Big Data

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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.


What Email Reveals About Your Organization

  • Research Highlight
  • Read Time: 11 min 

By studying data from email archives and other sources, managers can gain surprising insights about how groups should be organized and about which communications patterns are most successful. Anonymized analysis of internal information communication found that creative people, for instance, work more productively on projects with strong leaders than on collaborations without a clear leader. In addition, in many situations, groups of leaders taking turns worked better at sparking creativity.

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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?


Preparing for Disruptions Through Early Detection

In an adaption from his new book The Power of Resilience, MIT’s Yossi Sheffi explains how companies are learning to more quickly detect unanticipated problems that can interfere with their global operations. Sheffi looks at how leading companies are using an array of detection and response techniques, from sensors to supply chain control towers. These tools are helping companies become more resilient to disruptions such as hurricanes, the discovery of product contamination, and political events.


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.



General Mills Builds Up Big Data to Answer Big Questions

General Mills brought a data scientist into its Consumer Insights group because it wanted to use its existing data more effectively. The company thought it was making decisions based too much on outside data at the expense of what it knew. But figuring out what the company actually knew about its consumers was the challenge facing Wayde Fleener as he came on board. In an interview with MIT SMR’s Michael Fitzgerald, Fleener talks about how he got started in building a Big Data practice within his division.


Analytics in E Major

The Echo Nest, a self-described “music intelligence” company recently acquired by Spotify, uses machine-learning technology to connect people with music. “At our core,” says CEO Jim Lucchese, “what we’re trying to do is what a great deejay does, or the friend that you rely on musically: to better understand who you are as a fan.” In a Q&A, Lucchese describes how the company merges machine learning and cultural analytics to describe music in an analytics-friendly way and help users find new music they’ll enjoy.

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Are Data Scientists Really a Breed Apart?

What differentiates data scientists from other quantitative analysts? It's partly their skill set and partly their mind set. “The recent emergence of the digital enterprise has created a seemingly insatiable management appetite to amass and analyze data,” write Jeanne G. Harris and Vijay Mehrotra. Data scientists are particularly able to make sense of so much information. For instance, 85% said their projects often or always address new problems, compared to 58% of analysts who made that claim.

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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.


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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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?


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.


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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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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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.

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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.

Randy Bean
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Big Data Fatigue?

Some people suggest that the concept of “big data” is nearing the end of its fifteen minutes of fame. They couldn’t be more wrong — because big data isn’t just about managing social media, unstructured data or massive data sets. It is an approach to data and analytics that finds new ways of looking at information — and it’s here to stay.


Preparing Analytics for a Strategic Role

The way health care is billed in the U.S. system is part of the reason costs are so high. WellPoint*, one of the largest providers of health care benefits and insurance in the U.S., is using analytics to change its provider payment system. The goal: promote a health care system based on value, not the volume of services. This Data & Analytics Case Study takes an in-depth look at how WellPoint went from idea to implementation, working with physicians and IT staff to build its Enhanced Personal Health Care program.

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