In today's world, the frequently used term “data-driven business” often falls short of what can be achieved in terms of business growth through the effective use of data and analytics.

Profit calculations and strategic decisions informed by reliable data are crucial in business development. If a business isn't based on trustworthy, understood data, it is essentially based on guesswork and intuition.

A critical part of the equation is the ability to read and convert data into practical actions, thus being data-driven. 

What does being data-driven really mean?

Today's data tools, such as Google Analytics, Looker Studio, Matomo, and PowerBI, are familiar to many. However, their presence in an organisation's technology stack does not automatically make it data-driven.

Data-driven business is about turning cold numbers into a story that engages various stakeholders, allowing for the sustainable application of practical measures.

Starting point: Data vs. problem

The better an organisation can analyse and truly understand the data it collects, the less it needs to use the data at hand to find problems.

In a nutshell:

• Data should primarily be used to solve identified problems

• Searching for problems in the data can lead to tunnel vision

• If data is mainly used to seek problems, it could indicate that a comprehensive understanding of the business needs to be developed.

As a clarification: it is possible to find problems to solve in data, but this approach requires the ability to identify issues both from data and across the business. Fundamentally, hunting for bottlenecks in the numbers and charts is slow, and data deficiencies or distortions can lead to prioritising the wrong issues. 

Before diving deep into data analysis, a company should know its products, customer base, business environment, and competitive landscape well.

For example:

Company X wants to understand why sales are lagging despite heavy investment in marketing during the peak season. The marketing team has been tasked with uncovering the root causes of the problem.
The table below (hypothetical) serves as an example of how data can support decision-making. Note: the analysis can be extended considerably.

Properly analysed and presented data can provide decision-makers with the opportunity to understand what is truly happening in the business - the next step is to apply development actions based on the collected data.

The next time you find yourself (or the organisation's marketing team) just looking at charts, remember these points.... 

4 Steps to Support Being Data-Driven

1. Problem Identification and Goal Setting: A smart approach begins with identifying the problem (there are many tools for this, such as SWOT analysis or a customer journey model) and setting the goal in a measurable and trackable way (read more about SMART criteria).
   
   
   

2. Use Data to Solve Problems: Once the problem is defined, data can be used to select the best approach, for example:
   
   i) analysing historical data to identify trends that help to understand the root causes of the problem.
   
   ii) collecting new data to deepen the understanding of the problem or validate potential solutions.
   
   
   

3. Focus on Practical Insights: An effective approach is to generate ideas from data analytics that assist decision-making. Prioritize issues that help identify essential actions and avoid getting stuck with irrelevant information that leads nowhere.
   
   
   

4. Measure, Measure, Measure: When the solution is applied, its impact must be assessed based on reliable data. This means measuring the solution's effectiveness and making necessary changes – A/B testing serves as an example. The impact of the actions must be communicated to stakeholders to support data-driven decision-making as effectively as possible.
   
   
   

In Summary

Focusing on the correct application of data analytics in problem-solving allows for a robust foundation for informed decision-making in business, and for identifying practical and measurable development ideas.

The success of data-driven decision-making requires strong data management and analytical skills. In addition to the right tools, a company must ensure it has the right processes and expertise to collect, store, analyse, and visualise data.

If the development of in-house expertise seems too large a development project, it is advisable to rely on the comprehensive expertise of data consultants.

Would you like to hear how being data-driven is implemented in practice?

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