Data & Analytics

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Overcoming Legacy Processes to Achieve Big Data Success

Most large corporations are saddled with fragmented analytical processes, limiting their ability to operate with agility, flexibility, and insight. As a result, larger firms are often challenged when it comes to innovation and responsiveness. But Big Data approaches that enabled the flexibility and rapid growth of newer, smaller firms are being adopted by mainstream corporations. The goal: overcome legacy challenges and introduce greater corporate speed.


Using Unstructured Data to Tidy Up Credit Reporting

Greg Jones, vice president of Enterprise Data & Analytics at Equifax, says the credit reporting agency is beginning to incorporate unstructured data from sources such as social media to better round out the individual profiles in its database. “My focus is to create a compelling differentiator between us and the other credit reporting companies by enabling our customers to provide the most efficient, the most predictive, and the most accurate experience for their customers,” he says.

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Secrets in the Age of Data

Secrets may be an unexpected casualty of increasing analytical prowess — just ask Volkswagen. Companies often have information they’d rather keep under wraps; sometimes it’s innocuous, like the timing of a new product launch, but other times it’s embarrassing details about unethical or even criminal behavior. But as data analytics becomes more broadly available, the chances of keeping secrets out of public view grow slimmer every day. Will this result in a change in how companies do business?


Preparing for Disruptions Through Early Detection

In an adaption from his new book The Power of Resilience, MIT’s Yossi Sheffi explains how companies are learning to more quickly detect unanticipated problems that can interfere with their global operations. Sheffi looks at how leading companies are using an array of detection and response techniques, from sensors to supply chain control towers. These tools are helping companies become more resilient to disruptions such as hurricanes, the discovery of product contamination, and political events.


Sharing Supply Chain Data in the Digital Era

Effectively managing and coordinating supply chains will increasingly require new approaches to data transparency and collaboration. Supply chains in coming years will become even more “networked” than they are today — with significant portions of strategic assets and core capabilities externally sourced and coordinated. Already, progressive companies are developing novel solutions to the dilemma of data transparency by using data “cleanrooms” and digital marketplaces.



Managing Data in the Age of the Internet of Things

Organizations have made great progress with analytics using traditional data sources, but Internet of Things (IoT) will mean a new upsurge in data, and attendant challenges in absorbing and analyzing that data. In this webinar, analytics experts Lynn Wu, Sri Narasimhan, and Sam Ransbotham discussed the data and analytics opportunities presented by this phenomenon.


Telling Data’s Story With Graphics

At the alcohol beverage company Constellation Brands, graphic presentations of data are making it easier for sales people to see how they’re performing. In an interview with MIT Sloan Management Review, Joseph D. Bruhin, the company’s CIO, says that measuring marketing and sales efforts is a particular challenge in the alcohol industry — but one that his team has come up with a solution to. “Visibility of data is a critical piece,” he says. “We came up with a solution that’s really driven predominantly by information technology.”

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Deodorizing Your Data

Problems with data quality come from a lot of sources — short-term solutions, mergers or acquisitions, or even the mundane complications of living in a complex society. The “stench” that develops when data quality declines can create serious issues for data-driven business. If a foul odor is emanating from your data, one solution might lie in refactoring analytics processes.

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Participant Questions From the Recent “Internet of Things” Webinar

On July 30th, 2015, MIT Sloan Management Review hosted a webinar on “Managing Data in the Age of the Internet of Things.” At the end of the webinar, many participants asked questions, but we didn’t have time to answer them all during the webinar itself. We’ll answer some of the most popular questions here. Included: Should an international organization be required to take control of uniting the Internet of Things (IoT) into one system?

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The Talent Dividend: Interactive Infographic

An interactive infographic from MIT SMR’s content collaborator, SAS, and its partner site,, highlights findings from the 2015 data and analytics research report, The Talent Dividend. The animated infographic illustrates several key stats from the report, including findings on finding, acquiring and managing analytics talent, and on changes to how companies are leveraging analytics for competitive advantage.


Image courtesy of Flickr user enshahdi
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How to Build (and Keep) a World-Class Data Science Team

To manage a first-rate data science or quant group, leaders need to build an engaging environment, get the team the resources it needs and balance being involved while also staying out of the way. In banking, for example, division managers generally don’t review loan applications. But in analytics, the most successful leaders engage regularly in hands-on research and continue to publish regularly even as they move up the executive ladder. By staying active in line research, analytics managers are able to hone their abilities to judge how difficult projects are and how long they will take.

Image courtesy of Flickr user janneke staaks
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Why Managing Data Scientists Is Different

The process of managing a data science research effort “can seem quite messy,” writes MIT Sloan's Roger M. Stein. That can be “an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.” While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talent are structured and managed.


Marketing In Five Dimensions

Computers, scanners, mobile and wearable technology have made it both easier and harder for companies to find their customers. Easier, because there’s so much more data about consumer behavior; harder, because analyzing that data is a significant challenge (never mind deciding how to act on the analytics). Companies like Epsilon are stepping up to help businesses to figure out what the data tell them about their customers — and what to do with that knowledge. In a Q&A, Epsilon’s CEO Andy Frawley describes some of the challenges his company works through on a daily basis.

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Better Decision Making with Objective Data is Impossible

“Our world is awash in data, and data is not the same thing as facts,” writes Boston College's Sam Ransbotham. “While data seems to promise objectivity, instead it requires analysis — which is replete with subjective interpretation.” Ransbotham argues that while having data is a necessary step towards making objective decisions, it’s a myth that data is objective. Moreover, findings that counter current thinking provide organizations with opportunity for distinction, differentiation and advantage.

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Using Big Data for Better Health Outcomes

Intermountain Healthcare is leading the way in data driven healthcare. In an example from Intermountain’s own operating rooms, the use of data to measure the impact of standardized surgeon attire on infection rates resulted in a significant drop in those rates. The infection control scenario is just one result from decades of work at Intermountain to build a data culture. Over the years, clinicians have learned to work together on a concerted effort to bring data based insights to clinicians and managers.


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The New Data Republic: Not Quite a Democracy

There are clear signs that the movement to democratize data is making real progress. Barriers such as infrastructure, culture, tools, and governance that once kept data access limited are quickly eroding. But access to data isn’t enough: Data democratization also requires knowing how to work with data and understand data analysis tools and techniques. Without these capabilities, the data democracy is only an illusion — and most people are still unable to participate fully.


At This Education Nonprofit, A Is for Analytics

In an interview with MIT Sloan Management Review, Christopher House CEO Lori Baas and director of quality assurance Traci Stanley explain how they're using data throughout their educational organization to track student outcomes and look for improvements. "We now can show, based on the assessments, not only how our kids are improving in their cognitive development, or social-emotional development, but also how we compare to similar organizations," says Bass.


Innovating with Airborne Analytics

Hong Kong’s premier airline is using a blend of data and know-how to guide its daily operations. In an interview with MIT Sloan Management Review, Cathay Pacific CIO Joe Locandro describes how the airline uses analytics to make decisions that balance data with what it knows from the field. “Analytics will give you statistical spreads, give you training, but you still need to have this thing called experience and insight,” he says.


When Health Care Gets a Healthy Dose of Data

American health care is undergoing a data-driven transformation — and Intermountain Healthcare is leading the way. This MIT Sloan Management Review case study examines the data and analytics culture at Intermountain, a Utah-based company that runs 22 hospitals and 185 clinics. Data-driven decision making has improved patient outcomes in Intermountain's cardiovascular medicine, endocrinology, surgery, obstetrics and care processes — while saving millions of dollars in procurement and in its the supply chain. The case study includes video clips of interviews and a downloadable PDF version.


The Analytics Talent Dividend

In May 2015, co-authors Sam Ransbotham, David Kiron and Pamela Kirk Prentice presented the findings from the recent global sustainability study, “The Talent Dividend.” The study found that the integration of analytics talent into the organization is key to analytics success. The webinar speakers discuss the components of a human resources plan for analytics talent, and give guidance on how to implement that plan in your organization.

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