5 Ways IT Can Create a Data Science Strategy

Why IT needs to partner with the business and prepare for a lot more data.

Live from Gartner Symposium ITxpoCognitive computing and deep learning are terms that IT leaders are increasingly exposed to when it comes to analytics and big data. But with all the hype, it can be difficult to decide on a course of action. However, it’s important that IT leaders sort through the noise and use emerging technology to come up with a plan.

“Data science is where a lot of the innovation that is driving the digital disruption is coming from,” said Jim Hare, research vice president, during Gartner Symposium/ITxpo 2017 in Barcelona, Spain. “You as IT leaders need a data science strategy. I guarantee if you don’t have one, your executives at the board level are looking for IT leadership to say ‘What is our strategy?’ and the business is probably already doing something around data science, with or without IT involvement.”

Big data, and the emerging tech surrounding it, is an area that IT leaders need to think about to operationalize data science in the organization. These five actions can help.

Jim Hare, research vice president, during Gartner Symposium/ITxpo 2017 in Barcelona, Spain.
Jim Hare, research vice president, during Gartner Symposium/ITxpo 2017 in Barcelona, Spain.
  1. Partner with business to identify where data science can help
    Engage with the line of business to look at your organization’s industry vertical and problems that will affect the enterprise. Have a conversation about specific challenges that business unit is having that could be addressed by data and analytics. Data science should not just be an IT initiative, but rather a partnership with the business. The partnership enables IT to offer technical expertise, while the business unit offers domain expertise.  
  2. Help data science lab operationalize machine learning
    Data scientists don’t really know how to deploy or manage models into production. IT leaders need to provide the DevOps mentality to help data scientists move ideas into production and help to scale in terms of how they build and monitor the models.

    2021 Top Priorities for Data and Analytics Leaders

    Emerging trends, expected challenges and next steps for data and analytics leaders in 2021

    Download eBook
  3. Plan for storing and managing more data
    IT leaders should be planning for storing increasing amounts of data. For data to be valuable it must be high quality, which means it will require a lot of storage. Technologies such as deep learning require that high-quality data, and IT leaders will be responsible for managing and storing it, along with making the correct data available to the right party.
  4. Equip teams with the correct tools and infrastructure
    Data scientists are experimenting with many open source and cloud technologies. Make sure the team has the right platforms, tools and infrastructure to be successful. Explore with cloud, but when it comes to products, ensure you have a platform designed to scale and grow with the enterprise.
  5. Invest in your people
    Data science is a team sport. Data scientists can’t do it all, so it’s important to have data engineers to sift data and IT architects who can help with models. As an IT leader, your job is to support the data engineers, architects and team, as well as ensuring the business unit is involved.

Gartner clients can learn more in the full report, “Hype Cycle for Data Science and Machine Learning, 2017” by Jim Hare, et al.

Get Smarter

Follow #Gartner

Attend a Gartner event

Explore Gartner Conferences

Gartner IT Roadmap for Cybersecurity: A Resilient Strategy

Gartner IT roadmap for cybersecurity based on unbiased research and...

Learn More


Get actionable advice in 60 minutes from the world's most respected experts. Keep pace with the latest issues that impact business.

Start Watching