The ROI of a Modern Data Strategy

February 9th, 2017

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):

  1. The ROIs of all of the projects in your data strategy roadmap.
  2. The additional “speed to value” that the strategy helps you bring to future projects.
  3. The additional value, enabled by the data strategy, to your company’s overall business strategy.

Value of the roadmap projects

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.

Figure 1: Components of roadmap project ROI

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.

  1. Common data and analytics platform: A project where you would design and build a common technology platform to support the data storage, data processing, and analytical reporting needs of your company. This is often the foundation or starting point for many other capabilities.
  2. Micro-targeted marketing: Using analytical techniques to identify niche population groups of potential customers—like VIPs—and design marketing campaigns for them. Even if it’s not explicitly one of your business objectives, you’re probably interested one way or another in better understanding your customers.

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.

Speed to business value

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. Going back to the consolidated data and analytics platform example, the efficiency gains from this project may speed up product development for one of your teams, helping them to release products sooner than was previously possible. Because of this early release, your company now has the opportunity to earn revenue sooner. All of the resulting revenue from the early release measures up as part of the strategy ROI (since the strategy identified the potential for the efficiencies in the first place). Perhaps even more importantly, speeding time to market may have allowed you to capture greater market share or beat a competitor’s offering.

To enable micro-targeted marketing, you could look at a framework for formalizing the development and production of consumer analytics models. Clients who develop micro-targeted marketing can now rapidly deploy and test new models, learning more about their consumers than ever before. If the client sees a change in behavior, they have the opportunity to react more quickly and reap the monetary gains.

Overall impact to business strategy

The final piece of a data strategy’s ROI is its effect on your company’s overall business strategy. The capabilities that result from your strategy roadmap projects will allow your company to perform completely new types of tasks and functions. You’ll be able to develop products, capabilities, or strategic initiatives that were nowhere near feasible in the past.

Going back to the micro-targeted marketing example, if it identifies new groups of customers, you can now create business initiatives that focus on going after those markets and adding additional revenue. New capabilities resulting from your data strategy roadmap might also help you to identify new patterns of customer behavior, or analyze each individual customer’s behavior to create truly personalized marketing campaigns. These are customer classifications you might have never even known existed before. This is vastly different from the past, where you were forced to analyze each customer as part of a larger market segment or demographic.

Simply having more of these new capabilities at your disposal will allow you to tackle more ambitious problems. Previously in the healthcare and biotech spaces, computations to study different drugs or genetics were extremely time consuming and expensive. With recent advances, such companies can tackle tasks that were simply too cost prohibitive before. Technologies like Apache Spark are vastly reducing the time (and thus cost) it takes to complete certain genetic calculations, as mentioned in David Patterson’s talk on using Spark for genetics processing. With advances like this, capabilities and tasks that were nearly impossible before are now attainable realities. A data strategy can help an R&D company identify these opportunities and then build a plan to realize them, resulting in many new opportunities for the client.

How do you identify the ROI from examples like this in your data strategy? Think about the outcome of the new business strategy initiatives. For the initiatives that go after new markets, one way to look at the ROI is the new revenue from those market segments. For the initiatives that create more personalized customer experiences, think of the revenue resulting from the uplift in customer loyalty. For R&D companies, the ROI can be the cost savings in decreased R&D time and decreased product development time and costs.

Conclusion

A data strategy will help you identify the optimal projects for you to tackle that will have the highest return to your business and its overall strategy. Looking at the resulting projects, along with accelerated business value, and effects to the company’s overall business strategy, will give you a comprehensive view on how to think about the specific value a data strategy will bring. Knowing this, you can begin to look at a data strategy not as a project that delivers abstract, unquantifiable results, but as an impetus for identifying key sources of ROI for your business. To learn more about creating a data strategy, download our related position paper.

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