Data & Analytics

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Why It Pays to Be Where the IT Talent Already Is

As demand for big data technologies grows, so does the problem of finding sufficient skills. Result: Talent shortages could limit the rate of productivity growth. Research shows that labor-market factors have shaped early returns on investment in big data technologies such as Hadoop, a framework for distributed processing of large data sets. It turns out that when know-how is scarce, organizations that invest in new IT or R&D derive significant benefits from the related investments of other organizations.


Designing and Developing Analytics-Based Data Products

The combination of new analytical capabilities and burgeoning data assets are being used to form value-added “data products.” Such products have powered rapid growth in the value and success of online companies, but the expansion of analytics means the standard model for developing these products needs to evolve. An updated model needs to reflect new “time to market” expectations and input from a variety of stakeholders.

Image courtesy of Flickr user Keith Allison

Stephen Curry, the Golden State Warriors, and the Power of Analytics at Work

Organizations across an increasing number of sports and levels of competition are capitalizing on data to gain a competitive edge. Indeed, few industries have implemented data-driven decision making as successfully as sports. And learnings from the sports analytics revolution are applicable to a broad range of other industries.


Building a Better Car Company With Analytics

Using data and analytics to understand the complexities of modern business has become not only common, but essential. Gahl Berkooz joined Ford Motor Co. in 2004, eventually becoming head of data and governance and a member of the company’s global data insights and analytics skill team. Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. “What it boils down to,” he told MIT SMR’s Michael Fitzgerald, “is that we know how to make decisions. It’s about finding the opportunities to bring data and analytics to make better decisions.”



Pushing the Boundaries of Predictive Analytics and the IoT

From sensing issues with turbine engines to identifying non-standard washing machine loads, predictive analytics are a given in the Internet of Things (IoT). But what will happen to predictive analytics once everything is connected? This list of five links points to predictions and calculations that some people in the field are making.


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

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

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.



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.


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.


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.


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 supply chain. The case study includes video clips of interviews and a downloadable PDF version.


Once You Align the Analytical Stars, What’s Next?

You’ve figured out how to get the data, and how to make sure it’s good quality. You’ve hired the right people to put your data through the analytics wringer. Now you’ve got the results in your hands &mdash and you may not be sure what to do next. Consuming analytics effectively — and getting business value out of your analytics — is a challenge for many companies, and executives must get creative to increase their comfort level.



Minding the Analytics Gap

While analytical skills are improving among managers, the increasing sophistication of analyses is outpacing the development of those skills. The resulting gap creates a need for managers to become comfortable applying analytical results they do not fully understand. A 2014 survey by MIT Sloan Management Review, in partnership with SAS Institute Inc., highlights the ways that companies can address this problem by focusing on both the production and consumption sides of analytics.


Detecting Bias in Data Analysis

Data analysts may have external agendas that shape how they address a data set — but Boston College’s Sam Ransbotham argues that a savvy manager can identify biases by learning to question the underlying assumptions that go into dataset cleanup and presentation.


How to Hire Data-Driven Leaders

For recruiters, the technological developments of the past 3 years have been transformational, says Tuck Rickards of Russell Reynolds. With the transformation of business to a more real-time, connected, data-driven focus, the type of talent companies seek — even the type of organizational structure they’re building — has undergone a quantum shift. But the changes aren’t yet done: “The next five years are huge for companies to reorient themselves from a leadership and team perspective,” warns Rickards.


Data Scientist In a Can?

Companies will want hundreds of thousands more data scientists than exist, creating a much discussed skills gap. In the past, businesses have figured out how to automate in-demand skills, and some companies say they can automate what data scientists do. What does it mean for companies when they do the equivalent of putting their data scientists into a can?

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