5 Steps to Optimal Data Quality

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.’

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.

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?

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).

A real-world example: NedZink

NedZink is a good example of a company that has committed to optimizing the quality of their data.

A number of their larger DIY customers had asked NedZink to exchange article data through EDI. It did not take long for NedZink to realize that the quality of their master data did just not cut it for this kind of operation.

Their data was appropriate for internal use, where it was usable despite a few extra handling steps, but an external party would not accept that.

NedZink decided to go all the way and to make data quality optimization a standard step in their process. The recurrent cleansing, in combination with the Data Quality Monitor, have dramatically increased the quality of the data.

The obtained level of quality and the speed at which the project was finalized made that NedZink was ready for electronic data exchange long before their own customers were.

 
“ We are most certainly pleased by the display of expertise and know-how by Type 2 Solutions. While many IT projects tend to be tedious, the data management project here was really painless. The data quality dashboard was set up in very little time and enables us to analyze the situation at a moment’s notice. Thanks to the involvement and proactivity of Type 2 Solutions, we have gained back the control and understanding of our business. The data makes sense again, which provides clarity, relief and structure. ”
Roel Frissen CTO at NedZink

Data Quality Dashboard

The T2S Data Quality Dashboard helps you to tackle data quality issues. It provides that kind of insight that makes it possible to improve and maintain data quality.

You can experience the Data Quality Dashboard yourself based on an example set with product data.

Start using the Data Quality Dashboard today to see for yourself how it can help you.

Strategic advantage

It is not easy to put a price tag on ‘poor’ maintenance. According to Jack de Hamer, data specialist at T2S, 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.”

Read more in the article written by Jack de Hamer about ‘Data quality is a strategic asset‘.

Do you want to improve the quality of your data?

Type 2 Solutions has a lot of experience with the optimization of business data and has supervised various master data management projects. Based on the knowledge gained in practice, we have developed a data quality scan.

The data quality scan helps you on your way and offers an excellent start for any master data management project.