In my Daily Work, I Help Customers to Have Data which They Can Use
My Name is Oliver Reh, I have two kids and I live in Ravensburg, the very south of Germany. I love the mountains and skiing or hiking with my family.
I have been working with data since the Millennium change, being an owner of product data for many years. I know first hand how bad data can affect business processes and decision-making – so I developed a real passion for data quality!
Here are my top-3 Q&A on data quality:
1. What is data quality, and why is it important?
Data quality sounds very abstract at first. I can't touch data, and its quality is often invisible to me. But as soon as I understand data quality as suitability for a certain purpose - "fit for purpose" - it becomes tangible.
These purposes are manifold: starting with the simple address data of my customers, via my sales reporting, via digitalized business processes to the decision template for my board of directors or even the training data for the new machine learning project.
2. How can I measure data quality?
Basically, it’s a good idea to measure data quality. True to the motto: "If you can't measure it, you can't manage it".
But first, I have to define the goal. We don't strive for the best possible data quality, but for the quality that is needed for a specific purpose. And we start where data errors cause concrete problems.
To do this, we form so-called causal chains: we identify the "pain points" and their effects with the specialist departments and then investigate which data are the cause. Then we define rules to which the individual data must correspond to be "fit for purpose". Once everything has been technically defined, the technical implementation follows. Very often we can use the "on-board resources" available in the companies for this purpose.
3. What do I have to do to get good DQ?
Quality arises in the process, not during the quality inspection after the end of the process. So I have to go into a proactive mode to ensure data quality right from the start.
For this purpose, we have developed the so-called CDQ Data Excellence Model in our Competence Center Corporate Data Quality (CC CDQ). This enables me to derive the required data quality on the basis of the strategic corporate goals and also to achieve it using proven methods and good practices from other companies.
The model helps me to define the data strategy, the processes, roles and responsibilities, the data architecture, and the selection of suitable systems.
Always with the goal of generating not only good data but also added value for the departments, and their business processes.
#SharingDataExcellence: Data Quality SOS Workshop
If you need help with your Data Quality, this might be interesting for you: We would like to raffle one free data quality workshop. Usually, a short workshop helps to
- understand the impact of data quality on your business processes and analytics capabilities
- evaluate your current data quality management capabilities
- identify fields of action and recommend good practices
To participate, contact us via the contact form in the infobox until the 12th of December 2019 24:00 CET and tell us why you think you need help regarding data governance. Please check out the details of our promotion in our terms of participation below.*