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Friday, February 27, 2015

Indian startup shows how the cloud can be used to transform education in rural India 02-27

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.

Dell and IIT Madras Join Hands to Drive Research in Next-Generation Infrastructure and Cloud Technologies 02-27

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.”


Wednesday, February 25, 2015

MIT Invents A Social Network You Can Wear 02-25



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?

Google launches 'smart' spoon to help steady shaking hands 02-25

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.

Why We Pay to Save Time 02-25

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.


Leveraging Social Networks to Drive Collaboration and Improve Execution 02-25


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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Saturday, February 21, 2015

Data Scientist: The Sexiest Job of the 21st Century 02-21

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.

Thursday, February 19, 2015

Where the Digital Economy Is Moving the Fastest 02-20

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.

Wednesday, February 18, 2015

How big data from space helps life on earth 02-18

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.