You finally have top management on board to get started on your company’s data transformation, but now comes the hard part. You know that you need a solid data strategy to guide your efforts, but other managers at your company would rather focus investment on building things, investigating questions in the data, you know—things that have a much more visible ROI. You know that there’s value in having the right strategy before you begin your big data work, but how do you convince the rest of your stakeholders and team on the ROI of beginning with the strategy instead?
The main output of your data strategy will be a project roadmap that shows you how to use technology to meet your business goals. This roadmap identifies the projects that have the biggest impact for your business and likely the biggest return. These projects, and the strategy itself, often also affect not only your technology strategy, but your overall business strategy and any future initiatives as well. When thinking about the specific value of a data strategy, you can start by looking at the following three components (I will walk through examples of each in detail):
A modern data strategy will identify the optimal projects and corresponding implementation order so you can get the fastest ROI. In terms of measuring ROI, one of the best places to start is by looking at decreased costs or increased revenue that result from each project. Figure 1 provides a quick list of some basic examples before we dive into a more detailed look at projects that might be on your roadmap in one form or another.
Let’s look at some examples of project ROI by walking through two common types of data and analytics projects that could appear on a data strategy roadmap. We’ve seen many companies use these initial projects to build out new analytics capabilities or to better understand their customer markets.
Ideally, the new capabilities and processes from these projects will decrease costs or increase revenue. For example, the common platform can reduce duplicate hardware and data across your company, reducing your hardware and storage costs. It might even reduce the number of software licenses your company is using, if you can move to a single platform license.
A common platform will bring you many efficiencies—if all of the data is in the same place, everyone has access to up-to-date data. There’s no wasted time looking at data that’s out of date, and you’ll spend less time searching for data that may be stored in different environments (and merging it later). Think of all the time your employees currently spend integrating data or trying to sift through mounds of data files to find the right data points. It adds up pretty quickly.
What about opportunities for increased revenue? With the analytical outcomes of micro-targeted marketing, you can segment your customers into different groups and then market to each more effectively with new campaigns. The upside of this can be huge, realized through increased sales after the new campaigns are introduced. You may also be able to market high margin products to the right customers more effectively, again helping to increase revenue.
There are some common themes here. For decreased costs, when thinking about ROI, you should look at decreased time or decreased amount of resources required to complete a specific task. For increased revenue, you need to think about increased output (with the same amount of time or resources) and freeing up employees for higher value, higher margin tasks.
The projects in the roadmap aren’t the only piece of the puzzle when it comes to measuring your data strategy’s ROI. With those projects come more capabilities and efficiency gains that will help your teams complete tasks more quickly, bringing you early returns that compound over time.