How to build your data strategy Part 3: measuring return

Innovation should be afforded the freedom to flourish. But try telling management you don’t know what the return on their investments in data are. Investments you’ve been responsible for. At a practical delivery level, measuring return of data projects is a crucial part in ensuring the value of data is understood and taken seriously. It is then no longer a cost vs return conversation, but an adopt vs ignore one too.

Data has a value, yet measuring return on data activities remains a challenge for many. So how do you know when your data strategy is working, delivering value, and meeting the original goals you set? The question is more challenging to answer for programmes where the potential payoff is less clear. And one of the biggest challenges in justifying investment in data projects.

Your sponsors will be keen to know the detail behind the investment. The first step to providing it is to identify specific impacts and change as a result of the data project.

Will this new program add enough value to offset its costs? The answer is clear for programmes that address significant problems with high costs or opportunities with potentially high rewards. Regardless of the situation, this question presents an initial opportunity to ensure your programme remains aligned with the needs of the organisation. Your data projects ultimate payoff will be in the form of profit, cost savings, or cost avoidance.

✓  Effect on revenue: How is my data project affecting the number of customers, spend per customer, etc ...

✓  Effect on costs: Is my data project resulting gin direct or indirect cost savings?

✓  Competitive edge: Are my data projects bringing something to the business that differentiates it from the competition?

✓  Team efficiency: What am I gaining (or saving in costs) by making people more efficient in their jobs?

✓  Speed-to-value: How can you quantify the ability of the data team to deliver more projects faster?

Pinpointing early, one or more business measures already in the system that need to improve as a result of the program, will make measuring change easier.

Analyst Garner found that 75% of ERP projects fail. And consultancy firm McKinsey found that 70% of all digital transformation projects also fail. Why? There are, of course, many contributing reasons, but misalignment with the core businesses objectives is cited as the main one. This is why it is crucial, when building your data strategy, to have it laser focused on business objectives. The positive impact of your data strategy will then influence reaching your business goals. Value your project sponsors will understand and appreciate.

Implementing your data strategy is a continuous journey. A journey with lots of little steps that take you towards the goals you set out to achieve. As part of that journey, define, quantify, and measure results as you go. So, although your data strategy is continuous, having points of measurement keep it pointed towards the business’s objectives.

Monitoring progress also serves another purpose. When you look at your data activities in isolation, you can you say whether each is a success or, though one may not quite work, these are the lessons learnt. Breaking a data strategy into little steps, then focusing on those steps, allows you to capitalise on the successes, and learn from the failures.

And learning is essential. Because your competitors are likely following, or developing, their own data strategy.

There is value in your data. Without question. And it’s the job of your data strategy to chart the journey towards extracting that value. There are many nuances that will dictate what goes into your data strategy: the technology you will use, the people you bring in to support it, the priorities, and so on, but without a strategy, your data projects will be aimless, and, ultimately, valueless.