Data Science

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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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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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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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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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Big Data’s Travails Don’t Mean It’s Derailed

Executives are growing dismissive of Big Data’s value. Even the best companies can struggle to get good results from their data. But data isn’t getting smaller, it’s getting much, much larger. Corporate executives should look at what’s emerging from universities like MIT, where researchers are beginning to get answers to longstanding big questions in healthcare, public policy and finance.

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Business Quandary? Use a Competition to Crowdsource Best Answers

Top data scientists often share three characteristics: they are creative, they are curious and they are competitive. Anthony Goldbloom, CEO of Kaggle, a company that hosts data prediction competitions, has figured out how to tap all three of these characteristics to help companies crowdsource their analytics problems.

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Innovating With Analytics

A data and analytics survey conducted by MIT Sloan Management Review in partnership with SAS Institute Inc. found a strong correlation between the value companies say they generate using analytics and the amount of data they use. The creators of the survey identified five levels of analytics sophistication, with those at Level 5 being most sophisticated and innovative. These analytical innovators in Level 5 had several defining traits. This article explores those traits.

Jeanne Ross, director of the MIT Sloan Center for Information Systems Research

Do You Need a Data Dictator?

Some companies have a counting problem when it comes to data. Revenues, customers and leads can be counted the same way by all managers…or not. Director of MIT’s Center for Information System Research discusses the growing interest in data analytics and how one company that was in the red dealt with business unit heads all of whom were reporting profits.

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Finding Value in the Information Explosion

Today’s companies process more than 60 terabytes of information annually, about 1,000 times more than a decade ago. But how well are companies managing the data and capitalizing on the opportunities it presents? To answer these questions, seven IT research centers studied data-related activities at 26 corporations and large nonprofit organizations. The research shows that while the IT unit is competent at storing and protecting data, it cannot make decisions that turn data into business value.

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The Storage and Transfer Challenges of Big Data

A lot of the talk about analytics focuses on its potential to provide huge insights to company managers. But analyst Simon Robinson of 451 Research says that on the more basic level, the global conversation is about big data’s more pedestrian aspects: how do you store it, and how do you transmit it?

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All Fired Up in Massachusetts: The State’s New Wave of Big Data Companies

The state of Massachusetts is a major U.S. center of big data, says Stephen O’Leary, an M&A advisor with Aeris Partners and executive committee member of the Massachusetts Technology Leadership Council. It’s only poised to get hotter.

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Quick Wins Help Avoid Culture Obstacles on the Path to Value

"The biggest predictor of success...has been when there's a strong business sponsor involved," says Randy Bean, co-founder of NewVantage Partners. Broad-based organizational support usually follows when the business sees how analytics will improve the top and bottom line.
K. Ananth Krishnan is chief technology officer of Tata Consultancy Services Ltd.

The “Unstructured Information” Most Businesses Miss Out On

Businesses’ ability to process numbers in “well-behaved rows and columns” goes back 40 years, notes K. Ananth Krishnan, chief technology officer of Tata Consultancy Services, one of the largest companies in India. Figuring out how to mine and process the information in text, video, and audio is the new frontier.

Showing 1-20 of 33