With GDPR regulations imminent, businesses need to ensure they have a handle on data governance.
When the philosopher Francis Bacon declared that “knowledge is power” at the end of the 16th century, he inadvertently provided a fitting description for the digital age.
It’s not just chance that led multi-billion-dollar industry giants such as Apple, Facebook, Google and Microsoft to collect many terabytes of information every day. They, like Bacon, recognized the value of information and have demonstrated that data is the key to success.
However, not all data is good data – quality plays a crucial role. Accumulated information is useless if it doesn’t display certain properties.
The methodology of “data governance” tells us at what point a data record becomes “valuable,” how we collect it, maintain it, and how we archive or delete it at the end of its value chain. After all, data has a “service life” in the same way as a product.
When talking about “valuable” data, we first need to look at the foundation of functioning data governance: master data management.
A data object or data record should, by definition, map and describe a real-life object as well as possible. If we store a person object in our database, we have a good idea of what additional information we want to gather for it: the name, the street, the place of residence, the zip code, an e-mail address, and much more.
The more detailed the object, the more possible strategies we can adopt. If I contact the person being mapped by the object, is it best to do so by e-mail, telephone, cellphone, or post? If I have the necessary information, I have the choice.
Therefore, it’s crucial to have a correct and targeted description of your data objects. If these are handled carelessly to begin with, the data record now only serves a limited purpose. In the worst-case scenario, it’s rendered completely useless.
If we describe our data objects with a sufficient level of quality, we must ensure that the descriptions required in master data management actually exist. We call this procedure “data quality.” Let’s look at our person object for a simple example. Although it’s correct to define the “name” as the attribute of the object, this is useless if the field contains no value or an incorrect value.
The point at which the data is collected, known as the “point of entry,” plays a decisive role here. An effective data governance policy establishes safeguarding mechanisms, ensuring that a data record is always collected correctly and fully.
The advantages are plain to see – complete and correct data records save us a lot of time. A salesperson can only work quickly and efficiently if data meets the abovementioned quality standards.
If, for example, a price list contains gaps, it will cost even more time to research the required entries. And even worse – if the wrong prices are recorded, the meeting with the customer could be awkward, and even result in lost business.
Although it may appear obvious at first, make sure you don’t lose sight of the fact that data has a limited service life. We describe this phenomenon as the “data lifecycle.” How long this lifecycle lasts and which associated value chains we can extract from a data record depends on how we maintain this data record.
Data maintenance is therefore an integral part of a functioning data governance principle, as well as a decisive factor for stable “data quality.” It’s mainly about having “correctness” from the point of data entry onward to get the most out of it. However, deletion and archiving are also crucial because once data reaches the end of its lifecycle, it mustn’t become “zombie data.”
Only if we consistently remove useless data records can we obtain databases that are performant in the long term, saving costs on maintenance and related work.
So far, we’ve only addressed the quality of our data. However, there’s more behind this topic – protecting our company from external access is essential.
If we look back at the “spying scandals” over the past years (2009, Deutsche Bahn; 2010, various Lidl sales companies; 2013, Deutsche Telekom), we quickly realize that inadequate data protection is a serious problem with grave consequences. In addition to financial penalties in the millions, your corporate image could be damaged severely.
Data governance not only provides constantly high-quality data, it also protects it from damage and ensures the long-term trust of business partners and customers. In the long term, every company requires a data governance program because of these obvious benefits:
The data governance model supplies us with all the answers we need to establish such a program. Here the “better sooner than later” principle applies – the sooner you implement an effective data governance strategy, the lower the costs and effort.
There’s much more to data governance, so we recommend you check out our free white paper. Or you can get in touch with me and my colleagues as we’ll be happy to advise you.