Building a Data-First Culture: Beyond Data Collection

Originally published at Building a Data-First Culture: Beyond Data Collection.

Last week, I talked to an old colleague who proudly explained that their company had collected over ten years of customer interaction data. When I asked what insights they'd gained from this information, he was silent for a minute and said, "I'm not real sure, honestly." He had the data but wasn't using it to make better decisions or improve his business.

This situation exists in organizations everywhere, highlighting the challenge of building a data-first culture. Data collection has become easier and cheaper, leading companies to accumulate tons of data. The original intent is usually good – understanding customers better, improving operations, or predicting market trends. But somewhere between collection and action, things break down.

Understanding the Accumulation Problem

Data collection serves essential business purposes. Organizations need customer information to provide better service, operational data to improve efficiency, and market data to stay competitive. These are legitimate, valuable reasons to gather information.

Yet many organizations cross a critical line. They move from purposeful collection to unconscious accumulation. Instead of gathering specific data to answer particular questions, they cast ever-wider nets, creating vast information repositories without clear purpose or direction.

I recently worked with a healthcare provider who had spent years collecting patient feedback through surveys, comment cards, and online reviews. They had thousands of responses but had never systematically analyzed them to improve patient care. The data existed in various departments, each collecting and storing their portion without coordination or shared purpose.

What good does it do to collect this type of data if you don't convert it to information and knowledge (and do something with it)?

The True Meaning of Data Intelligence

Data intelligence isn't about having more information—it's about asking better questions and using the answers to drive meaningful change. A friend who is head of data at a retail chain discovered this when they stopped asking, "What else can we collect?" and started asking, "What decisions could we make better?"

They began with a simple question: Why do customers choose certain products over others? Instead of launching new surveys or collecting more data, they analyzed their existing purchase records, combining them with customer service interactions and return patterns. This focused approach revealed clear patterns in customer preferences, leading to concrete changes in their product mix and store layouts.

The difference between this approach and data hoarding is its purpose. Every piece of information they analyzed connected directly to decisions they needed to make. When they needed additional data, they collected it with specific questions in mind rather than casting a wide net hoping to catch something useful.

Creating Cultural Change

The hardest part of building data intelligence isn't technical – it's changing how people think about and use information. A manufacturing company I worked with learned this lesson the expensive way. They invested heavily in data infrastructure and analytics tools but couldn't get their teams to change their decision-making processes.

The breakthrough came when they stopped treating data as a technical project and started approaching it as a way of thinking. They began by identifying key decisions made throughout the organization – from production scheduling to maintenance timing to inventory management. For each decision, they asked what information would help make it better.

This approach created natural connections between data and daily work. When a maintenance supervisor saw how equipment sensor data could help prevent breakdowns, she became an advocate for data-driven decision-making. When production managers understood how customer order patterns could improve scheduling, they embraced the new approach.

Building Your Organization's Data Intelligence

The path to data intelligence starts with an honest assessment of your current state. Look at the last ten significant decisions in your organization. How many relied on data analysis? How many were based primarily on intuition or past practice? This assessment often reveals the gap between having data and using it effectively.

Next, decisions that could benefit from better information use should be identified. Don't try to transform everything at once. Pick specific areas where better data use could make a clear difference. That friend I talked about at the start of this post? He's now started using interaction data to identify common issues and improve response times for their customer service team. This focused approach delivered visible results, creating momentum for broader changes.

The key is connecting data to decisions. When people see how information can help them work better, resistance to change diminishes. A hospital system demonstrated this by showing nurses how patient data analysis could help predict and prevent complications. The nurses enthusiastically supported data collection because they saw how it directly improved patient care.

Moving Forward

Creating a data-intelligent culture requires sustained effort and transparent leadership. It means changing how you collect and analyze information and how your organization thinks about and uses data in daily operations.

Start by examining your current data collection processes and data stores. For each data set, ask what decisions it helps inform. If you can't identify specific ways the information improves decision-making, question whether you should continue collecting it. This audit often reveals opportunities to collect less data but use it more effectively.

The goal isn't to eliminate data collection but to make it purposeful. Every piece of information should connect to specific decisions or insights that drive your business forward. This focused approach often leads to collecting different data, not just less of it.

Remember, the measure of success isn't the size of your data warehouse (or data lake or whatever it's called these days)—it's the quality of your decisions. Organizations that understand this difference transform data from a resource they hoard into a tool they use to drive success.

How will you start transforming your organization's approach to data? The answer to this question might determine whether your data becomes a valuable asset or another underutilized resource.