How can you support data stewardship and ownership with your governance tools?

Once again, these are data management tools, not data governance tools. But yes, they are key to successful and effective data management. You do need your data operating model in place before you implement these tools though. Without a clear way of operating you will waste money on these tools, especially a data catalogue like Collibra or Alation, as your catalogue will become a metadata swamp!

Other tooling to consider are ID and access Management systems, risk management systems, policy management systems, rules management engines, content management systems, case management systems and security monitoring and Intrusion Detection Systems.

You’re absolutely right Pete, and no more so than Analytics Governance specifically context (metadata) which is at the topic of the business value chain. Without this toolset and supporting best practices, full business value from data will not be realised. Check out Metoscope from Metonomy or contact Alex Stevenson for a meaningful & insightful discussion

A data catalogue is the home for all your metadata but don’t approach it as a technical tool. You must approach data catalogues from a business requirements perspective. You need them to solve business problems related to finding and understanding your data. If they become too technical they only serve engineering and architecture communities and their value will be diminished.

Your catalog could be visio diagrams, spreadsheets, SharePoint or a shared folder. The catalog at the most fundamental level is just a storage locker for artefacts like definitions, diagrams and specifications related to your data domains, the management and data governance practices that you subscribe to.

It need not be a single tool, in fact often it is not just one tool and one practice. Often it doesn't describe owners and stewards and in some cases is not even accessible to them. The basic DQ might allow you to define data sources and the associated rules and measure you expect in those sources. An ideal one might be an adaptive one that evolves over time based on patterns detected in the data and the systems that store the data.

I am not sure that the data lifecycle is entirely the right term here. I think more importantly it speaks to principally data origins, any transitions which I suppose you might call life-cycle and where the data impacts. What it doesn't necessarily tell you is who has access to it, how permanent it is, not how relevant or valuable it might be. It also needs to be related back to DQ to have higher value.

This is a novel recent addition of late, but it includes case management, upvoting scores, usage statistics, ownership and commentary. All as metadata elements that might influence those who might use the data asset or plan to use it.

Don't think about tools; instead think about how business outcomes are enabled by capabilities. Those capabilities are granular and will be part process and part technology. Define the capabilities, map existing technologies and people/processes (or the lack of) and always keep them linked to specific outcomes of risk mitigation or compliance adherence. Once you are clear on this then start reviewing technology platforms, as others have said, with a view to interoperability. If you start with an outcome and capability view then you can adjust technology roadmaps over time. Also, I agree with some other comments that Data Governance is "what" and Data Management is the "how".

You will definitely need a suite of tools so don’t think you can get one tool to do all aspects of data management. Think about interoperability and how chosen suite of tools will work together. Think about this from a user experience perspective to ensure your users can effectively tackle the gnarly data problems they face. The tools are there to help them, not to hinder them.