This site uses cookies to enhance your browsing experience and deliver personalized content. By continuing to use this site, you consent to our use of cookies.
COOKIE POLICY
I’ve spent my entire career helping organizations use data to create better outcomes for customers, employees, and shareholders. Getting an organization to leverage data effectively in its decision-making process successfully requires a sound data strategy. However, having a data strategy by itself is not sufficient. Successful organizations develop a data strategy that is actively supported by the organization.
In this post, I will outline why you need a data strategy and what a robust data strategy should cover. This is the first blog of a three-part series. The next blog will cover how to build your data strategy, and the third blog will explain how to sell it to the most senior executives of your company. Along the way, I will include practical examples you can leverage in crafting and selling your data strategy.
Why you need one is the most important of the questions you could ask about a data strategy. Being able to clearly articulate why you need a data strategy will help you in so many ways. Not only will it help you target the right strategy, but it also enables you to get executive attention. Leading with “why” when you talk to your c-suite helps them understand the reason they should pay attention. If you don’t get their attention upfront, it’s not likely that you’ll influence them to support the data strategy.
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT CISR and more. I chose one from the Melbourne business school and A.T Kearney to share below. They measured the financial lift possible by advancing from each stage of capability. Since most firms are measured on profit, this is a powerful message – but does it directly say you need a data strategy?
It doesn’t, but another study from McKinsey does. In speaking with ~300 firms who indicated that their organizations effectively used data and analytics, they said that the construction of a data strategy was the number one contributing factor to their success. That is slightly ahead of senior management support and well ahead of architecture and talent considerations.
Being good at using data to drive decisions creates a meaningful profit advantage, and those who are leaders indicated that the number one driver of their success was their data strategy. It seems like a compelling case for a data strategy to me.
Now that you understand why a data strategy is important, let’s talk about what one is:
A data strategy is a comprehensive method to employ data and analytics to drive critical business objectives. That is a mouthful. Maybe a better way to say it is a thoughtful approach to monetize data to achieve business goals. Because it needs to cover a wide range of topics to deliver results, your data strategy should address four key concepts:
The first element “Story” represents all your typical strategy elements – vision, goals, objectives, and success measures. It speaks to what you will do and why it is important. “Oversight” refers to the means and methods to build and execute the data strategy. It covers who needs to be involved, how their activities will be prioritized, how you manage risks, as well as how you fund and manage the data strategy. “Transformation” refers to the changes that need to be made to go from current to target state. This includes things like determining architecture, developing roadmaps, and setting up data governance. “Culture” touches on the role people play in the strategy. How will you manage people through the change, provide proper training and communication and how will resources be organized? As you can see, it is a lot. Focusing on only one or two elements will leave a lot of important questions unanswered.
The next blog in this series will dive into how to build your data strategy.
Additional Resources
Reid, SVP of Data and Analytics at UDig, is a long-time data professional with experience at multiple Fortune 500 companies. Most recently, he was the Chief Data and Analytics Officer at Markel. Prior to that he held multiple roles at Capital One including VP of Data Engineering.
