Data & Analytics

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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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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.

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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.

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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.

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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.

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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.

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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.

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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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Is Your Organization Ready for the Impending Flood of Data?

Hal Varian, chief economist at Google and emeritus professor at UC Berkeley, has been with Google for more than a decade and has unique insight into the past and future of data analytics. In a conversation with MIT Sloan Management Review guest editor Sam Ransbotham, Varian says that companies need to beef up their systems to function within an overwhelming data flow — including new voice-command system data and other computer-mediated transactions.

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Analytics Meets Mother Goose

Businesses are running into the issue of having analytics professionals who can’t communicate what they mean. Companies need to train their data scientists to explain how their work helps the business. A little communications 101 is in order, says Meta Brown, whose business has shifted from helping companies analyze data to helping them understand what their analysts are doing.

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Catching Up with Scantily Clad Analytics Emperors

If you’re lying awake at night fretting that your competition has mastered analytics when you haven’t, take a breath — many of the stories we hear about analytics success are likely skewed. The transition to analytics-focused business is still far, far from universal, and that, says information systems expert Sam Ransbotham, means you have a chance to catch up.

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Short on Analytics Talent? Seven Tips to Help Your Company Thrive

Companies are having a tough time finding the data scientists they need — they just aren’t being trained fast enough to meet market demand. While it may be challenging to keep ambitious analytics projects in development without employees with the necessary skill sets, that doesn’t mean those projects need to halt altogether. Sam Ransbotham offers seven tips for making progress when you don’t have enough analytics talent on board.

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Online “Chatter Data” is Big Data Gold

Brands are extremely interested in finding out what people are talking about on Twitter and Facebook — what’s known in the industry as “chatter data.” This is a kind of real-time knowledge that Facebook, for one, has the ability to capture — and share. Blake Chandlee, vice president of global partnerships at Facebook, says that Facebook is working with brands such as Procter & Gamble and Unilever to help them understand their consumers through this detailed social data.

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Coming Soon: Doctors As Data Analysts

At the Big Data Innovation Summit, Kaiser’s John Mattison detailed his expectations for the future of health care. He envisions a data-driven system that relies on genetic data in combination with personal data from the patient regarding exposures and lifestyle to help physicians predict health risks. But he also warned that companies have a great deal of work to do to meet the challenges of health care’s digital transformation.

Image courtesy of Flickr user Frank Hebbert https://www.flickr.com/photos/f-r-a-n-k/244365325
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When an IT Project "Goes Red"

Declaring that a project everyone is excited about is in trouble can be demoralizing. But it’s exactly what can turn things around. That’s what health care insurer WellPoint found when it ran into trouble changing its provider payment system and put the project into “Status Red.” Sending the warning message up the organization ended up having a positive effect, even if team morale initially took a hit. Four steps in particular helped set a better course.

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Data Analytics Makes the Transition From Novelty to Commodity

Business is nearing a tipping point in which the use of data analytics is becoming routinely adopted. While widespread adoption of analytics will mean that it offers less competitive advantage to companies, it also means that the business environment overall will change. Information systems expert Sam Ransbotham identifies four key changes that businesses need to consider now.

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How Facebook is Delivering Personalization on a Whole New Scale

As Facebook becomes more mobile-centric, it’s also becoming adept at laying its customer data over brand data and third-party data to create uniquely customized experiences for its users. In a Q&A, Blake Chandlee, vice president of global partnerships at Facebook, details the power that comes from being able to overlay all that customer information. “Historically, we’ve never had the ability to have the scale of a mass media along with the personalization that digital provides,” says Chandlee.

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