How to leverage your data in an economic downturn

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If data is the new gold, then controlling your organization’s data is invaluable, especially in the face of economic uncertainty. For startups, that time is now. Capital is much more difficult to come by, and founders who were receiving unsolicited term sheets just a few months ago are suddenly investigating how to extend the runway. Growing an audience is also more challenging now, thanks to new data privacy legislation and restrictions from Apple devices. 

So, what’s a founder to do — curl up in the fetal position and lay off half their staff? Slow down. Step away from Twitter. Recessions and downturns leave their battle scars on everyone, but truly spectacular businesses can and do emerge during economic downturns — and your business can be one of them with the right data strategy. 

Your data can be your organization’s superpower. When leveraged properly, data can help go-to-market teams do more with less, like:

  • Customize onboarding and product experiences to increase conversion rates
  • Understand where users are struggling and proactively help
  • Apply sales pressure at the right time, yielding expansion revenue that may have occurred naturally a few months later

But, for many organizations, user data is most frequently siloed within product and engineering teams, locked away from marketing and sales, and not often tied to monetization outcomes. This doesn’t have to be your company. Good hygiene and an efficient, sensible data setup can help your team ensure that data is accessible and available to all who should be using it. 


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Product measurement

One major issue that organizations face when it comes to democratizing data is translating actual product usage to business value. When a user leverages a key feature in your app, that’s good, but if they do it 50 times in their first week, that’s excellent. Simply measuring usage and storing it somewhere dampens the value of these key activities. 

That’s why it’s helpful to have a cross-functional team meeting while setting up your data structures to consider facts and measures.

Defining facts vs. measures

Facts are simple: They’re actions that are taken in your product. For example, feature use, alongside the user’s ID, and an organization’s ID are all facts. Engineers and product managers are usually pretty great at identifying and capturing facts in a data warehouse. 

Measures, on the other hand, are calculations that emerge from the data. Measures can tell the story of the value of the facts that they’re built upon, or can illustrate how important that particular step is in the user’s journey.

An example of a measure can be simple, like a qualifier of a person, i.e., “They selected that they’re looking for a business use case in onboarding” in a column named “business or personal.”

Measures can be more complicated, like a running count of the times a user visited a pricing page, or a threshold of whether or not they’ve activated. 

I always recommend that organizations leave the engineering and tracking of the facts up to the builders of the product–engineering and product, and then put together a team around the measures. The best teams treat measures like a product themselves, with user interviews occurring within support, marketing, and sales as to how those customer-facing and go-to-market teams view and use that data, and a roadmap to create measures that matter. 

Implementing data collection and distribution 

Once your team has mapped out what they want to track, the next key question to ask is “How can we store this?” It feels like every day a new data solution is coming to the market, and less technical audiences and founders might find their head spinning with options to store, ingest, and visualize their data. 

Start with these basics:

  • Data (the facts) lives in a data warehouse 
  • Data is then transformed into measures with an extract, transform, load (ETL) tool, and those measures are also stored in the data warehouse
  • If needed, measures and facts can then be moved into employee-facing tools to democratize them with a reverse ETL tool

Tons of options are on the market for data warehousing, ETL, and reverse ETL to move the data, so I won’t mention vendors here. It’s important to involve not only your engineering team here, but also product teams and the roundtable you’ve set up to productize your measures as well. That way, no one’s missing actionable data in the tools that they use. 

Taking action with your data

The final and most complicated step after storing your facts, and identifying and creating your team’s ideal measures, is making that data available where your team works on a day-to-day basis. This is where I typically see the most fall-off. It’s not easy to get sales, support, and success teams to log into a dashboard and take action with the data every day. It is key to get the data in the tools that they already use. 

This is where data democratization becomes more of an art than a science. Your creativity with what you do with your own data will help you own your organization’s destiny. You need to use reverse ETL to get those measures into a CRM, a customer success platform, or a marketing automation tool, but what you do with it is up to you. You could create dynamic campaigns for accounts that start to find value with the tool, or serve up highly active users to the sales team for direct outreach.

In a downturn, it’s extremely valuable for support and success teams to understand if an account is using your product tool less than usual, or if a key player is no longer at the customer organization. 


  • Look outside of product and engineering to think of critical use cases for your data
  • Bring in players from across the organization when setting up a reporting structure
  • Data democratization dies when data is siloed in a dashboard

We as an industry are fixated on those businesses that do fantastic things with their data, but we don’t speak frequently enough about the underlying structures and frameworks that got them to that point. All of these playbooks are enabled by data, but can only happen when you have proper data hygiene, structures, and are getting information into the hands of the right people at the right time. 

Sam Richard is the VP of growth at OpenView.


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