new ad format, anchored by a brand’s Twitter presence.
    And as for video? Both Facebook and Twitter have made heavy advances in their own native functionality and built out powerful propositions for marketers, but the latter’s additional reach might yet be its trump card here too. Journalists are used to embedding tweets into their articles and as more of this content becomes video based they will naturally start embedding that too. Twitter will begin to find its video content playing out in the far corners of the web right where so many of its Tweets are already being read.
    While embedded text ultimately offers little commercialisation opportunities, we’re far more used to seeing advertising associated with video – over the coming months Twitter will begin serving hundreds of billions of video impressions right across the internet, each one potentially an opportunity to engage consumers with carefully targeted pre-roll videos (although they’ve made no announcement of such a product to date). It’s an area that Facebook lags behind in and although YouTube has a long history of being the embedded video format of choice Twitterlooks set to be its most credible threat in years.



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    Is Your Leadership Style Right for the Digital Age?



    Advancement in digital technologies has disrupted everything, including leadership styles, according to Barry Libert, Jerry Wind and Megan Beck Fenley. Employees want more ownership rather than to follow instruction; customers want to participate in the marketing and development process; and leaders are finding that open and agile organizations are able to maneuver more effectively than organizations where “all insight and direction comes from the top. In short, the autocratic Commander, whether brilliant or misguided, just won’t cut it anymore,” they write in this opinion piece.
    History is full of great Commanders. The stories of General Patton commanding his troops before D-Day, Steve Ballmer yelling at his employees to “get on their feet” at a Microsoft event, and Jack Welch berating his people as he barked his orders “straight from his gut” are all well documented. These leaders accomplished great things and relied heavily on a “Command and Control” style of leadership. However, leadership preferences are evolving in parallel with a number of market and cultural shifts. Their successors, General Colin Powell, Jeff Immelt (GE) and Satya Nadella (Microsoft), as well as a host of other executives like Tony Hseigh of Zappos or Marc Benioff of salesforce.com, more often take on the role of Collaborator or Co-Creator, rather than Commander. And for good reason: These less autocratic leadership styles resonate with today’s empowered, connected and skeptical customers and employees — often leading to increased innovation, loyalty, profit and growth. Twitter 
    So what has changed in the last 20-30 years to require new ways of leading? Technological advancement has created a ripple effect that is transforming the market. Today’s digital technologies — social, cloud, big data analytics, mobile and the Internet of everything — have created new, intangible, sources of value, such as relationships and information that are delivered by new business models. Along with the new sources of value, customers and employees’ wants and needs have evolved as digital technologies have created new ways of interacting with businesses. Attracting, satisfying and retaining these connected and savvy stakeholders requires leaders to learn some new tricks — but there are rewards. Businesses and leaders that adapt to this new environment see economic payout with higher profit, growth and valuations, and more. 
    New Leadership Styles 
    So what is a leader to do given this new digitally enabled and hyper-connected environment? Employees and freelancers (such as Apple’s developer community) want ownership, impact and recognition, rather than to follow instruction. Customers want to participate in the marketing and development process (witness how consumer/business relationships have grown on social media and the rise of crowdsourcing businesses like Victors and Spoils), rather than be told what they want and why. Leaders are finding that open and agile organizations are able to respond faster and more effectively to these developments than organizations where all insight and direction comes from the top. In short, the autocratic Commander, whether brilliant or misguided, just won’t cut it anymore. Leaders need a broader range of style options to match the broader range of assets companies are creating today.
    Figure 1: Disruption caused by new technology
    Figure 1: Disruption caused by new technology
    In our business model research, based on financial data from the S&P 500 companies, we found that Network Orchestrators — companies that invest in intangible assets, like relationships with customers and suppliers (Facebook, LinkedIn, Airbnb, TripAdvisor) have the highest Multipliers (price to revenue ratios) at an average of 8x (more details here). These value premiums result from rapid growth and low scaling cost, as noted by Jeremy Rifkin in The Zero Marginal Cost Society.Further, we identified that the different leadership styles complement some business models and detract from others because each business model leverages different types of assets, which perform best under different leadership styles.
    Since most companies are actually a composite of different asset classes and business types — for example, Nike manufactures shoes (physical), but also develops some software (intellectual) and is developing a network with Nike+ (network) — most leaders use several of the four leadership styles:
    Figure 2: Relationship Between Business Model, Leadership Style and Value
    Figure 2: Relationship Between Business Model, Leadership Style and Value
    The Commander sets the goal and tells others how to accomplish it. This works well with machinery, which happily does what it is told, and with direct subordinates who prefer to simply execute. It is less effective with employees and customers who want choice and participation. The result in today’s world is high marginal costs and little participation and buy-in. This style is most suited to the production of manufactured, commoditized goods as it is limited by the Commander’s vision and bandwidth.
    The Communicator also sets a vision and a plan, but communicates it in order to inspire and create buy-in. This works better with employees and customers who want to at least understand where “the firm is headed.” It enables them to take action in line with the leader’s vision (it scales effectively), but it does not encourage innovation. This style is suited to services firms where all employees must work to fulfill the mission.
    The Collaborator works hand-in-hand with customers and employees (be they full time, part time or independent) to achieve the organization’s goals. As a result, it is empowering and enabling. This style taps into the innovation of people and drives the creation of new intellectual capital. Great examples are open innovators such as Victors and Spoils, a collaborative ad agency and Merck with its crowd-sourcing competitions.
    The Co-Creator allows other stakeholders to pursue their individual goals in parallel with the goals of the organization. As a result, he or she drives both rapid scaling (due to the high level of participation) and innovation. This style is at the heart of network companies where value is shared by the company and the network participants, such as Airbnb, Uber and Innocentive.com.
    The four styles are differentiated in terms of scalability — how efficiently they enable growth — and innovation — whether controlled by the leader or shared with stakeholders. Most leaders are already able to employ several styles effectively (although co-creation is still a rarity). However, using leadership styles effectively, in the proportion required today, and in the right situations, is tricky. Let’s take a look at how these styles were used by a great leader. Steve Jobs isn’t often remembered for his collaborative, open leadership style, but a thoughtful review of his business choices and words reveals more flexibility:
    Commander: Jobs often had a specific vision for design that he would insist on.
    Communicator: Jobs’s inspiring keynote presentations are legendary.
    Collaborator: Jobs collaborated with others “to take music and sport to a new level.”
    Co-Creator: Jobs eventually built a developer network that is unprecedented.
    Figure 3: The Four Leadership Styles
    Figure 3: The Four Leadership Styles
    For Jobs, and for many leaders, co-creation can be uncomfortable. Given that network-based businesses are the most highly valued and profitable companies in today’s digital world, what does it take for a leader to co-create? Our answer: the ability to relinquish control and the willingness to share the value created with the crowd. 
    When Jack Dorsey and his collaborators developed Twitter in 2006, employees of their startup used it internally. As co-founder Evan Williams described it, “There was this path of discovery…. Twitter actually changed from what we thought it was in the beginning.” They had no idea the role it would play in sociopolitical movements, pop culture and business until the network actually started using and forming it. Although it may be difficult for founders to allow the network to shape their creation, that is the path to creating the most valuable, and valued, tool. 
    The same is true for companies like Airbnb, Etsy and Uber that actually share revenues with their partners. Their business models depend on the enthusiastic engagement of their partners (hosts, creators and drivers). But these multi-billion-dollar start-ups are not the only companies that use this new leadership style. So do established companies like Visa and MasterCard, stock exchanges and those that rely on open-source development, like Red Hat Software. These businesses survive and grow because of the participation, co-creation and co-ownership of their members. 
    Generating More Innovation, Growth and Profit 
    If you are a leader of a traditional company or industry, you may be thinking that Co-Creators are great for digital start-ups, or even existing membership based businesses, but not really applicable to you.  
    We disagree. Our research and others suggests that in the digital age there is much to be gained by increasing your leadership skillset to include Co-Creation, even if you aren’t a network company:
    Allowing partners to share in the value creation and provide resources greatly reduces your marginal costs of marketing, sales and distribution — for example, the way Uber avoids buying cars and hiring employees with its partner network; 
    Employees and customers who are co-creators — for example, those using Coca-Cola’s Freestyle machines to custom-make their own drinks — are more loyal and thus less price sensitive or likely to defect, improving customer lifetime value; 
    Co-creation leads to an influx of new ideas by opening the organization to the innovative capability of external sources (a great example is https://www.innocentive.com); 
    Co-creation builds a flexible and organic system that can more quickly adapt to market changes and new technologies (for example, Apple’s developer network can quickly jump on new trends and needs); and 
    Co-creative business models are growing at faster rates, are more profitable and more scalable than those that rely on proprietary, in-house solutions and people (see What Airbnb, Uber, and Alibaba Have in Common).
    In the end, the argument for leaders to co-create is an argument for profit, growth and value creation. Today, the most valuable assets are intangibles: relationships (with employees, customers and investors), knowledge (ideas) and people. The newest business model, Network Orchestration, taps into these “assets” at low or near-zero marginal cost of scaling, resulting in rapid growth, higher profit margins and, ultimately, greater investor returns.
    Remember that your firm already has dormant networks of customers, employees and partners that want to share in value creation, and are already doing so with other firms. They are an enormous asset, but one that cannot be tightly controlled, even by the best executives. Only leaders who are able to relinquish some control and share the rewards will be able to access the value that these groups have to offer.
    “Remember that your firm already has dormant networks of customers, employees and partners that want to share in value creation, and are already doing so with other firms.”
    Building Today’s Digital Leadership Styles
    Leaders who wish to add co-creation to their playbook should be guided by the following four guidelines:
    Understand your innate preferences. Everyone is naturally inclined to a particular style of leadership. Assess your own capability with each of the four leadership styles. Take a test atwww.digitalgrader.com/leadership-survey.
    Find mentors to support your development. Seek out leaders with strengths in this new style of leadership. It is hard to change without support, and mentors provide external perspective and give practical ways to change your approach. Reverse mentoring, where younger employees advise the leadership, is also a great option for leaders coming up to speed on new digital technology and cultural shifts.
    Experiment with new business models: Dedicate yourself and your team to regular exercises and workshops that hone your co-creation skills. Begin to experiment with co-creative, network businesses by investing some of your capital into business initiatives that require co-creative leadership. 
    Create measurable goals for co-creation. Successfully co-creating looks different than successfully commanding. Update your personal and leadership team objectives with appropriate indicators: customer or employee engagement, participation, loyalty and co-creation. It will keep you on the straight and narrow. 
    Remember, every one of us possesses a “portfolio” of leadership styles and each one has its place. A surgeon may be a Commander in the operating room, a Communicator with patients and a Collaborator when performing research. However, the styles that created value for many leaders decades ago are less effective with today’s empowered stakeholders — and since 95% of companies are not Network Orchestrators, we suspect that most leaders lack strength at co-creation. The digital, cultural and asset revolution provides a fantastic opportunity for shared success — increased growth and profit for businesses, and increased value for customers — but creating network-based businesses will require openness, adaptation and the development of new leadership skills.

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    Growing a digital social innovation ecosystem for Europe



    This report coordinated by Nesta and commissioned by the European Commission, DG CONNECT is the first systematic network analysis of the emerging digital social innovation (DSI) ecosystem in Europe. 

    Key Findings

    • The report identifies more than 1,000 rising examples of digital social innovation organisations across Europe, and the hidden links among them.
    • Social innovation in Europe is currently done by a few large organisations alongside a large mass of smaller organisations, but the majority of social innovators in Europe are disconnected from the bigger networks.
    • The largest and more interconnected community is focused around open hardware and open networks, and there is a large focus on awareness networks and new ways of making.
    • The open knowledge cluster is the second largest, with a focus on collaborative economy.
    • THe third largest network is grouped around Nesta and is focussed on funding, acceleration and open democracy. Other communities, such as those grouped around open data are developing connected communities.
    A growing movement of innovators in civil society, tech and social entrepreneurs are now developing inspiring digital solutions for a variety of social issues, in areas such as health, democracy, consumption, money and education.
    We have identified DSI organisations and projects as part of a larger social network and have mapped this network in a way that has not been possible before. 

    Digital technologies and the internet have transformed many areas of business – from Google and Amazon to Airbnb and Kickstarter. Huge sums of public money have supported digital innovation in business, as well as in fields ranging from the military to espionage. But there has been much less systematic support for innovations that use digital technology to address social challenges.

    Over the last 18 months Nesta, funded by the European Commission, has led a large research project into DSI. The project seeks to define and understand the potential of DSI, to map the digital social innovators, their projects and networks, and to develop recom­mendations for how policymakers, from the EU to city level, can make the most of DSI.


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    10 Fundamental Success Truths We Forget Too Easily 

    It’s surprising how easy it is to lose sight of the important things in life. Busy schedules and weekly routines have a tendency to put the brain on autopilot.


    Some of life’s essential truths need repeating. Keep this list handy and give it a read any time you need a boost.

    1. Life is short

    None of us are guaranteed a tomorrow. Yet, when someone dies unexpectedly it causes us to take stock of our own life: what’s really important, how we spend our time, and how we treat other people.

    Loss is a raw, visceral reminder of the frailty of life. It shouldn’t be.

    A great day begins with a great mindset. Remind yourself every morning when you wake up that each day is a gift and you’re bound to make the most of the blessing you’ve been given. The moment you start acting like life is a blessing is the moment it will start acting like one.

    2. Being busy does not equal being productive

    Look at everyone around you. They all seem so busy—running from meeting to meeting and firing off emails. Yet how many of them are really producing, really succeeding at a high level? Success doesn’t come from movement and activity. It comes from focus—from ensuring that your time is used efficiently and productively. You get the same number of hours in the day as everyone else. Use yours wisely. After all, you’re the product of your output, not your effort. Make certain your efforts are dedicated to tasks that get results.

    3. You’re living the life you have created

    You are not a victim of circumstance. No one can force you to make decisions and take actions that run contrary to your values and aspirations. The circumstances you’re living 

    in today are your own—you created them. Likewise, your future is entirely up to you. If you’re feeling stuck, it’s probably because you’re afraid to take the risks necessary to achieve your goals and live your dreams. When it’s time to take action, remember that it’s always better to be at the bottom of the ladder you want to climb than at the top of one you don’t.

    4. Great success is often preceded by failure

    You will never experience true success until you embrace failure. Your mistakes pave the way for you to succeed by revealing when you’re on the wrong path. The biggest breakthroughs typically come when you’re feeling the most frustrated and the most stuck. It’s this frustration that forces you to think differently, to look outside the box and see the solution that you’ve been missing. Success takes patience and the ability to maintain a good attitude even while suffering for what you believe in.


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