Data Science

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Why Your Company Needs Data Translators

  • Frontiers

  • Opinion & Analysis
  • Read Time: 8 min 

When it comes to putting data to use, communication — or rather, lack of it — between the data scientists and the executive decision makers can cause problems. The two sides often don’t speak the same language and may differ in their approach to and respect for data-based decisions. Given these challenges, organizations may need to call upon a “data translator” to improve how data is incorporated into decision making processes.

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Why Big Data Isn’t Enough

  • Research Feature
  • Read Time: 13 min 

There is a growing belief that sophisticated algorithms paired with big data will find relationships independent of any preconceived hypotheses. But in businesses that involve scientific research and technological innovation, this approach is misguided and potentially risky, as spurious correlations and “noise” may lead analysts astray.

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Lessons from Becoming a Data-Driven Organization

Organizations across the business spectrum are awakening to the transformative power of data and analytics. They are also coming to grips with the daunting difficulty of the task that lies before them. It’s tough enough for many organizations to catalog and categorize the data at their disposal and devise the rules and processes for using it. It’s even tougher to translate that data into tangible value. But it’s not impossible, and many organizations, in both the private and public sectors, are learning how.

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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.

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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.

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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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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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Are Predictive Analytics Transforming Your Supply Chain?

Some industries like health care and retail are starting to see the transformational potential of big data and predictive analytics, but the cost of hiring skilled employees and the complexity of an extended supply chain network are daunting. New research suggests that the convergence of data science, predictive analytics, and big data have the potential to transform the way supply chains operate.

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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.

Image courtesy of Match.com.

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.

Showing 1-20 of 44