How can we find transparent & trustable Data Product Discovery based on solid Data Governance?
I’m happy to announce that the webinar in which I cover this, along with host Ryan Crochet is now publicly available!
Following on from an informative intro by Ryan, I explain:
- What is Data Discovery – from identifying potential sources of data, to the role of Data Observability, to the required Subject Matter Expertise
- What is its antithesis: Data Discovery Chaos?
- If we can define it, we know how to identify it
- If we can identify it, we can address it.
- There are various Data Discovery Chaos sources, e.g. Data Product duplication, tacit knowledge, and AI Drift
- How to navigate the anti-patterns, from the Data as a Product mindset to the FAIR principles, to plain old agility
- How Data Discovery is a Team Sport
- I cover the six main roles involved such as Data Stewardship, Compliance & Risk, and of course AI Augmentation
- What characteristics can be used regarding findable, transparent, and trustworthy Data Products?
- This includes various Data Governance capabilities not least Data Quality Management, Data Classification, and Auditing
- The role of Data Catalogs & Data Marketplaces, and why they need to be scalable
I also provide 5 Quick Win challenges for the Data Practitioner, such as:
- Creating a first iteration Data Inventory
- Trust Scoring approach
- Validation Check Automation.
In summary, I explain how to navigate from Data Discovery Chaos to Findable, Transparent, Trustable Data Products, in a practical, practitioner focused manner.
The webinar is available here, runtime 38 minutes.
Thankyou Quest Software for the invitation to speak on this topic.
I’ve already had great feedback from the Data Community since the original webinar broadcast, and hopefully it will help you on your Data Discovery journey, and if you have any questions, do let me know.
© Dan Galavan