Data Quality
Reliable data is essential for making good decisions and successfully steering by data and AI. Poor data quality leads to errors, inefficiency, and missed opportunities. With a structural approach, you tackle data quality problems at the source and build a reliable data foundation for the whole organization.
Why Data Quality
Data quality is the degree to which data meets certain standards and criteria. High data quality means the data is fit for its intended use, while low data quality leads to errors and inefficiencies. To make quality measurable, we often use these dimensions:
- Correctness: to what extent does the data give an accurate picture of the actual situation?
- Completeness: does the data contain enough information to base meaningful decisions on?
- Consistency: does data stored in multiple places match?
- Timeliness: how up-to-date is the data?
Good data quality leads to better decision-making, more trust and compliance, higher efficiency, and lower costs. And because data is the basis for AI and machine learning, reliable data is also a prerequisite for usable AI outcomes.
Our approach
Data quality is not a one-time cleanup, but a structural part of working data-driven. That's why we help not only technically, but also foster a culture of data awareness, in which employees see the value of accurate data and actively contribute to it.
- We start with a thorough evaluation of your existing data and determine which quality level each application needs.
- We trace the causes of quality problems back to the source, instead of continuing to correct symptoms.
- We set up processes and tooling to identify and correct data problems, for example with a data governance platform such as TimeXtender.
- We anchor the agreements, so data quality becomes a lasting part of daily practice.
What does Data Quality deliver?
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FAQ
What is data quality?
Data quality is the degree to which data meets certain standards and criteria, such as correctness, completeness, consistency, and timeliness. What counts as "good enough" differs per application.
How do you measure data quality?
Data quality is measured using dimensions. The most commonly used are correctness, completeness, consistency, and timeliness.
Why is data quality important for AI?
Data is the basis for AI and machine learning, and these are only as good as the data they are trained on. Poor data quality leads to unreliable outcomes.
How do you improve data quality structurally?
By tackling problems at the source instead of correcting them ad hoc, and by fostering a culture of data awareness, supported by the right processes and tooling such as TimeXtender.