What Being a Data-Driven Organization Really Means

It's one thing to claim to be a data-driven organization — as nearly one-quarter of companies say they are today. It's quite another to develop the technical and cultural resources that actually enable data-driven decision-making.

Indeed, simply collecting or analyzing data doesn't necessarily mean your company is data-driven. You need to go further if you truly want to integrate data-based decision-making into all elements of your business.

To prove the point, allow me to unpack what being a data-driven organization really entails, and which practices businesses must implement to deserve the data-driven badge.

Defining Data-Driven Organizations

Most folks define data-driven organizations as any business that makes decisions by analyzing data.

I prefer an alternative definition. To me, a data-driven organization is one that doesn't make decisions based on hunches, assumptions, gut feelings, or intuition. Instead, it relies on data collected from disparate sources, which it then analyzes to make informed decisions.

I think this definition more accurately captures what being data-driven truly means because it emphasizes the importance of baking data and analytics into all core decision-making processes, across all facets of a business. Simply collecting some data and using it to make some decisions isn't enough to call yourself data-driven. Instead, data-driven decision-making needs to be a systematic practice that extends to all parts of the business.

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The Five Attributes of a Data-Driven Organization

How do you actually extend data-based decision-making across the business? The answer hinges on implementing each of the following five capabilities:

1. Collecting all relevant data

For starters, being data-driven means having the capability to collect all relevant data from all sources.

This is important because making the best decisions often requires looking for patterns from across disparate data sets. For example, if you want to understand how to increase revenue, you might want to look at sales data, customer data, and finance data at the same time. If you look at these data sets separately, you miss important correlations and deprive yourself of optimal decision-making capabilities.

2. Seamless data integration

Collecting data from disparate sources is only the first step in being data-driven. Businesses must also be able to integrate all their data. Integration means merging data from diverse origins into a single location.

Integration is important because it facilitates holistic analysis. When you integrate data, you can more easily identify trends that run across disparate data sources. This is harder to do if you have to analyze each data source separately due to your inability to integrate.

3. Effective data transformation

Sometimes, some or all the data you want to analyze originates in a form that is hard to work with. It might be stored in a database that your analytics tools don't support, for example, or you may have multiple data sets that are each structured in different ways, making it challenging to compare them effectively.

Data transformation, or the process of changing data from one form to another, solves these challenges. When you can readily transform data as needed, you can more effectively analyze it and make informed decisions based on it.

4. Real-time data analytics

Periodic analysis of data is better than not analyzing data at all. But it's not enough to enable a truly data-driven organization.

To get to that point, you need to be able to stream data and analyze it in real time. This allows you to identify and react to the latest insights available from your data. If you make decisions based on reports that were generated a week, a month, or a year ago, you might be missing out on critical information.

5. Data governance and security

When you collect, integrate, transform, and analyze large amounts of data in real time, ensuring that the data remains secure is critical. You need to make sure that the right people have access to the right data, and that the wrong people can't view data that shouldn't be accessible to them.

This is where data governance and security come in. These processes allow businesses to establish controls over how data is managed and made accessible to different users and groups.

While it's technically possible to make data-driven decisions without proper governance and security controls, doing so turns data into a liability as much as an asset. But when you can effectively govern and secure your data, you can use it to make great decisions without introducing undue risks into the process.

Becoming Data-Driven With a Modern Data Stack

If implementing processes like the ones I've just described sounds tough, the good news is that it's much easier than it once was. Thanks to the modern data stack — meaning the sets of tools organizations use to collect, integrate, transform, analyze, and govern data — establishing the practices that drive data-driven decisions across the business doesn't have to be deeply challenging. You no longer have to be a company like Amazon, capable of building your own bespoke data solutions from the ground up, to put the data-driven vision into practice.

This is not to say that being data-driven is as easy as turning on some tools, of course. You still need to deploy a data stack customized for your business and ensure that it's capable of scaling to support the ever-increasing volume of data following into the typical organization. But doing these things is well within the reach of most companies today.

And that's great news, because it means that with a little help from the modern data stack, every organization can become a data-driven organization actually worthy of the title. Instead of just talking about data and analytics, or using them sporadically, they can transform data-driven processes into the foundation of all of their operations.

Chris Resch is the Chief Revenue Officer at Indicium.