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It is often asked if a data governance program automates not only the ingestion of technical metadata, but also the connection of the technical metadata to its business context.
For example, we can import into a semantic catalog a list of tables with their columns from a schema or a database from a system. And as a result, we can see the hierarchy of this data set nicely catalogued into a data governance environment like Collibra. Here is an example:
As shown above, we see the ingestion of two tables and their columns. However, what is the next step? Some questions that we need to answer are:
The questions above are just the beginning of this course, as there are a lot more to consider:
But let’s get started with the three important questions above that address the first line of lineage.
It is easier to start from importing technical metadata. Just scan the databases and bring them all in. No requirement for business models, or ontologies, or asking business people to design anything. But then what? How do you know that this column stores a customer ID? In addition, how do you know if this customer ID is a master attribute or not? Furthermore, how does it translate into a business word that any user can understand? Is it a customer ID for the Customer Service center?
There are two ways to understand the meaning of data. Let’s look at each one in detail:
User input is the first step. The user can indicate some of the following right upon data ingestion:
We can try to automate the correlation of technical metadata to business data. There are a few ways we can do that:
Artificial Intelligence offers an amazing array of algorithms and methods to help us automate matching technical names of assets to business terms and concepts. However, a methodical step by step approach is necessary for this implementation. Also, some of these methods can be very complex. And most of the time the output should be confirmed by a business user.
There will always be a need for a business steward to validate the automated work of business model design based on a technical data model. And the work that comes out of a technical model in an automatic way is certainly very helpful, but will never be final before the human factor refines it.
Vasiliki has 25 years of experience in Enterprise Information Management and Architecture. She worked for 23 years at IBM and Oracle, and is currently part of the information governance sales team at Collibra. She also serves as an adjunct professor at Fordham University, Gabelli School of Business where she teaches an evening graduate class on Information Management. Vasiliki is fluent in French, English and native Greek and Ancient Greek.
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