Why data-driven working starts with structural data quality.

Organizations want to work in a data-driven way and are investing heavily in collecting and unlocking data. But availability is not the same as reliability. How do you ensure that the data you use to steer your business is actually accurate?

High data quality is crucial for internal decision-making, collaboration with customers and suppliers, and compliance with increasing legislation and regulations. Teams need to make decisions faster, products are being developed more quickly, and regulators are increasingly requesting detailed data. This puts pressure on the organization, but also offers opportunities.

Reliable data accelerates innovation and strengthens trust. Poor data quality achieves the opposite: delays, disputes, and risk. In this blog, you can read about the true cost of poor data, the benefits of good data, and how to structurally embed data quality in your organization.

What is the cost of low data quality?

As data is shared more widely within and outside the organization, the impact of errors also grows. Poor data quality does not affect just one department, but spreads through projects, reports, and decision-making.

Projects that depend on this data are delayed, require additional repairs, or deliver less value than expected. And the longer you wait to make improvements, the higher the costs. Problems pile up, become more complex, and ultimately require more time, money, and effort to resolve structurally.

What are the benefits of high data quality?

High data quality ensures reliable insights, faster decision-making, and less remedial work afterwards. Teams can focus on facts instead of arguing about figures.

But that quality does not arise by itself and is not a one-off clean-up operation. It requires structural attention to data sources, analyses, and reports. Data quality can only be guaranteed by consciously organizing and continuously managing that quality.

What is data quality management?

According to the Data Management Body of Knowledge (DM-BOK), data quality management is the structural planning, execution, and assurance of activities that ensure data is suitable for use and meets user requirements. It is not only about correction, but above all about prevention. It is not only about measurement, but also about ownership and control.

The best starting point is a targeted, project-based approach. It is essential that it is clear who is responsible for which data. This does not have to be an extensive governance program, but roles and ownership must be clear. Setting up data quality management helps to make governance concrete.

A pragmatic approach consists of the following steps:

1. Determine what "good data" means
   Define quality criteria such as completeness, timeliness, and consistency.
2. Establish a strategy
   Do you respond reactively to incidents, or do you take a preventive approach to measurement and control?
3. Focus on
   Identify critical data and existing rules and patterns.
4. Conduct an initial quality assessment
   Identify issues, prioritize them, and analyze the causes.
5. Develop and prioritize improvements
   Base choices on business impact and combine corrective and preventive measures.
6. Structure and monitor
   Set up procedures, measure periodically, and report transparently.

The chosen project approach, whether waterfall, agile, or hybrid, is less important than the iterative way of working. By improving in small steps, the organization learns what quality means in practice and where adjustments are needed.

The project phase is followed by the management phase. Here, data quality becomes part of daily operations. Issues are centrally recorded, assigned to data owners, and followed up. In the event of conflicts over priority, a steering group or governance board provides a solution. This creates a fixed cycle of identification, analysis, resolution, and prevention. The figure below shows the lifecycle of data quality issues and how this cycle ensures that operational data quality procedures are continuously updated and improved.