It’s remarkable to see how AI is prompting the ‘comeback’ of various Data Governance and Data Management components.
It almost feels like a band’s Reunion tour!
With a slight twist – The AI Reunion Tour©!
The background to these ‘comebacks’ is ultimately to inform AI of how to use data correctly, and to get towards trustworthy outputs.
Some of the band members making the ‘comeback’:
- Ontologies : These are where we can configure definitions and meaning of business concepts, along with the relationships between those concepts
- Knowledge Graphs : Where the data is stored, typically based on Ontologies. Ontologies help to define the schema of Knowledge Graphs
- Semantic Layers : Used to map complex data, and how to use that data, into a business friendly format. Example use case: helping with ‘talking to our data’
- Data Warehousing : Particularly from a cloud perspective, a solid foundation for AI augmented analytical initiatives…assuming we can ensure that the data is: subject orientated, non-volatile, integrated, and time variant. Easier said than done, but AI augmentation can help with this also.
- Data Governance & Data Management capabilities e.g. Data Quality Management and Metadata Management.
- Awareness in relation to the importance of these areas has increased significantly
- Data Modelling : This is something that plays a part throughout the above areas
All of these areas play a key role in relation to trustworthy AI.
I also have a feeling that at some stage this year, we’ll see the guest appearance from Master Data Management, and The AI Reunion Tour© in full swing!
Update: I’m offering a new training course setting the background and context to all of the above, and to bring Data Management back to center stage!
© Dan Galavan. Post written soley by hand. Image credit: Gemini.