That was an especially good explanation dear Nicola! And yes, I smiled several times - you are so right!

Thanks for sharing!!! You both are amazing!

Great answer

Hi, One question out of topic, how do you create a generic DG policy for a project where all the policy will not be relevant

Great video thanks❤

I agree, Data Governance first

Great practical advice as ever!

Halp

How are Data Custodians different from Technical Stewards?

Hi there, great info.. fyi, the 1st two links aren't active

Great

Very great video and a foundational topic.

Interesting

I would like to the stakeholders who can log DQ issues. I have a concern on how to prioritize DQ issues. How do I mitigate against the logging process being overwhelming. How do we ensure that only top priority DQ issues are raised.

See the following link to one of my previous videos for some guidance on how to implement a Data Governance framework: kzbin.info/www/bejne/rYbCn4iHfb-Ioqc

Thanks Nicola

Business are often surprised when I tell them IT doesn't own the data - it's a business asset. It's the business which will make decisions off the back of well governed data - not IT.

Very good video, simple but powerful. Especially for people starting their journey.

Lear more with my top tips here: docs.google.com/document/d/1G9YPU3vsvYq0sLYkSRYbigUZAt1z-ucoS0AcnYnD6sY/edit

You are so brilliant, thank you for your hard work.

Really great point and good anaology!

This is the link to my other video explaining what a Data Governance policy is! kzbin.info/www/bejne/pnecfXSuYtCBa6s

0:24❤❤❤❤

Keep it up dude!!! I think you will be interested in smzeus!

But how you define who is the owner in such kind of data? there are any questions we could answer to get the right person to be responsible for? thanks!

Thank you Nicola

Link to the original video is here! kzbin.info/www/bejne/rYbCn4iHfb-Ioqc

Very insightful, thank you for sharing

www.silwoodtechnology.com/blog/unlocking-the-power-and-value-of-metadata/

Some more videos relating to Data Owners can be found on the following links. Enjoy! kzbin.infoplsVC9iSFRY kzbin.infod3HRWf0o_uQ

I did everything engineering and managing very hard 😢

So what can you do if you are the creator , and need full access ?

Thanks for the video, Nicola. Why would a Data Product Owner be different to a Data Owner if the product derived by the "Data Product Owner" comes from data owned by Data Owners? Should the data governance committee rather not decide on the rules of use for the Data Product, rather than leaving this for the "Data Product Owner" to do, and in so doing, introducing what seems to me to be a conflicting role?

Awesome

@TheDataGovernanceCoach I'm afraid I have a silly question, sorry. Where would the data governance fit if I were to draw a timeline for the data engineering process? Would it be before the engineers start data ingestion or somewhere else along the pipeline? BTW. That was the best explanation I've ever heard. Thanks.

Nicola, we ended up with the option of having two data owners. Only thing is we called them as Primary Data Owner and Secondary Data Owner. The criteria is the function who produces the data with maximum number of critical attributes is defined as the Primary Owner and the other function is tagged as the secondary data owner. From a RACI perspective, the primary owner has the accountability of that complete data entity and the secondary owner has the responsibility on the critical data attributes that he owns in that data entity.

👀 'Promo SM'

I completely agree Nicola. I work with companies to help formulate data strategies and governance is inevitably one of the key themes that comes out strongly - even when the company had not expected it to. To me, governance is a key enabling pillar of a data strategy.

Data Mesh is not a technology. Also, you don't mention the concept of 'Federated Computational Governance'. I'm still trying to work out how it relates to traditional enterprise data governance roles, organisations and operating models.

Really useful, thank you. Maybe a next video or blog could be giving a real world example of HOW Data Governance supports each of the other data management disciplines on the wheel.

I completely agree with the argument. In fact we expect that within our organization especially those involved in data projects have some level of data literacy. In practice what often happens is that they do not have the level that is intended and that is where we enter, of course, in the scope of fluency.

This has been my argument for years, but I could not think of a better word than Literacy. Thank you for articulating my minds thoughts!

What is a big or small it company in this context ?

Nice short clip!

Good points Nicola. I would point out however that the use-case for data lineage for the data governance team is different from the use-case for the data engineering team. They need lineage for different purposes. The governance related need is primarily to follow the path to know where their data comes from, and perhaps something about how the transformations may be impacting the final output on the reporting layer. On the other hand, the data developers and engineers use lineage to be sure that they know what will be impacted by the changes they make to the data flows. As such, the technical lineage you described as being far to granular will be difficult for a business users trying to get the basics from the lineage map, but critical to the engineers to ensure that the data flows as designed, and thereby ensuring top data quality. With all that said, a good data lineage solution provides both ways to consume the information, and in a format that is digestible for the business users, while technically sound enough for the data engineers. Of course, this is coming from a vendor of Data Lineage Automation (Octopai.com), but we also have a data catalog that is designed for the data governance types too. As such, our approach is to provide both perspectives in a way that will not only be easily understood, but also double as a way for the two stakeholders (IT and Business) to collaborate around the use of the data elements in the landscape. Care to learn more? Ping us on octopai.com.

One of the issues of Data Governance today is really the complete disconnect from the reality. The sole fact that you find Data Engineering "trendy", on its own is a statement of complete lack of understanding of how "data" works. Good luck on your high throne of delusion.

Glad to see this topic covered. I agree that training an ML model on datasets with data quality issues is a poor idea. If an ML model was trained on high quality datasets it will be more effective of course. Since general AI doesn't exist yet we will need human input, it's not out of this world for a data governance application to analyze a dataset and suggest data quality rules. I'm hoping the data governance applications begin to implement something like this feature, it would reduce the amount of human involvement at least. There could even be an ML model for specific dataset contents, for example a database field "SSN" could be read and identified as a social security field. The model would then suggest data quality rules and ask for human input, this is taking use of the data labeling concept.

Love this analogy !!!😃

Great video, as always. When our governance office took on access management, it was with our security team and our goal was to engage and help automate and audit access with our data owners, so it was a natural fit. Data Governance always seems to be the ultimate collaboration group!

Spot on, Nicola. Statistical Process Control (SPC) is an example of using AI to look for data that is outside of a statistical norm and its real power is to augment human involvement rather than replace it. It simply isn't practical for humans to adaptively monitor the plethora of data flowing around a company, especially as the window of opportunity to act upon insights derived from it is forever narrowing.