How to build the right data mindset for people and processes step by step

31 March 2021


Collecting, managing, integrating and using data properly requires more than a list of technology tools for help. Data as a strategic differentiator needs organizational change to embed this revolution within the organization. This includes creating a data culture, raising data awareness among all employees and making your processes data-driven. Find out what it takes to get started step by step.

In the first blog post of this series, we explained the foundations on which a data-driven organization is built. In this article, we will focus on how you can work with your people and processes – two of the three most important pillars – to develop a solid data strategy. The third technology pillar, the data platform, will follow in a third article.


Commit to a shift in mindset, skills and responsibility

When we talk about people, we're not (just) talking about a fancy data team. A prestigious restaurant isn't simply successful because of their expensive ovens; each link in the chain needs to be right. Every employee should therefore be included in the data story every step of the way. The management team can play an important pioneering role here, for instance, by often referring to important data insights in their strategic messages. The use of data in business decisions is a reflex that every employee should have. This requires three changes:

  • A shift in mindset
    For every challenge that arises, data can formulate (part of) the answer. The reflex to tap into this resource can only be fully utilized when employees know what data is available and familiarise themselves with it. When creating or manipulating data, such as customer data, it's also important that they fully understand the impact half-baked data can have on the quality of the underlying process or on your own decision-making processes.
  • A shift in skills
    A data-driven organization works uses tools to create reports or monitor data quality, an AI model to automate processes, and so on. Most members of staff need to be able to work with these tools to create reports via self-service BI tools that give them insights into the market situation or customer profiles, for example. Their functional skills must also be improved to gain insight into the data and the data lifecycle. Where and when does the creation, transformation and disposal of certain data take place? And what is their own influence on this?
  • A shift in responsibility
    Certain profiles within your organization will become the owner of data domains. They must closely monitor data quality and set up procedures to correct data if necessary.

A streamlined process facilitates high quality data

Starting at the beginning always works. First of all, map out how things are going to take place during the day and how data moves throughout your operational processes. This will allow you to make realistic improvements based on existing pain points and doesn't have to turn all your processes upside down. Many perfectly written data policies have already been left by the wayside as they couldn't be applied in reality. Availability, quality, access, security and privacy are important elements that need to be managed through an entire data-driven process. Good processes are also embedded. If a sales employee creates a new customer account, they must be able to see whether it already exists in the system and what information is needed to feed all the underlying processes. Business and IT have an important role to play here, and must be aligned and aware of each other's objectives. Data is changing all the time and both parties must stay in close contact with each other to keep the processes aligned. Choosing the option 'other' can work perfectly from a technical point of view, but is a missed opportunity to collect information for the business.

A double-edged sword

Processes are carried out by people, so attention has to be paid to both aspects. Changing mindsets calls for change management. In general, there must be a sense of awareness along the lines of 'quality starts with myself', as an error at the start of the data chain leads to wrong decisions at the end of the chain. Many companies start from the end product, such as trendy AI algorithms, but don't realize that a lot of work is required to get the basics right first. This in itself doesn't have to be a massive shift, but you do have to be mindful of it.

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Discover the first blog post in this series. Read 'These are the foundations for building data-driven organizations' now.

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