Data-driven insights have the power to transform business strategy and answer your organization’s most critical questions. But without a clear path to building and scaling your data science organization, you might feel stuck on your journey.
If this rings true for you, you’re not alone. At 84.51°, we partner with businesses at all levels of cultural and technical maturity and found these four steps to be key to building a data science organization that delivers sustainable ROI.
Start small: Focus on business areas that are ready
You may be eager to push your data science organization to deliver value quickly across many areas of the business. But to set realistic expectations and create sustainable ROI, the key is to start small. Take the first step in your journey by finding areas where there is excitement and opportunity waiting.
Keep your focus on a few parts of the business that understand the value of data science. Look for areas where clear, data-oriented and measurable questions exist and are ready to be answered. Ideally, you’ll begin with challenges that can yield results relatively quickly – think “quick wins” like pricing and promotions – rather than complex business problems that would take much more time to answer. This will help drive momentum and buy-in for data science as you move to more complex and strategic problems. You’ll also want to make sure there are stakeholders willing to make decisions and drive action based on what is learned.
Build context: Align technical teams to the business they support
Context is key to setting your technical talent up for success. The farther removed your data science team is from the business, the more likely they are to miss valuable insights.
Align data science directly to the area of the business they support by creating cross-functional teams. This structure will help facilitate critical connections between the teams that are closest to the data and the business partners they support.
Keep it simple: Leverage science that delivers quality over complexity
It can be tempting to press your team to start with sophisticated and cutting-edge machine learning approaches – but complex science doesn’t always mean better science, no matter where you are on your journey.
Take time to frame business problems clearly rather than jumping into complex modeling and AI. Data assets that are simple, yet high quality will allow your team to answer critical questions with confidence. The resulting insights will ultimately lead to more effective measurement, strategies and activation. Complexity can be introduced over time to increase precision and open the door to solving new problems.
Drive scale: Build advocacy for the power of data science
Once your data science organization has delivered value by starting small, you can scale that value across the business.
Say for instance, your data science organization has identified the right price points for a certain brand to optimize consumer engagement. You’ve done the analysis, modified the price and measured the ROI. Now, you can drive scale by applying that approach to another brand in the portfolio.
When your organization is comfortable with both framing data questions and confident in acting on the results of analysis, the organizational culture around leveraging data science will grow. You can then expand your data science team and support the flywheel for more data, more science and more insights that drive sustainable ROI to business.
About the Author
As VP, Data Science for 84.51° Insights business, Emily Gibbons leads the data science teams supporting 84.51°’s commercial delivery to clients, including Custom Insights, 84.51° Stratum, and 84.51° Collaborative Cloud. She helps drive continuous innovation of the science in 84.51°’s portfolio to ensure its solutions are market leading, actionable, and valuable to its clients. Emily has a Bachelor’s in Mathematics from the University of Pittsburgh and a Master’s in Applied Statistics from the University of Tennessee.
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