Here are five criteria for building an effective data science culture in your organization. Do you have these in place?
The implementation of an effective data science culture must begin at the top. The CEO and other key decision makers must fully commit themselves to the cultivation of analytics. Many firms make their first hire a data steward. For a large organization this position can be exclusively strategic. For a small to mid-size company it is probably someone who is hands-on with the data and analytics but can communicate up as well as across the organization AND understands the foundations that have to be put in place to even do effective analytics. Leadership then must support the investment needed to create data warehouses, buy appropriate software and staff for the different roles needed.
At a minimum a data science team must include a data engineer to get the data infrastructure in place, a data analyst to do the reporting and fulfill the myriad of data pulls that is the most common need of the line businesses, and a data scientist who is freed up to do the more significant, bottom-line impacting analytics by the hiring of the data engineer and data analyst. Usually the data scientist is the first hire in order to drive the strategic hiring of the other two, but no data scientist can be successful without data infrastructure. It is possible your existing IT department can fulfill this need, but transitioning someone to it full-time is critical if you are truly committed to implementing an effective data science culture. Without a data analyst, your data scientist will be unable to dig out from under the reporting and simple data tasks required from line management. These reports and tasks are critical for the organization but don’t effectively utilize a true data scientist’s skills.
Shared Vision for Analytics
ElderResearch.com says this vision definition should happen BEFORE your first hire in analytics. If you already have a staff in place and haven’t done this exercise, take a day and have that data science staff and their line leadership discuss, argue, resolve what is your firm’s shared vision for analytics. It has to be a reasonable road map of the types of analytics that can be done with the resources available. The metrics to evaluate the need for and effectiveness of each project must also be agreed to.
Sit your business and analytics teams in the same location. If both teams are working toward the same goals they become even more productive when they can interface easily. “Physical proximity breeds the kind of understanding between teams in which analytics thrives,” encourages dativa.com.
On-Going Education and Learning
Because of the shortage of skilled data scientists and the number of openings, retaining your expert is a critical part of an effective data science culture. Because data science is an emerging expertise, chat rooms, conferences and online training are abundant. Supporting lifelong learning will benefit your employee and your firm. Organizing a meet-up or lunch-and-learn in your organization will give your data scientists a platform to present their work and increase their visibility within the firm. Avoid your data scientist going on the job hunt because they don’t see any career progression or new challenges to solve in your firm.
Ready to advance your data science culture? Contact Smith Hanley Associates’ Data Science and Analytics Recruiter, Nancy Darian at firstname.lastname@example.org.