Is your data science team isolated from the rest of the company?

David Dietrich, Advisory Technical Consultant, EMC

October 10, 2014

4 Min Read
(Source: EMC)

Many times companies want to do more with data science and big data, but struggle with how to start. Or once they start, the data science team is a splinter group, and the rest of the organization does not know how to interact with them.

[Big data is about more than just business. Read Data Science That Makes a Difference.]

It's important to get a data science team in place, but that's where the real work begins. There are steps companies can take to make sure that collaboration is alive and well between data science teams and the rest of the company. The following five tips, based on my experience working with a number of data science teams, will help nurture that collaboration.

1. Consider a collaboration platform for analytics. Although collaboration platforms get less attention than modeling tools, there's been progress with tools that allow teams to perform and share advanced analytics. This is particularly helpful for geographically dispersed teams, providing a common workspace where team members can write notes to each other about their analyses, and work together on the project.

A couple of tools to consider in this space would be Alpine Chorus, which lets users interact with the data and execute advanced data mining techniques in PostGreSQL. Another option would be to look into OpenChorus, which is free and available on GitHub for teams to download. 

2. Take a team approach. Data scientists can spend huge amounts of time wrangling data, so sometimes it is better to bring a data engineer on to the team to help with this exact problem. This is someone who may excel at reshaping, manipulating, and loading data sets of various shapes and sizes into data stores for others to analyze. Many times companies think they need an army of data scientists, when what they need is a versatile and balanced team.

3. Consider hackathons. Another way to showcase the skills of the data science team is to hold or compete in Hackathons. This is a way to find important data science challenges and compete on a problem that someone cares about. Hackathons have exploded, and their breadth is staggering. Examples include HackNY, MassTech Transportation Hackathons, and many in the Bay Area. Hackathons allow data science teams to showcase their skills, and also make the group more visible to the company and the general public. Marketing departments are usually happy to promote this kind of activity and showcase the top talent at the organization.

4. Be competitive. Consider having your group form a team and compete on Kaggle and InnoCentive. These challenges range from automating essay scoring for the Electronic Testing Service to classifying the sentiment of sentences from the Rotten Tomatoes data set. This is another great way to help people, showcase skills, and increase the visibility of the team.

5. Work for the public good. There are plenty of good causes that need data scientists to help them. Examples include groups such as Data Kind, which take on challenges to help people in need and seek data scientists to assist them. Other instances may be groups such as EarthWatch and NGOs that are trying to champion a cause but need high-powered analytical help to drive home their points and show others the impact of the work being done.

Of course, the data science team has a day job, too, so they cannot spend all of their time on additional activities. Still, integrating a few of these activities periodically will raise the group's visibility and nudge them to work together as a team on important problems.

Before long, this will attract others within the company who have seen tangible examples of the data science team's work and now grasp the benefit of collaborating with them on their own business problems.

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About the Author(s)

David Dietrich

Advisory Technical Consultant, EMC

David Dietrich is an advisory technical consultant in EMC's Global Education Services organization. His focus areas include big data analytics and data science. Recently, he co-developed EMC's first course in the new data science curriculum. He has filed multiple patents in the areas of data science, data privacy, and cloud computing, and he has been involved with analytics and technology for nearly 20 years.

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