Data & Analytics

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Enough Health Care Data for an Army: The Million Veteran Program

The holy grail of medicine is therapy that is customized for the patient. But to get there, health care researchers need huge amounts of data to help identify which genes affect health. The Million Veteran Program has tapped one of the largest cohorts available — U.S. military personnel — to obtain the dataset, but managing the security of this sensitive data is a challenge. In a Q&A, two of the project’s lead scientists, J. Michael Gaziano and Saiju Pyarajan, explain the process.

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Ready or Not, Here IoT Comes

The Internet of Things is on the brink of transforming business, but most businesses aren’t ready for the changes to the marketplace that the IoT will bring. There is very little time for companies to prepare for the changes coming as data-collecting devices proliferate. The good news is that by recognizing certain challenges, organizations can begin the possible, albeit difficult, process of getting ready.

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The Ethics of Wielding an Analytical Hammer

With analytics as a hammer, so many questions can start to look like nails. It is difficult for organizations to know what to do. But the “should” in “What should we do?” goes beyond just selecting what to hammer on for maximum insight. Companies need to pay attention to the ways in which the possibilities that analytical abilities create involve responsibilities as well.

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Viewing Data as a Liquid Asset

Most people recognize their data as an asset — yet few regard it as a liquid asset. But a chance meeting opened up an opportunity for using data assets in a different way to support R&D — and uncovered a whole new path for financing of science and tech research. SVB Analytics head Steve Allan explains how using analytics “allows us to ask if we need to look at the data a different way.”

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Data and Devices Bringing Transparency to Energy Use

Hugh Scandrett, VP of engineering for EnerNOC, is bringing transparency to an energy system that works against clarity. The goal: help companies realize more cost savings and cut back on energy usage. In a Q&A, Scandrett says that one big issue for companies is predicting future demand. “We predict a company’s usage based on analytics that look at weather, degree of sun azimuth, and a whole set of other parameters,” he says. “We then can provide techniques for minimizing peak usage, like pre-cooling a building.”

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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.

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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?

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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.

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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, AllAnalytics.com, 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 https://www.flickr.com/photos/shahdi/5210439036/

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.

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Image courtesy of Flickr user janneke staaks https://www.flickr.com/photos/jannekestaaks/14391226325

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.

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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.

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