From Gut Feeling to Data-Driven Decisions

Whether we like it or not: we all have to deal with more and more data, both business and personal. This data, if used in the right way, can go a long way toward helping you achieve your goals. It provides the insights you need to make better decisions.

If we set up and automate the data processes in the right way, you can make informed decisions and productivity will increase so that you and your colleagues have more time for other work.

To help you do this, we have developed the Data Impact Model. This model shows you the steps you can take to make better use of data. In doing so, we focus on four key areas: People, Process, Leadership and Tools. If you want to work (more) data-driven, you need to ensure that employees have the right skills and mindset (People), that data is smoothly integrated into the daily routine (Process), that working with data is encouraged and aligned with the strategic objectives of the organization (Leadership), and that the right tools, technologies and data are available (Tools).

In this white paper, you will learn what steps you can take to achieve the level of excellence in each domain that will enable you to get more value from data for your organization.

What is the value of data?

The value of data is often difficult to calculate exactly. This is because most data has no intrinsic value, but the value arises when you do something with that data. At the same time, we know that the most valuable companies in the world are data companies. They collect as much data as possible, determine the areas of interest of their customers, make predictions and thereby accelerate innovation or improve their service. The interesting thing is that you can reuse data infinitely and the value of data increases once you make it available to others - inside or outside the organization.

About this paper

We wrote this white paper to support marketers, analysts, managers, CEOs, finance and HR professionals from medium to large organizations to leverage their data and become more data-driven. Because they work every day to achieve the best results for their organizations, and we are convinced that data can help them do that. We see in practice that the potential is there, but that many organizations struggle with how to embrace data. That's why we provide tips and tricks, a pragmatic model for setting it up, and several real-life examples that will get you started quickly.

People generally make the decisions. In everyday life, this is often done based on gut feeling. In recent years, however, we see more and more decisions being made based on data, both in organizations and by individuals. Consider, for example, improving your sports performance using apps such as Strava and Runkeeper. Technological developments are currently moving very fast and many people are struggling to keep up.

By changing our mindset and better understanding why data can help us, we are going to teach ourselves to back up our decisions with data. Organizations that recognize this are therefore investing, for example, in sharing user experiences, providing training and hosting inspiration sessions. Ultimately, you want everyone in your organization to know and understand how data can be better utilized so that you will achieve better results. Those results could be higher profitability, better patient care, better sports performance, lower environmental impact, etc.

Those individuals who can explore, understand and interact with data can be labeled data literate. They can decide what data to use and interpret to make the right decisions. Those who can do this report that they are happier with their work. The result is that you will have enthusiastic employees who are likely to work with you longer, improve organizational results, reduce costs and innovation will increase.

So it is important to improve your data skills. As a foundation, you need to be able to perform simple data analysis and formulate conclusions. Then you will work toward more advanced techniques such as data science, machine learning and AI. Always consider self determination theory.

If you want to become a data-driven organization, you will also need to put processes in place. Think about processes for where you get data from and where and how you process it so that the user can be assured that the data is up-to-date, complete, consistent and reliable.

With these processes, you can ensure that data is readily available to anyone who needs it. You won't escape rules and guidelines (governance), establishing who can see what, and especially what not (security). This will vary enormously per type of organization, and you must always take privacy legislation into account.

The important thing is that both the processes surrounding data are set up properly and that the work processes of employees are set up in such a way that they can work with data on a daily basis. So it is about preparing the data for the organization in such a way that it can then easily be used.

Ultimately, we want to use these processes to create an environment that will increasingly support self-service. Self service has the advantage that users themselves can immediately start using data where necessary. However, the more self-service you offer, the more tightly you must organize your processes. Think especially about data and content governance.

Data governance is not about limiting the access of a few users, but about giving broad groups of users proper control over data.

The goal of content governance is to set up processes related to authorization and promotion, for example. Here you have to answer questions such as: who is allowed to access which dashboards, how can you put a dashboard live in production, how do we validate the content in a dashboard and what about the corporate identity? This ensures that there is not a wild west of dashboards, causing people to lose the overview, the reliability decreases and we produce even more reports in, for example, Excel.

Your organization most likely has clear business goals. But to what extent have you included a data strategy in them? We see in (large) data-driven organizations that there is a clear focus to achieve certain data goals. It is important that directors and management see the importance of this, because we are collecting more and more data anyway. If you use this as usefully as possible then you can start to better understand and support your customers, patients, stakeholders, etc., produce better products or even monitor data.

Leadership plays a crucial role in guiding and supporting the data-driven culture within the organization. Sufficient space must be created to innovate and experiment with data. There must also be room to make mistakes and not to be punished immediately for them. This is called a growth mindset and an antifragile environment. A data-driven culture is essential to ensure that data continues to be used in the long term. The role of leadership is to encourage working with data by inspiring, engaging and motivating people.

Leadership further considers how the data-driven organization will be set up. Will you work with centralized or decentralized data analysis teams, or just a combination of these (e.g., T-shaped). Perhaps you want to work with centralized reports but want everyone to have the ability to access all relevant data where needed (data democracy).