Data is a powerful tool for companies, as it provides information for businesses to work with. However, many companies do not make the best out of the available data, because they don’t use it the right way.

Data collection and analysis have changed rapidly over the past decades. Technology is advancing quickly and supercomputers can now take in huge amounts of data and draw conclusions within seconds. Yet, experts argue that truly efficient use of data still requires humans to think instead of simply relying on machines.

Data-driven versus Data-informed – What's Best for You?

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This guide will look at the difference between being ‘data-driven’ and being ‘data-informed’. Should you fully rely on data or better use it as one factor that guides your decisions? We’ll explore the advantages and the limitations of these two approaches and discuss which one is better to support your organization’s decision-making process.


Before we take a more detailed look at data and the best practices for using it, we should first understand why we are using data at all. Does collecting huge amounts of data matter and in which way does it matter?

Raw data provides businesses with information, which can be processed in different manners. The main goal is to use these information to make decisions. Therefore, data collection is part of your daily business – every company gathers and uses some sort of data.

Analysis of raw data can provide many insights into your business. While a single piece of information in isolation might not contain much information, entire sets of data can provide ground for decision-making. If your business operates in the retail sector, for example, daily sales data can provide trends and pattern. You can find out which are the busiest sales days or which items sell the most.

This information can be used to improve your business operations. You are able to hire more staff for the busier days and therefore boost sales further. You can optimize your sales pipeline by finding out which products sell the best. Hence, data can make your business run smoother and more efficiently, and save costs.

In 2010, researchers at the University of Texas found that businesses, which improved their data quality and usability generated larger financial returns. According to the study, enhancing data usability by only 10% can increase annual sales per employee by more than 14%.

The rise of big data – why is it important?

‘Big data’ has been one of the buzzwords over the last years. The term refers to data sets, which are extremely large and complex. Due to their size and complexity, these data sets can only be analyzed with computerized systems.

Big data has revolutionized the importance of data, as businesses can now use more data and draw more conclusions. Big data is especially useful in analyzing human behavior and interactions.

To understand more about the importance of big data, watch the following TedTalk video with Kenneth Cukier:


Companies use data in different ways. While some corporations are ‘data-driven,’ others are ‘data-informed.’ Read on, and understand the difference.

Data-driven – data leads the decision-making process

In its essence, data-driven means that data leads the decision-making process. The data thus plays an crucial role in the company, as decision-makers rely mostly on the data.

Organizations, which are data-driven implement the use of data throughout the entire organization. Data analysis and use is not only the job of IT or data specialists, but all departments of the organization use and analyze data.

Jim Giles, the author of the Economist Intelligent Unit Report called ‘Fostering a Data-Driven Culture’, said in an interview that data-driven businesses are “placing data at the heart of almost all important decisions”. “They [the companies] are tolerant of questioning – even dissent – about business decisions being made, as long as the questioning is based on data and their analysis,” Giles continued.

The importance of data becomes increasingly evident when companies grow in size. Data-driven companies, such as the British supermarket chain Tesco or its American counterpart Walmart, achieved business successes with this method.

Data-informed – putting the data in context

On the contrary, ‘data-informed’ organizations don’t rely on data in all of their decision-making. The companies use a more agile and responsive way of treating data. Therefore, data sets are seen in their specific context. Data is used to create a hypothesis, which remains to be proven.

Today’s start-ups and organizations often view themselves as being data-informed instead of data-driven. However, the definitions are not set in stone and sometimes it’s not that clear to tell if an organization as a whole is data-driven or data-informed.


The benefits – less human bias, less time-consuming

Studies have highlighted that a data-driven culture can improve the financial performance of an organization. The Economist Intelligence Unit’s survey in 2012 showed that top performing companies often use a data-driven approach.

Furthermore, since data-driven cultures allow data to lead the way, the decision-making is less influenced by the human element. The data is used as it is, without much further analysis. Data are considered as concrete facts. That removes the human bias that may come with data analysis.

MIT Center for Digital Business studied data-driven decision management and found organizations using this approach had 4% higher productivity rates, together with 6% higher profits.

Moreover, as companies spend less time analyzing their data, there is more time to deal with other operational aspects of running a business. Since gathering data and allowing it to come to its own conclusions won’t require much effort on your part, the focus can be directed to the day-to-day operations.

The risks – huge amounts of data needed, gathered data can be wrong or biased

A data-driven approach requires the organization to collect vast amounts of data to ensure decisions are as effective and accurate as possible. Otherwise, outliers will bias the decision-making process. This can be an obstacle for many companies, especially for younger organizations, that might not have the capabilities or the resources to collect huge amounts of data.

On the other hand, the more data you collect the harder it is to process it. The organization might end up in a situation where it has a wealth of data, but is unable to use it to generate answers.

Finally, relying on data can increase the chances of making the wrong decisions. Data itself can contain a bias. For example, the way how data is gathered can lead to a bias, which could distort the ultimate decision.


The data-informed approach to data has increased in significance in the past few years, with many experts claiming it to be the best way to handle large data sets.

The benefits – company puts data in its context to consider the whole picture

Data-informed organizations understand the limitations of data. Since the collected data is only a snapshot of the reality, decision-making shouldn’t rely solely on this data. Therefore, instead of simple focusing on the data, you have to test and question it in order to draw conclusions.

You could compare the approach to other automated structures. For example, flying an airplane in today’s world is highly automated. But the importance of the pilot is still evident in many situations. The pilot can override the decisions and take control of the process.

Hence, a data-informed approach adds more assessment and revision to the use of data. This opens up more ways to use it. Since you are constantly measuring and analyzing different data sets, you are enhancing the collection and the use of data continuously.

Finally, data-informed decision-making understands that data is not always the  perfect information package it seems to be. As explained above, data can contain a huge bias, depending on how it was gathered. For example, the way how an interviewer asks questions in a questionnaire can have a direct impact on the results. Therefore, the data-informed approach doesn’t treat data as the ultimate truth.

The risk – outcome of data analysis depends on the analyst

Whilst there are many benefits to the data-informed approach, it isn’t a risk-free way to make decisions. Perhaps the main reason some experts argue against a data-informed approach is the added human element. A data-informed approach essentially argues against fully trusting the data. However, adding human interpretation can also add a bias, moving away from the actual evidence.

For example, if an analyst has a certain outcome he hopes to prove with his data analysis, he might twist and turn the data until it shows what he wants to see.


With the above in mind, should organizations look into adopting a data-driven or data-informed approach? Much of it depends on what type of data is available and the goals you are trying to achieve with the use of data.

Dave Martin distinguishes between the two approaches by saying data-driven is the ‘lazy’ approach and data-informed is a ‘more testable’ approach. In essence, being data-informed allows you to interpret the data and to understand the limitations of data, while data-driven organizations just use the data as it is. In the end, whether or not you operate data-driven or data-informed depends on your resources, your goals, and the data you actually have.

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