How to be data-driven? Start by answering these 5 questions

In a world where technology usage is increasingly a competitive differentiator for companies, extracting value from data is becoming more important. However, many companies claim to be data-driven, but in practice, they are not. According to research, while there is a lot of data generation, there is a lack of data management. Additionally, many data teams are overwhelmed, and few companies have a clearly defined data strategy.

But what does it mean to be Data-Driven? How do you properly extract, interpret, and manage data to make more effective decisions? What are the steps to transform all this into tangible results that translate into revenue and profit for organizations? Let’s break down these points below.

What Does it Mean to Be Data-Driven?

For companies to become data-driven, it’s essential to first understand what that really means. A definition I appreciate, taken from AWS’s “Modern Data Strategy” material, explains what being data-driven entails:

“An agile plan of aligned actions encompassing mindset, people, processes, and technology that accelerates value creation by directly supporting strategic business objectives.”

From this definition, we can note some important points where many companies fail in implementation. First, the implementation plan must be agile and aligned with three fundamental pillars: people, processes, and technology. We will discuss these further later on.

Another important point is the direct creation of value. Many companies believe they are data-driven but lack ways to measure this impact and connect it to business objectives.

We can also observe that companies that already have a data-driven culture share some common characteristics.

Characteristics of a Data-Driven Company 

Truly data-driven companies have some similarities. Thinking in terms of agility, they not only “think big,” but also deliver iteratively, prioritizing the deliveries that generate the most value in the shortest time for the business. Additionally, these companies manage to align a vision between IT and Business, cultivating a learning culture focused on experimentation and innovation.

Finally, but not least, they have mature structures for privacy, security, compliance, and governance that do not hinder innovation.

These similarities can be organized into five main pillars:

1. How is time allocated?

The time in data-driven companies is focused on innovation to address customer priorities, rather than finding and accessing data. According to an IDC study, 26% of an average employee’s time is spent searching for and consolidating information distributed across different systems. Increasing the ease and speed of effectively accessing data can have a significant impact on revenue.

2. How Are Decisions Made?

For truly data-driven decision-making, it is necessary to test and measure actions, continuously evaluating feedback. Whenever there is a suggestion, it should also be considered in the context of an A/B test to correctly evaluate the result. For example, in its less than 20 years of existence, Netflix has conducted over 33 million experiments.

3. How Is the Work Done?

Another point that greatly impacts the speed of innovation is the time it takes to make decisions. In companies with a strong hierarchical structure, decisions take longer to be made, and often the person making the decision is not the one with the most information to do so. At Amazon, for instance, they use the concept of “two-pizza teams,” where there are more than 3,000 distinct teams innovating with data, with the autonomy to make decisions within their scope of work.

4. How Is Technology Built?

In companies that believe they are data-driven but actually aren’t, the focus is on technology without tracking the real business impact. When departments don’t communicate, innovations are centered on enhancing tools rather than measuring the value generated for the company.

Additionally, we now have technologies designed for specific purposes, which is essential for an efficient data strategy. 

In Formula 1, for example, 1.1 million data points are collected per second from 120 sensors. Working with this volume of data is only possible by using the most appropriate technology for each need, never trying to adapt the need to the technology.

5. How Is Data Viewed?

One of the major changes in recent years is how companies view data. For a long time, data was seen as platforms, with solutions built from them. Currently, we see that the most successful companies view data as products. 

Data is a collective asset, shared across different lines of business. Along these lines, we have data producers and consumers, with responsibility for managing and generating value from them.

Finally, How to Become Data-Driven?

As we can see, a data strategy involves several aspects and requires significant dedication to build. However, when well-implemented, these efforts can yield great returns for the company.

To advance in building a Data-Driven culture, the first step is to understand where the company is on its journey to create an action plan. This can be done through a data maturity assessment. Additionally, one of the best ways to accelerate the process is through real-world cases. Identifying a company challenge and understanding how a data strategy can help solve it increases the team’s knowledge, generates value, and brings tangible returns with the mindset of “Think Big. Start Small. Scale Fast.”

To identify opportunities for these initiatives, the D2E (Data-Driven Everything) methodology can be used, which is based on Working Backwards. This approach helps to understand the challenges of customers, whether internal or external, get to know the personas better, and design a roadmap starting with an MVP.

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If you want to achieve tangible results with a truly Data-Driven culture, get in touch with us!