Next, to help you demonstrate progress, you should set some clear, measurable goals. When you’re first starting out you’ll likely identify goals in a number of key areas… First up, expenditure on data. One of your goals should establish the amount of money you’ll spend on data and where you’ll spend it. Is it spent on one or more data teams? How much are you spending on data products, services, and data sources? What’s being spent on building data feeds and APIs?

Add up all the numbers and then begin to consider how much more efficient and effective your expenditure could be. A possible KPI in this area could be tracking your spend on data as a percentage of profits.

Next, optimization of data resources and roles. You might set a goal to align data resources, skills and experience to the things you’re aiming to deliver - if you’re aligning resources to things that aren’t strategic priorities then this isn’t ideal! Possible KPIs include the number of data resources you have as a percentage of company resources, or your number of shadow data resources as a percentage of total data resources.

A third area you might develop goals is in delivery - you might choose to set a goal to design and deliver a number of different data projects in line with business goals, tracking your progress with KPIs on the number of projects delivered and the number in progress.

Next, value delivered. A goal might identify the potential value that could be delivered to the business through data initiatives, and then the actual value successfully delivered to the business after the event. A KPI you might choose to track is business value delivered by data as a percentage of profit.

The final area you might choose to set a goal is data quality, where you look to identify the quality of your data and plan to improve it. Your data will only provide you with meaningful insight if it’s good quality, so it’s important to establish quality levels, plan to improve those levels and then demonstrate that improvement. Possible KPIs might be data quality percentage for different data sets - like consumer data and employee data.

These are just some of the areas where you might choose to set goals - there are lots of different potential goals out there, some of which might be better for your organization than the ones we’ve discussed, so you should be prepared to adapt as you see fit. Over time, as your data capabilities mature, these goals will change - which is great, it’s a good demonstration of progress! But, if you do lose your way at any stage, just think back to your answer to the very first question we asked: “why are we doing this?”

Measuring Your Efforts