Data & Analytics

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If You Think Big Data’s Challenges Are Tough Now

Although workers and consumers generate two-thirds of all new data, that’s about to change. Sensors and smart devices — from traffic lights and grocery store scanners to hospital equipment and industrial sensors — are beginning to generate an enormous wave of data that will increase the digital universe ten-fold by 2020. Guest blogger Randy Bean, CEO of NewVantage Partners, explains what this means for executives trying to adapt to a rapidly changing, data-centered business environment.

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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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Gone Fishing — For Data

When you’re dealing with data on the massive scale that a company like GE uses, a data warehouse just isn’t big enough to house it all. And organizing it ahead of analysis is more of a burden than a help. GE’s CIO Vince Campisi explains to MIT Sloan Management Review why his company is now storing data in a data lake — and how that approach changes the kind of human resources his company is looking for.

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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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Using Simulated Experience to Make Sense of Big Data

As data analyses get more complex, how can companies best communicate results to ensure that decision makers have a proper grasp of the data’s implications? Research has found that letting decision makers gain experience on the outcomes of different possible actions by interacting with simulations helps those executives make better decisions. Simulations narrow the often a large gap between what analysts want to share and what decision makers understand, and more clearly illustrate complex statistical information.

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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.

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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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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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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 Tristan Martin (https://www.flickr.com/photos/mukumbura/4043364183)
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Why the Non-Superstar Might Be the Most Important Team Member

Hot shots get all the attention, but other team members can be the ones who make a group really tick. “Plus/minus” analysis, which is used by some professional sports teams, lets organizations understand, through data, not just individual performance but performance in context. Research by Thomas H. Davenport details how the goal is to understand how a team performs when one person is part of the mix, and when they’re not.

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Analytical Value From Data That Cries Wolf

It’s a common assumption: errors and biases in a data set mean the data is useless. Not so fast, says Data & Analytics expert Sam Ransbotham — even data with less-than-great accuracy has its uses, if you understand how to parse it. His blog post explains how to make sense of uncertainty, and how tradeoffs between accuracy and breadth in a data set can better inform your decision-making process.

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Training the Next Generation of Business Analytics Professionals

When Michael Rappa proposed to his employer, North Carolina State University, that they create a degree program for business analytics in 1999, they dismissed the idea. But 14 years later, the Institute for Advanced Analytics is a pioneering and successful program that trains analytics professionals for businesses hungry for analytics skills. Rappa sat down with MIT Sloan Management Review’s Michael Fitzgerald to explain how the Institute came about — and where it’s headed.

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Getting Value From Your Data Scientists

Data scientists differ from other types of analysts in significant respects. To create real business value, top management must learn how to manage these “numbers people” effectively. To help executives avoid repeating some of the mistakes that have undermined the success of previous generations of analytical talent, the authors offer up seven recommendations for providing useful leadership and direction.

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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.

Showing 21-40 of 164