How hard do you find it to prove the value of your data projects? Those of us who work in data and insight are numbers driven. Quantifying, classifying, and providing ‘proof’ is our calling and purpose. So, it’s one of the great ironies of our profession that we often struggle with proving what we do.

I’m not talking about the quantity of the work we do. Given enough time, it would be easy to provide a number on the amount of code written or analysis run. I’m talking about the impact our work has on organisations. The real-world change we enable.

Some data & insight teams rely on stats on user activity. For example, queries run on their tables, or the number of users accessing a dashboard. These are somewhat useful metrics, helping provide context to the impact we are having. But they don’t tell us anything on their own if data, analytics, and insight are only valuable when they help create change. And that change is most often made because of the .

Don’t take our word for it. Locally Optimistic, a popular Slack community full of the brightest minds in data, have discussed this at length. Take these two comments during one of their recent webinars:

“Knowing the ROI of analytics is hard because it’s usually a third order affect. It is only realised through someone else.”

“The ROI story for a data team comes down to the business saying these guys are great and I’m getting a lot of leverage.”

In a nutshell, what is being said here is this: the value of any data team is largely defined by its stakeholder’s opinion. How do you get that input? Well, you must ask for it yourself. Here are 3 tips to make the process easier.

How to prove the value of your data projects in 3 easy steps

1. Collect and record the stakeholders’ view on the quality and utility of every piece of work

Just because a model is perfect, doesn’t mean it’s useful. Therefore, we recommend that you ask your stakeholders if they think your work was of high quality. Did it answer the question? Was it delivered on time? Was the insight presented clearly? And most importantly, was it of high utility? Did the work give you the desired outcome? Did something useful happen? Better yet, implement a data project management tool that automates this step for you.

2. Make data project follow-up a part of the standardised process

Our research, ‘Better Insights Survey 2020’, shows that many data teams don’t systemise the process of project follow-up. This is a mistake. Knowing how you’re helping provide value over time and for which areas of the business is essential. It helps you learn and improve. Make sure you are doing this consistently by making it part of the agreed process. By doing so, you’ll transform your data culture.

3. Limit the amount of work which doesn’t link to a desired action, decision, or outcome

If you can’t associate a piece of work with its ultimate purpose, then why are you doing it? Knowing if you’ve enabled a business outcome, gives you a simple measure of the value you’ve created and how to provide more value next time.

Some projects are easy to quantify the impact of. For instance, marketing and sales often have measurable KPIs which can and should link to your work. But not all work is easily associated with measurable outcomes. The best way to prove the value data teams provide organisations with, is to implement a standardised system to do that for us.

These steps not only help you prove the value of your data projects, but potentially improve your data team’s job satisfaction! We’ve recently released our latest report on Data & Insight job satisfaction and its impact on data projects. Some of our findings are presented in this article on 5 things that really matter to data professionals. A full report can be downloaded here: https://brijj.io/emotional-intelligence-report-2022