Every organization says they want to be data-driven. But what does that mean in practice?
“Being data-driven means that the key decisions you make about how your organization runs and how you serve your customers and stakeholders is driven empirically,” says Dmitri Adler, Co-Founder of Data Society. “You’re collecting information about what has happened in the past, what has happened in analogous situations, and you’re using that to draw conclusions about what to do next.”
It sounds simple, but there’s more nuance beneath the surface. Being data-driven doesn’t mean measuring everything. Nor does it mean perfection in your systems. Adler believes the obsession with perfect data can get in the way of progress.
“Being data-driven doesn’t mean that you measure absolutely everything all the time. That’s not utilizing your resources effectively. You want to focus on the things that ultimately drive your performance.”
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The Myth of Measuring Everything
In the race to become data-driven, many organizations fall into the trap of trying to track every metric, every action, every signal. However, data without purpose becomes noise. Worse, it can drain resources that would be better spent elsewhere.
Instead, leaders should focus on identifying key business levers: what has the greatest impact on outcomes? Where are the strategic drivers of success — people, processes, technologies, markets?
Once those drivers are identified, you can work backward to determine which data you need, and how to collect it consistently.
Start Where You Are — Then Build
The path to data maturity doesn’t require a fully staffed data science team from day one. “Paper and pencil is a perfectly great way to start,” Adler says. “It’s a little out of fashion, but if that’s all you’ve got to work with, it’s better than nothing.”
Starting simple gives organizations a chance to:
– Build historical data over time.
– Improve data quality gradually.
– Identify gaps in processes and systems.
Eventually, more sophisticated infrastructure becomes necessary. However, early wins often come from clarifying what matters and committing to tracking it — consistently.
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From Data to Insight to Action
Once a company has a baseline of reliable data, the focus shifts to interpretation — and that’s where context matters. Adler emphasizes the role of metadata (the data about your data) in making information more usable and actionable.
“The more history you have, the more context around those data points you’re able to gather, the better you’re able to utilize that data to turn it into information — and information into decisions.”
This journey from raw data to actionable insight requires:
– Consistently collecting data
– Contextual metadata
– A shared language developed across teams
– A culture of transparency and accountability
Culture Is the Backbone of Data-Driven Organizations
Adler clarifies that being data-driven isn’t just about the tools you have or the systems in place– it’s about the people.
“Being data-driven means making a conscious decision to do that. You set up the systems. You explain the ‘why.’ You create a culture where people know what success looks like and how to measure it.”
Creating a culture of shared understanding separates the companies that are data-driven versus those that aren’t. Without prioritizing the importance of data, even the best dashboards fall flat.
What Being Data-Driven Is Not
To recap, here’s what being data-driven does not mean:
– Measuring everything indiscriminately or inaccurately.
– Expecting instant results without the lift.
– Relying solely on technology to do the job for you.
– Replacing intuition entirely for a quick output.
Instead, it means:
– Making deliberate choices about what to measure.
– Prioritizing what it means to be a data-driven organization.
– Creating feedback loops to learn and adjust to changes.
As Adler says, “It’s a culture of knowing why you’re doing what you’re doing — and being able to measure the outcomes.”
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Q&A: What does it mean to be data driven?
Being data-driven means using empirical evidence to guide key decisions across an organization. It involves collecting and analyzing relevant historical and contextual data to inform how a company operates and serves its stakeholders.
