5 Steps to Optimal Data Quality
In this blog post Type 2 Solutions, presents a practical 5-step guide to optimize data quality. By offering everyday examples and clear steps, we simplify the concept of data quality, providing insights on achieving optimal data standards.
How to get a grip on data quality
Data quality management is one of those concepts that is hard to define and hard to put into practice.
That is why Jack de Hamer, data management specialist at Type 2 Solutions, has put together a simple 5-step guide with everyday examples to help clarify what data means and how to get a grip on its quality.
Data Quality Management
An online search for the term ‘data quality management’ yields – among others – a link to techopedia.com.
The site defines data quality management as ‘an administration type that incorporates the role establishment, role deployment, policies, responsibilities and processes with regard to the acquisition, maintenance, disposition and distribution of data’.
“In order for a data quality management initiative to succeed, a strong partnership between technology groups and the business is required”, says Jack de Hamer
5-Steps towards optimal data quality
Unfortunately, this definition of data quality management does not really offer any suggestions on how to go about it. That is why we have put together 5 easy-to-understand steps towards optimal data quality.
The 5 steps towards optimal data quality are:
Step 1. Define the kind of data to be collected and the elements within that data which should be optimized.
Step 2. Define rules for each data element and automate the control mechanisms.
Step 3. Assign the responsibility for the optimization of a data entity to a single person.
Step 4. Automate data validation according to their definition, and keep the results.
Step 5. Correct exceptions and adjust their definition if necessary.
How does it work?
The diagram illustrates the 5-steps process.
Data is extracted from the data source of an application (step 1). This data is automatically evaluated by the Data Quality Monitor (step 4), according to the business rules resulting from the data definitions (step 2).
Exceptions to those business rules are detected, collected, and presented to the data owner (step 3), who can now take appropriate corrective action, either by correcting the data, or by fine-tuning the data definitions.
By repeatedly going through this process, a continuous improvement of the quality of the data, and of the optimization process leading to it, is achieved (step 5).
The Data Quality Monitor is an online dashboard that tracks and measures the quality of your data and helps you to take targeted action. It provides that kind of insight that makes it possible to improve and maintain data quality.
Discipline and planning
The optimization and above all the preservation of data quality requires discipline and planning.
It is not easy to put a price tag on ‘poor’ maintenance. According to Jack de Hamer, this is strongly influenced by its consequences.
“The costs can vary from a few euros per customer when mail is returned to thousands or hundreds of thousands of euros in damage as a result of incorrect data on an invoice, customs document or label.”
Learn more
Learn more about Type 2 Solutions' products and our latest data quality management solutions.