Analytics & Organizational Culture

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Beyond the Hype: The Hard Work Behind Analytics Success

The 2016 Data & Analytics Report by MIT Sloan Management Review and SAS finds that analytics is now a mainstream idea, but not a mainstream practice. Few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics. Organizations achieving the greatest benefits from analytics ensure the right data is being captured, and blend information and experience in making decisions.

Cognitive Technologies: The Next Step Up for Data and Analytics

This free on-demand webinar offers context for understanding cognitive technology offerings. It focuses on what technology capabilities will be available — and what tasks will still require human input. Topics include artificial intelligence, automation, and business rules for making cognitive technology functional. Presenters Thomas H. Davenport and Julia Kirby are co-authors of the forthcoming book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.

What the Internet of Things Could Mean to Consumers

From wearables to hotel desks that remind us to move around, connected objects are becoming a bigger part of consumers’ lives. For instance, famed design firm IDEO is using people-centered design to envision the Internet of Consumer Things, including helping to create a headband that lets people measure their brain activity and track their mental focus. In the hospitality industry, smart locks, smart lights, and even desks that suggest changing your posture could all become routine.

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.

Hype vs. Reality: A Reality Check on the Internet of Things

  • Blog
  • Read Time: 2 min 

The Internet of Things has plenty of hype — it’s going to be big, really big — but also plenty of detractors. The naysayers breathily predict everything from the surveillance state to a wrecked economy to people enslaved by machines. Here are nine bits of information to consider, from the way the Internet of Things will create (yet another) battle for control of the Internet, to the fact that the security of the Internet of Things is under fire before it even exists.

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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.

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.

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.

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.

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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 supply chain.

On the Care and Feeding of Your Analytics Talent

A panel of experts discusses the challenges of finding, engaging and organizing data scientists for best results. They talk about how to support your data scientists and keep them engaged in the right kinds of tasks and how to integrate new talent into your existing data and analytics team. They also talk about the skills and traits to look for when recruiting and selecting your data/analytics team, and how to assess existing internal talent for data roles.

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.

The New World of Work

Advanced digital technologies are swiftly changing the kinds of skills that jobs require. Researchers Frank MacCrory, George Westerman and Erik Brynjolfsson from the MIT Sloan School of Management and Yousef Alhammadi of the Masdar Institute studied the changes in skill requirements over the 2006-2014 time period. While demand has clearly grown for computer skills, it has grown for interpersonal skills, too. The authors advise people in all lines of work to be flexible about acquiring new talents.

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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Sports Analytics: The NFL Connects with Fans

In a conversation with MIT Sloan Management Review, Michelle McKenna-Doyle, the NFL’s senior vice president and first-ever CIO, discusses the organization’s customer-focused approach to big data and analytics. She explains how the NFL works to make its employees comfortable with their own data sets.

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.

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.

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.

At Amadeus, Finding Data Science Talent Is Just the Beginning

Everyone wants to hire skilled data scientists — especially Spain’s Amadeus, a travel sector technology company. Amadeus has brought more than forty new hires into this post since 2013. But locating talent is just the beginning. In an interview with MIT Sloan Management Review, Amadeus’s Denis Arnaud describes the steps he takes to not only identify data science talent, but to make sure they integrate well into the company, too.

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