Data-driven companies are those that maximize the potential benefits of their data. The data functions at these companies have efficient processes to turn data into action and actions back into data. Becoming data-driven, and learning to translate data into better decisions, is very difficult.
We’ve had the opportunity to help more than 1,000 companies along their journey toward becoming data-driven, and that process is a journey. There isn’t an international standard to implement, or a decades-refined playbook to follow.
We’ve seen certain similarities among our most data-driven customers. They all develop rigorous decision processes and foster evidence-based cultures. It’s from those similarities that we’ve collected these tips for building a data-driven company:
Tips for building a data-driven company
- Define and examine goals with data. Data is a means, not an end. Have a clear vision of your end goals, how each tactic contributes along the way and the data that will inform each step. Give numbers on dashboards context beyond their literal value, so they can be easily examined to answer questions as necessary.
- Enable everyone at your company to use data daily. While the Analytics team owns the data stack, every employee needs the opportunity to use relevant data in their existing workflows. At the org level, all departments should share the same sources of truth and work toward the same shared metrics.
- Set company direction and strategy using data. The most important decision makers need access to the best possible data. Past strategic decisions should be tracked, tested and re-evaluated in a feedback loop. Leadership must enforce the expectation that data will be used to evaluate every decision and they rely on data to minimize risk.
- Act on predictions of the future. Spend your time using data to make testable, tactical predictions, then take actions based on those predictions and feed the results back into your process to improve future decisions. The data-driven decision engine looks a lot like the scientific method. Historical analysis should be focused on learning why something happened, not just reporting what happened.
- Create analyses with diverse groups of people. Analysts must work tightly with their business counterparts and drive change together. Intentionally include different levels of experience and perspectives throughout the process to reduce the biases that homogenous groups exhibit. This will minimize blind spots and maximize potential solutions.
- Make all decisions with an evidence-based culture. Make sure data is trusted throughout the organization and included in most status-level communications. While “gut feels” might provide initial areas for investigation, decisions should be made based on science. Ready analysis can then support the business by being relevant, informative and actionable.
- Encourage curiosity in your culture. Everyone should be empowered (and expected) to ask a lot of questions and there need to be resources available to provide answers. KPIs should be regularly dissected into their components to provide new perspectives on what the non-curious might see as a simple sum or projection.
- Train everyone at your company to use and interpret data accurately. Recognize that no one is born data literate, and implement classes to teach everyone to interpret and use data properly in their daily jobs. All employees should understand how to determine if they’re using metrics, goals and conclusions correctly.
- Invest in collecting data, even if won’t be immediately useful. Spend the resources to invest in knowledge without knowing if it will pay off. Collect data from wherever it is generated, not just from easily accessible stores. And make fixing data collection errors and outages a high priority.
- Constantly improve your data tech stack. Expect to frequently update or expand your technical footprint. More data from more sources requires more data tooling over time. Concurrently, invest in documentation and unification projects to minimize sprawl.
- Know that you’ll never finish, and embrace that. Understand that your analytics team has more work than it will ever be able to finish, and be rigorous about prioritization. Make sure your organization has a continuous improvement mindset, and is always looking for opportunities to optimize existing processes and capture new data.
There isn’t a definitive end point for becoming data-driven. Just like getting more efficient, being customer-centric and having the “best” product, there is no finish line. But when you undertake this journey to become a data-driven company, the benefits will quickly materialize — faster decisions with greater confidence, a direction based on truth instead of hunches and a culture that puts data at the center of every function.