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What’s happening this week at the intersection of management and technology: IoT implementation lessons; IBM’s AI primer for the White House; forecasting requires augmented intelligence.
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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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
What can companies do to help fill their data scientist gap? That was the topic at a conference hosted by the MIT Center for Digital Business.
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.
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.
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.
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.
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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