Data & Analytics

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The Relevance of Data: Going Behind The Scenes at LinkedIn

It is an understatement to say LinkedIn is growing like a weed. With 238 million members in over 200 countries, 2.8 million active company profiles, and 1 million professionally oriented groups, LinkedIn has become the world’s largest professional networking site.

Deepak Agarwal, LinkedIn’s director of relevance science, explains how his company uses data and analytics to sustain this growth.

Image courtesy of Flickr user Paul L Dineen.
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Does Data Have a Shelf Life?

Recent research out of the Department of Operations and Information Systems at the University of Utah, Salt Lake City, and the Department of Management Information Systems, Eller School of Management at the University of Arizona, Tucson, asks a seemingly simple question about organizations’ data collection and usage that could have some big implications on your own data techniques.

The question: When is the right time to refresh data to support organizational decision-making?

Image courtesy of Flickr user Vermin Inc.
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Omnichannel Retailing and Data Analytics: Leveling the Playing Field

We’re in a new world of omnichannel retailing that includes physical, online and mobile channels. And those channels are blurring.

In a recent AllAnaltyics video and web chat, Analytics in the Age of Omnichannel Retailing, researchers Erik Brynjolfsson, director of the MIT Center for Digital Business, Yu Jeffrey Hu, associate professor at Georgia Institute of Technology’s Scheller College of Business, and Mohammad Rahman, associate professor at University of Calgary’s Haskayne School of Business, discussed the challenges facing retailers.

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Big Data That’s Good for the Public

Facts: 900 million. Active sources: more than 100,000. Data sets: 30,000, with 200 million time series and 1.5 billion fact values.

Link all these data sources together and what do you get? Timely, if not crucial, contextual information about markets, trends, competitors, products and consumer opinions.

This is the promise of DOPA, a project funded under the umbrella of the European Union.

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Transparency as a Competitive Advantage: Think Very Carefully About Communicating Your Data Sharing Initiatives

In the weeks following revelations that the NSA has a domestic spying network that taps the electronic and telephone communiqués of nearly every American, consumers have intensified their concerns about corporate complicity in government data snooping. That leads to the question: Are we at the beginning of a consumer backlash that will stymie expected economic growth related to data-sharing? Or are consumers resisting the inevitable: a new era of diminished privacy?

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A Process of Continuous Innovation: Centralizing Analytics at Caesars

Over the past several years, Caesars has undergone a reorganization, in part to centralize its analytics functions. It has sought to build a deeper understanding not only of customers, but also of operations — everything from food and beverage analytics to labor analytics. Ruben Sigala, chief analytics officer at Caesars, talks with MIT Sloan Management Review contributing editor Renee Boucher Ferguson about that process, some valuable lessons learned, and where innovation and intuition play a role.

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The Pitfalls of Using Online and Social Data in Big Data Analysis

Is Twitter a litmus test for how a segment of society is acting — or thinking — at any given moment? Not quite. Striking new research out of Princeton University and the University of North at Carolina Chapel Hill suggests that inferences based on how people use social media platforms like Twitter and Facebook should be reconsidered because these platforms represent skewed samples from which it is difficult to draw accurate conclusions.

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Big Data’s Travails Don’t Mean It’s Derailed

Executives are growing dismissive of Big Data’s value. Even the best companies can struggle to get good results from their data. But data isn’t getting smaller, it’s getting much, much larger. Corporate executives should look at what’s emerging from universities like MIT, where researchers are beginning to get answers to longstanding big questions in healthcare, public policy and finance.

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How eBay Uses Data and Analytics to Get Closer to Its (Massive) Customer Base

You can find just about anything on eBay: A vintage BMW, a Lear jet, a half-million-dollar yacht. From all that activity stems a lot of data and, eventually, information. In conversation with MIT Sloan Management Review contributing editor Renee Boucher Ferguson, Neel Sundaresan, senior director of research at eBay, discusses how eBay uses data and analytics at every level of the company to continuously evolve eBay’s numerous sites and services for buyers and sellers.

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When Competition Shifts From the Known to the Unknown

In a research paper, Masters of Big Data: Concentration of Power Over Digital Information, Alessandro Mantelero posits that because not everyone has access to all sources of information — or the ability and infrastructure to exploit data — there is a concentration of a new kind of power: the power of information.

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Analytics is Transforming Customer Service: Should We Worry?

Customer service and … cognitive computing? Really?

Yes, it’s happening. A recent Forbes article, IBM’s Watson Now a Customer Service Agent, Coming to Smartphones Soon, describes cognitive computing’s growing influence on customer service at a wide range of consumer-facing organizations — think financial services, telecoms, retail and insurance companies. ANZ Bank in South Africa offers a glimpse into a present that sounds more fictional than real.

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Big Data and Big Change Management: A Path Forward

MIT Sloan School of Management recently held a two-day executive education course, Big Data: Making Complex Things Simpler, designed to provide organizations with both an overview of big data and a few techniques to harness some of the illusive power of data. But in all the discussions that surfaced around the big issues from big data, the reality that many organizations grapple with is change management.

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Educating Executives Online in Four Dimensions

I am the proud recipient of an MIT Sloan Executive Education Course certification. Or, my avatar is.

MIT Sloan’s Big Data 4Dx executive education course, held simultaneously in Cambridge and in the virtual world this April, was the first ever to use a gaming interface. Think Second Life for the professional set.

Through the AvayaLive Engage platform, we online students were transported (as avatars) to a virtual auditorium, the classroom setting for the two-day Big Data: Making Complex Things Simpler course.

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Predicting the Performance of Analytics Talent

The surprising finding that 55% of big data analytics projects are abandoned comes from a recent survey of 300 IT professionals. The most significant challenge with analytics projects, according to the survey? Finding talent. Most (80%) of the respondents said that the top two reasons analytics projects fail is that managers lack the right expertise in house to “connect the dots” around data to form appropriate insights, and projects lack of business context around data.

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Reimagining the Possible with Data Analytics? Some Answers to Your Questions

During our webinar, we described those characteristics that define each group, and what your organization can do to become more like Analytical Innovators — if you’re not there already (in a poll, 13% of audience members identified themselves as Analytical Innovators).

As the hour-long webinar came to a close, we found that there were far more questions than we had time to answer. And we also found that there were far too many intriguing questions to let them go unanswered.

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How Analytics Can Transform Business Models

There are 52 million Latinos in the United States, with $1.5 trillion of purchasing power.

Entravision Communications Corporation a Spanish-language media company, reaches about 96% of that U.S. Latino audience through its numerous television and radio stations and digital platforms. It uses that extraordinary reach to provide media solutions to marketers interested in tapping into the Latino consumer market.

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Lessons From Analytical Innovators

In a webinar recorded in March 2013, the speakers present findings from the recent global study they co-authored, “From Value to Vision: Reimagining the Possible with Data Analytics.” In their study, they identified leaders of the analytics revolution they call “Analytical Innovators.” These companies share three key characteristics: a widely shared belief that data is a core asset; more effective use of more data for faster results; and support for analytics by senior managers shift power and resources to those who make data-driven decisions.

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Competitive Advantage with Data? Maybe … Maybe Not

IBM chairman, president and CEO Virginia Rometty has been making the rounds of late — in February she spoke at the annual IBM investor briefing; in March to the Council on Foreign Relations — espousing a bold new future built upon big data.

At the investors’ meeting, Rometty said that big data — defined as data streaming in to the worlds’ organizations from cloud, mobile and social networks (and presumably mingled with enterprise data) — will be the basis of competitive advantage for every company and every industry for the next decade.

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Analytics and Intuition: Finding Equilibrium

Just before the New Year, New York Times writer Steve Lohr wrote a blog, Big Data is Great. But So Is Intuition, which addresses a question we here at MIT Sloan Management Review have been researching for the past year: With the growing potential of data and analytics, where is the shifting line between analytics and intuition?

Image courtesy of Flickr user MegMoggington.

Team GB: Using Analytics (and Intuition) To Improve Performance

Becoming an elite athlete — or coaching a team of this rarified breed — has as much to do with talent and skill as it does with experience and intuition (not to mention some serious hard work). And data is increasingly part of that mix at the highest echelon of sports: the Olympic Games. At Team GB analytics are used to both monitor the performance of athletes and to predict how well a team will perform. But what could the future hold? Evidence-based coaching — and training.

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