A Guide to Data Quality

While working with some exciting strategic data management projects together with the data management consultancy firm parsionate, the quest of ensuring data quality in large companies is one of the key topics.

Your Success Factors

In their latest whitepaper parsionate has put data quality in context. The idea behind is this is that only when your data quality initiatives are connected with business goals they will be acknowledged and sustained in business operations.

Marketing departments today want to drive more sales through online channels. To do that you will need a bunch of data quality improvements like having convincing product descriptions for all products put on sale online and having consistent and updated prices across all channels.

In operative management you always strive for making better decisions. To be able to do that you need accurate, updated, and well-related information about markets, products, competitors.

In strategic management your aim is to exploit economies of scale. During mergers and acquisitions, managers must pay particular attention to data quality. In the case of mergers, it must be ensured that the data quality of the previously separate systems is impeccable so that weaknesses are not ported to the new overall situation.

For HR key objectives are to find the best candidates and develop potential. These processes are being digitalized with machine decisions involved. This can only work if the undelaying data is complete, updated and consistent.

For logistics the future belongs to the intelligent supply chain. In many cases the data needed to support this is available, however not in the right quality at the right time. Here, the right data quality management can make a huge difference. 

Source: parsionate, data quality in context

The Right Steps to Drive Business Forward

Your roadmap to high data quality that will pave the way to successful business should involve the following 8 steps:

1: Appoint responsible persons for the data

2: Set targets and Key-Performance-Indicators

3: Evaluate data quality of existing data

4: Cleanse and harmonize data inventories

5: Define standards and processes

6: Automate data quality maintenance

7: Regulate data quality across divisions, groups and borders

8: Continuously improve data quality

Learn More

To get more details on the range of success factors for the various business areas and the 8 step roadmap you can download a free copy of the parsionate Data Quality in Context guide here.

4 thoughts on “A Guide to Data Quality

  1. Matthias 12th April 2022 / 07:48

    On second position (right after appointing responsible persons), I would strictly recommend to build a data catalog as a very foundation.

    • Henrik Gabs Liliendahl 12th April 2022 / 08:45

      Valid point, Matthias. One can always discus if data catalog is part of data quality or is a seperate discipline adjacent to data quality just like data governance and data quality are overlapping disciplines. Nevertheless, having data catalog as part of the the overarching data management steps at that point is well recommended.

  2. Jeppe Thing Sørensen 4th May 2022 / 19:21

    Hi Henrik,

    One thing I have also experienced in my work with data quality is, that it is important to learn how the organisation is structured.
    Data quality reporting should hit the right data maintainer (could be a company, region, performance unit, separate organisation handling parts etc.) not just show an owner that there are 100k errors. If you do that your job has just started since the systems often do not reflect the organisation.

    • Henrik Gabs Liliendahl 5th May 2022 / 14:47

      Thanks for adding in, Jeppe. Indeed, data quality issues are most sticky where several units and/or systems are involved.

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