Historically speaking, data and analytics teams have been seen as data providers and report builders. The focus was not to have an actual business impact, but on methodologies, tools and specific approaches.

These days, data teams are embedded more deeply in businesses, and operationalizing data is becoming increasingly common, if not mandatory.

Along with the significance of Data teams, the amounts spent on cloud services are increasing too. With almost infinite computational power at our fingertips, it’s all too easy to lose track of how much is leaving your pockets and how much is coming back from a specific investment.

So how can data teams measure their impact? How can they reduce costs?

In the very first edition of our webinar, Alvin co-founder and CTO Martin Sahlen sat down to talk to Ole Bossdorf, VP of Data at Project A, about measuring the ROI of data teams.

Watch it here.

If you can’t watch the whole thing right now, here’s my handy list of the three key takeaways:

Should data teams measure their ROI?

Cutting costs and justifying expenses is far from being a new discussion in this (or any) space, but should these teams actually measure their ROI?

What's the modern data stack’s impact on cost?

With tools like dbt enabling the entire data team to build models using SQL, it is nearly impossible to govern, so a lot of complexity and duplication is being created that can become very difficult to manage.

Are we moving towards widespread data operationalization?

Since data is becoming more embedded in the products we use every day, the tendency to operationalize it is something that we will continue to see more of in the future.

Let's keep the conversation going?

We’d love to hear how you’re facing the challenge of data-related ROI in your company. Join our Slack community, and let's have a chat!