Analytics & Strategy

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Better Decisions with Smarter Data

Part of that gap between the pressure to be more data-driven in decision making, and a lack of useful data, says Grenoble University researcher Ali Fouladkar, can be explained by the fact that while data analysis has led to competitive advantage for some organizations, decision makers are often presented with noise-filled big data. What they need instead is smart data, but the problem in getting to smart data in an era of big data is part technological and part training.


Luminar Insights

Facing headwinds from a shifting media industry, executives at Spanish-language broadcasting company Entravision recognized the need to innovate their business model. To get there, they created Luminar, a big data insights division that utilizes about 2,000 external data points to deliver customized, transaction-based insights to marketers about Entravision’s Latino audience. The fastest-growing U.S. demographic, Latinos have amassed buying power worth more than a trillion dollars annually.


Video: Leveraging Analytics to Transform a Traditional Broadcast Business

Using big data technology, Luminar is helping to transform its parent company, Entravision, beyond its broadcasting roots. With a data store of 50 million Latino adults with transactional records from 2,000 sources, Luminar has insights on 65% of the US Latino adult population. Luminar is able to provide new, relevant insights to advertisers and marketers interested in reaching the Latino consumer, and in the process change Entravision from a broadcast company to an insights company.

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Should You Outsource Analytics?

Outsourcing analytics activities can offer benefits, but it requires a carefully constructed relationship between the company and the business process organization (BPO). Customers must be careful not to lose their expertise or their core intellectual property. Research suggests that companies with superior analytics capabilities will approach outsourcing differently than companies that are analytically challenged.

NJ in the foreground, NY in the background
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Are Predictive Analytics Transforming Your Supply Chain?

While some industries like health care and retail are starting to see the transformational potential of big data and predictive analytics, these strategies haven’t quite panned out for supply chain managers. Why? Two big barriers: The cost of hiring skilled employees and the complexity of connecting nodes across an extended supply chain network. New research suggests that the convergence of data science, predictive analytics and big data have the potential to transform the way in which supply chains leaders lead, and supply chains operate.


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Turning Big Data Into Smart Data

Current thinking has big data and analytics turning companies into data-driven powerhouses. But if companies don’t know how to use data well, it can actually prevent companies from making the transition to data-driven operations. Jeanne Ross, director of MIT’s Center for Information Systems Research, says that companies can build cultures that encourage people, even low-level employees, to work well with data by using evidence-based management.

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How Big Data is Influencing Big Companies

A recent data and analytics survey sheds light on what large organizations are actually doing with big data initiatives. The survey of nearly 100 senior executives from Fortune 1000 companies found that executives are reporting anincreasing commitment to big data projects. As well, most executives, 70%, are looking to improve their “time to answer” with big data.

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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, Yu Jeffrey Hu and Mohammad Rahman 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.


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 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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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.


From Value to Vision: Reimagining the Possible with Data Analytics

Based on a global executive survey with 2,500+ respondents and interviews with more than two dozen executives and academics, MIT Sloan Management Review and SAS Institute Inc. report on the distinctive characteristics and habits of companies that are very effective at using analytics to compete and innovate. This report offers an in-depth analysis of Analytical Innovators, the early leaders in the analytics revolution that is changing how many companies are managed.

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Location Analytics: Bringing Geography Back

Remember geography lessons in school, painstakingly memorizing the longest rivers, cultures of various regions, the state capitals? For most of us, those lessons are in the past. But in the world of big data analytics, geography is making a comeback. The relatively new market of location analytics is expanding the uses of more traditional geographic information system (GIS) technology to include social, geographic, physical and emotional indicators that help organizations better predict trends


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Quick Wins Help Avoid Culture Obstacles on the Path to Value

"The biggest predictor of success...has been when there's a strong business sponsor involved," says Randy Bean, co-founder of NewVantage Partners. Broad-based organizational support usually follows when the business sees how analytics will improve the top and bottom line.
K. Ananth Krishnan is chief technology officer of Tata Consultancy Services Ltd.

The “Unstructured Information” Most Businesses Miss Out On

Businesses’ ability to process numbers in “well-behaved rows and columns” goes back 40 years, notes K. Ananth Krishnan, chief technology officer of Tata Consultancy Services, one of the largest companies in India. Figuring out how to mine and process the information in text, video, and audio is the new frontier.

Image courtesy of Flickr user Nathan Eal Photography.

Why Companies Have to Trade "Perfect Data" for "Fast Info"

Companies have been trained to think about data all wrong, say Attivio’s Ali Riaz and Sid Probstein. “Analytics don't have to be based on super-precise data,” they say. “The report doesn't have to be perfect. It needs to capture the behavior, not the totality of it."

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