At Interana, we’ve talked with a lot of companies that are just starting to see how important a data-informed approach is to growth.

One significant commonality we’ve seen is that becoming truly data-informed often requires a significant recalibration of how work gets done.

Being data-informed is a holistic process, and it’s a lot more than collecting more (or better) data. It’s how you collect the data, who collects it, how it’s analyzed and towards what ends. It’s who makes the decisions, what data they use to do it, and who’s involved in those conversations.

Becoming “data-informed” isn’t just about quoting metrics here and there—in some cases, it means totally revamping existing practices around:

  • Decision-making
  • Setting goals and incentives
  • The structure of an organization

To determine how data-informed your company is, and evaluate where you need to improve, we’ve put together a data collection and usage audit.

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Fundamentals

Before we dive into the specifics of how data gets used inside your organization, let’s go through some basic questions to get started.

  • Do you use data to make decisions about your business? Have you experienced the ROI of being data-informed?
  • Which teams are involved in using data to make decisions? None, some, or all of them?
  • Does every team have metrics/KPIs that they regularly track?
  • Does your company have a “North Star” metric?

Further reading

Roles

In organizations that are not data-informed, collecting and using data is often no one’s responsibility—or solely the responsibility of a single person/team.

  • Do people throughout your organization understand the value of data?
  • How is data used to make product decisions, business decisions, marketing decisions, etc.?
  • In what capacity do people use data in their daily lives? Are they active in querying the data or more passive consumers of dashboards?
  • Does data play a role in supporting intuition and hypotheses, and in presenting arguments?

Further reading

Accessibility

Having access to data means more than having the simple capacity to get a spreadsheet open on your screen. It means understanding how to form questions, how to query data, and how people disseminate their learnings across the organization.

  • Do people who want to ask questions about data have access to it?
  • Can they ask these questions and get data insights themselves? Or do they need help from the data science team?
  • Does your analytics stack allow for quick adoption across the company? Or is there a significant learning curve?
  • Is it easy to share data insights across different organizations in the company through emails, dashboards, or other methods?

Further reading

Tooling

The tools that you use to analyze user behavior aren’t just defined by their features—what they can do. They’re defined by how you use them, how you understand what you’re doing and how you interpret your results:

  • Are you using raw data in your analysis or relying on summarizations?
  • What assumptions are the tools you’re using making about your users/data? Are they unimpeachable?
  • How do you define a session? How are you defining an event?
  • Do you know where your anonymous visitors are coming from?
  • Are you logging the time between when a visitor first visits and when they sign up?
  • Are you tracking how browser type, search traffic, and landing page affect the conversion of your visitors?

Further reading

Performance

The two most important qualities of any analytics stack are flexibility and speed. There’s no such thing as a perfect question. That means you’ll often be asking multiple questions in a row as you iterate and figure out exactly what it is you should be asking.

To support this, your analytics solution needs to allow you to ask a flexible variety of questions at speed. You need low latency so you can iterate and refine your way to the perfect query.

  • Can people ask pretty much any type of question about their data? Or are they limited by their tools in the kinds of questions they can ask about their users/product?
  • Can people ask questions and get answers quickly?
  • Can you achieve insights about your product and customers fast enough to stay competitive?

Further reading

What to Do With Your Answers

A data-informed organization should:

  1. Possess the ability for everyone on the team to use data to learn and make decisions.
  2. Have an analytics stack that is fast, flexible, and simple to use (or learn to use).
  3. Be aligned towards a North Star metric and use KPIs to assess their progress toward towards it.
  4. Be clear and transparent, both with their data and the assumptions they’re making around it.

Run through this audit, and assess where your organization falls with respect to these 4 principles.

Where is your team doing well? Where could your team use a little help? Let us know in the comments below, and we might feature you in a future article for the Interana blog!