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If data is the new gold, controlling your organization’s data is invaluable, especially in the face of economic uncertainty. For startups, that time is now. Capital is much harder to come by, and entrepreneurs who received unsolicited term sheets just a few months ago are suddenly examining how to extend their runway. Growing an audience is also more challenging now, thanks to new privacy laws and restrictions from Apple devices.
So what’s a founder to do – curl up in the fetal position and lay off half the staff? Calm down. Get off Twitter. Recessions and downturns leave battle scars on everyone, but truly spectacular businesses can 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 properly harnessed, data can help do more with less, for example:
- Customize onboarding and product experiences to increase conversion rates
- Understand where users are struggling and help proactively
- Use sales pressure at the right time, and provide expansion revenue that may have occurred naturally a few months later
But for many organizations, user data is often hidden away in product and engineering teams, locked away from marketing and sales, and not often tied to monetization results. This does not have to be your company. Good hygiene and an efficient, sensible data setup can help your team ensure that data is available and accessible to everyone who should use it.
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A major issue organizations face when it comes to democratizing data is translating actual product usage into business value. When a user uses a key feature of your app, that’s great, 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.
Therefore, it is useful to have a cross-functional team meeting while setting up your data structures to assess facts and measures.
Defining facts vs. measures
The facts are simple: There are actions performed in your product. For example, feature usage, along with the user’s ID, and an organization’s ID are all facts. Engineers and product managers are usually pretty good at identifying and capturing facts in a data warehouse.
Measures, on the other hand, are calculations that emerge from the data. Actions can tell the story of the value of the facts they are built on, or can illustrate how important that particular step is in the user’s journey.
An example of a goal could be simple, like a qualifier for a person, ie “They selected that they are looking for a business use case when onboarding” in a column called “business or personal.”
Measures can be more complicated, such as a running count of the times a user visited a price page, or a threshold for whether they have activated or not.
I always recommend that organizations leave the engineering and tracking of facts up to the builders of the product – engineering and product, and then assemble a team around the initiatives. The best teams treat measures as a product themselves, with user interviews in support, marketing and sales about how the customer-facing and go-to-market teams view and use this data, and a roadmap for creating measures that matter.
Implement data collection and distribution
Once your team has mapped out what they want to track, the next key question is “How can we store this?” It feels like every day a new data solution hits the market, and less technical audiences and entrepreneurs can find themselves spinning around with options for storing, ingesting and visualizing their data.
Start with these basics:
- Data (facts) live in a data warehouse
- Data is then transformed into targets with an extract, transform, load (ETL) tool, and these targets are also stored in the data warehouse
- If necessary, measures and facts can 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 name vendors here. It’s important to involve not only your engineering team here, but also the product team and the roundtable you’ve set up to productize your initiatives as well. That way, no one is missing actionable data in the tools they use.
Take action with your data
The final and most complicated step after storing the facts and identifying and creating your team’s ideal goals is to make that data available where your team works on a daily basis. This is where I usually see the most fallout. It’s not easy to get sales, support and success teams to log into a dashboard and take action on the data every day. Getting the data into the tools they already use is key.
This is where data democratization becomes more of an art than a science. Your creativity in what you do with your own data will help you own your organization’s destiny. You need to use reverse ETL to get these measures into a CRM, customer success platform or marketing automation tool, but what you do with it is up to you. You can create dynamic campaigns for accounts that are starting to find value with the tool, or serve highly active users to your sales team for direct contact.
During a downturn, it is 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 in the customer organization.
- Look beyond product and engineering to think about critical use cases for your data
- Include players from across the organization when setting up a reporting structure
- Data democratization dies when data is siled in a dashboard
We as an industry are fixated on those businesses that are doing amazing things with their data, but we don’t talk often 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 the right data hygiene, structures and get information into the hands of the right people at the right time.
Sam Richard is VP of growth at OpenView.
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