When most people think of Ironclad, their minds don’t typically jump straight to the data analytics side of the business. However, contracts and the way users interact with them is data that’s extremely valuable, making Ironclad a data treasure trove.

We need data to run our business and inform future product decisions, and that’s where our small-but-mighty data analytics team comes in. The analytics team members operate essentially as data treasure hunters.

We gather the right data, do exploratory analysis, create dashboards, forecast key metrics, and make recommendations so that Ironclad’s business moves forward in a data-informed way.

We operate as cross-functional liaisons between Ironclad’s product, customer success, sales ops, marketing, and finance teams to make sure everyone is speaking the same data language and gathering valuable insights from these findings.

So, how do we do it? And what do we recommend other early data teams at startups do to promote and expand a data-centered culture? Let’s dig in.

Ensure data cleanliness and data inspection

First, get a firm understanding of your data.

Start by asking, what data do we have already? Do we have both event data (i.e. tracking data on contract completions, page visits, and collaboration history) and current state data (i.e. the latest state of information pertaining to users, customer accounts, or contracts)? What about data from external systems like Salesforce? What kind of data is flowing from other apps and tools we use?

Then, how do we implement data tracking and storing? To get a better sense of your data, it’s helpful to draw out your company’s data ecosystem. We’ve provided ours below.

Ironclad's data ecosystem

Once we got a better understanding of our own data ecosystem, we can begin asking deeper questions like:

• How complete is our data?
• Is the right information accessible?
• What’s missing from our tracking?
• Are there duplicates in the data?
• Which edge cases does our data not account for?
• Is the data accurate?

After doing your own inspection, talk to others across the company (i.e. product managers, engineers, and business stakeholders) to get their perspective on data quality at your company and begin to get a holistic picture of which data quality items you should prioritize tackling. Then, create a data quality gap list and try to fill them based on the highest return on investment.

Remember that bad data in means bad data out! Implementing good data quality practices early on will make it easier for your startup as it begins to scale rapidly.