Manufacturing line with machinery.
Chapter 3

The 2 pillars of Data Excellence

Many companies are experimenting with IoT, predictive maintenance and smart maintenance, such as adding sensors and developing machine learning models. Unfortunately, it often remains at this exploratory stage.

Achieving Data Excellence requires the use of various data and analysis tools, for predictive maintenance, advanced robotics and track-and-tracing, for instance. It all starts with a data strategy and data governance: the two most important pillars to achieve Data Excellence in manufacturing.

Pillar 1: Data strategy

A data strategy forms the basis for Data Excellence and describes how data contributes to the organisation's long-term goals. A strategy provides direction and is translated into concrete actions. Without a strategy, it is difficult to get maximum value from your data. Moreover, you also want to take data integrity, security and compliancy into account. These are all issues that are reflected in your data strategy.

In a strategy, you make choices that focus on the components that have the most business impact. This is not about tools like IoT sensors or operational performance dashboards, but about fundamental things like operational efficiency, process optimization and accurate demand forecasts.

Start with a Data Maturity Scan

Step one to a good data strategy? Mapping the current situation. To do this, use the Data Maturity Model. Using this model, you examine and assess the current situation and determine the company's ambition. Indeed, a data strategy describes the difference between that dot on the horizon and the current situation and translates this into a roadmap. This is how you achieve optimal data-driven maturity.

4 elements of the Data Maturity Scan

You can map the current situation using a Data Maturity Scan, which consists of four essential parts:
  • 1

    Business strategy

    The data culture, leadership around Data Excellence (functions and support), and the current impact of data are examined. And is there a clear business case for current data initiatives? It is important that business and data strategy are aligned.

  • 2

    Data organization

    Is data accessible to the right people and is there a Single Source of Truth (SSOT)? Stakeholders and lines of communication are mapped and whether there is sufficient trust to make decisions based on the existing data.

  • 3

    Current data and tooling

    What analytics and reporting tools are already in place? It also examines how data management is organized, such as operating models, guidelines, resources and skills. Aspects such as data governance, security, privacy and compliancy are also included in the scan.

  • 4


    What analytics and reporting tools are already in place? It also examines how data management is organized, such as operating models, guidelines, resources and skills. Aspects such as data governance, security, privacy and compliancy are also included in the scan.


Now that the current and desired situation have been mapped, you can get to work on the roadmap, that includes concrete actions, priorities and KPIs. If you go for Data Excellence, it take three to five years before you realize the complete roadmap. Do not be discouraged, because the implementation is a short-cycle and agile process of ideation, prototyping and implementing. Therefore you will soon achieve the first results.

Pillar 2: Data Governance

As you have already read above, data governance is also included within the Data Maturity Scan. This is for good reason, because especially in manufacturing, huge amounts of data are generated by machines, sensors and robots. And it is a challenge to handle such large data sets efficiently and adequately. Data governance is a crucial part of a data strategy and focuses on the policies, processes and people involved in managing, protecting and making best use of the value of data.

The benefits of data governance

Data governance ensures that data quality is at the right level to get reliable insights that you can in turn use to make decisions. After all, you are going for a Single Source of Truth (SSOT). Other benefits of data governance:

  • Security, privacy and compliance in terms of quality requirements and laws and regulations are ensured.
  • Consistency and uniformity in datasets, terminology and reporting.
  • Investment of ownership, so that responsibilities are known and lie with the right departments and individuals.
  • Support and cooperation, by working from a company-wide vision and breaking through data silos.

Time, quality, safety and transparency are essential factors in manufacturing. To deploy data as a strategic tool and optimize its use, data governance is indispensable. You can shape this in a data mesh, where certain data belongs to an expert in a specific domain. Although methodologies and frameworks do provide direction, there is no standard approach to deploying data governance. Data governance is tailor-made and leans on the identity, culture and distinctiveness of a manufacturing company.

Data Excellence starts with strategy and policy

A data strategy and data governance policy are indispensable if you want to achieve Data Excellence. A strategy provides guidance and supports decision-making, and data governance ensures that everything happens in a structured way, with reliable and qualitative data. Without these two pillars, it is difficult to get true value from your data.