Data awareness: the key to improving data quality

For the boards of many companies and organisations, the advent of the law was General Data Protection Regulation (AVG) was a 'wake-up call'. Boards noticed that they had insufficient control over the personal data of their customers, staff and other individuals. As a result, when the AVG came into force on 25 May this year, a number of companies and organisations were unable to demonstrate that they were complying with the law as required by it.

Discussion

It is an example of the problems many organisations have in controlling their datasets. But there are also other times when attention often arises to dealing with data. For example, when a board discusses strategic goals and discussion arises about the accuracy of the numbers rather than what the numbers show. Preparing a marketing campaign is another such moment. Then it suddenly turns out that customer and relationship data are incomplete or outdated.

Staff

A major cause of problems with datasets is a lack of 'data awareness'. Employees are not sufficiently aware of the importance of data to the organisation. People have learned to store certain data, but are not really sure why it is important and what other departments do with it. As a result, data supplied by one department to another is more than often incomplete or contains errors. Completing or correcting it then leads to extra work and additional costs.

Subject area

What can organisations do if they have problems with their datasets? First of all, they need to understand at board and management level that data awareness - and by extension higher data quality - adds value to the organisation. And that, on the other hand, low data quality can cost the organisation money in many ways. In addition, it is essential to recognise that data management is a field in its own right and should be handled in a professional manner. To be successful in raising data awareness, the board and management need to work on the organisation and its processes as well as its systems and employees. An organisation needs to grow in all these areas to improve data awareness and data quality.

Nine steps

Once the decision has been made to start working with data, organisations can take the following steps:

1. Identify key data objects, e.g. customers or products.

2. Resist the temptation to tackle all data objects at once. Start with one or two data sets.

3. Suppose customer data is involved, describe how that customer data should be captured, in what form and how detailed.

4. Appoint a data owner to monitor data capture and verify that work is being done according to the definitions and take steering action when necessary.

5. Examine systems and processes, and determine whether they fit the way the data should be stored.

6. Inform staff and discuss the processes and systems so that it is clear how they should work with them and people understand why it is important to get data entry right.

7. Establish Critical Performance Indicators (KPIs) for the data so that the quality of the data can be assessed and, where necessary, adjustments can be made to improve quality.

8. Once one or two data objects are properly set up, it can be decided to start working on a third or fourth data object.

9. Finally, it is important to continuously monitor data quality using the KPIs and, if there are any deficiencies, take measures that can lead to quality improvement.

Process of awareness

To be successful in raising data awareness, it is crucial to engage employees in a process of raising awareness about the importance of good data. The topic needs to come alive and employees need to see the added value and encourage each other to work on quality improvement. It is an essential part of the approach we take at Improven to achieve higher data quality.

Improven Data Quality Scan

Is there uncertainty about data quality in your organisation and has discussion about it arisen? Then Improven has a simple method - the Improven Data Quality Scan - with which we can quickly perform an initial analysis on your datasets. The method provides quick insight into aspects such as completeness, uniqueness, accuracy and correctness. Do not hesitate to contact us if you want to know more about this.