So here is my day 4 in the day-in-the-life series of blogs. For Day 4 I will summarize the key themes, frameworks shared with attendees, and the topics across the week. First, of course, is my Muse and the state of my brain Thursday morning.
My score for Thursday was 706, and the line looks quite stable. For Wednesday my score was 745, for Tuesday it was 757, and for Monday 699. So, it seems I actually improved towards the middle of the week, but my mind became a little more active after that. I would argue that the ideas I had collected through the week were synthesizing and I was already starting to think ahead to going home and reflecting on the information.
Popular Models and Frameworks
In terms of the most popular images and frameworks I shared during the week, here are the main ones.
Gartner’s Value Pyramid and “linking data to outcome” is a very popular workshop tool to help business and non-business folks explore how a business outcome can be de-composed into real data. This resource can be used in building and developing a data and analytics strategy, as well as prioritizing data for governance, and presenting D&A to the board of directors! It’s non-tech focused and very business-terminology focused.
We talked about about organization, setting up governance boards, and the role of stewardship. It seems there are a LOT of firms who have failed, again, with governance and stewardship. They are set up often too early (because the consultant told the too) and talking about standards and data principles all day only goes so far.
- Value Pyramid Workshop resource: Toolkit: How to Connect Data to Business Outcomes
- Value Pyramid and Three Rings recorded webinar: Link Data to Business Outcomes
- Governance Organization and Stewardship Role (day in the life)
Here is another graphic from the workshop. This helps explain how repeated use of the Value Pyramid can help discover your needed data standards for re-use. They exist in every organization, hidden in plain site. You just need to know your business outcomes to discern them.
A number of 1-1’s discussed how to report the business value of impact of D&A and also governance.
After all the fun and games I ended up with some rough tongue-in-cheek recommendations to try to call out the main conditions and issues facing organizations. In a nutshell, they are as follows.
- Shut down the governance council if it meets, has every business unit represented, and talks about data, standards, and principles. Too many organizations were shutting them down or having them shut down. They start up too soon and have nothing of (business) value to add. Yet.
- Re-write the Governance charter. It should be one page. First paragraph describes why we govern: to enable business and its outcomes and decisions. Second paragraph looks at who prioritizes D&A…which often exposes the issue with governance: it’s distinct from D&A strategy and before you embark on governance you need to have a D&A strategy process in train.
- Don’t hire stewards, yet. They don’t have anything to do at the start either. The need for stewards, and how to identify them, will become apparent as you work out which business outcomes are more important, and then what data drives those outcomes. Hint: Process owners are pretty much like “data owners”.
- Don’t start your governance effort by buying a data catalog*. There’s no point cataloging data for governance. You might need a data catalog by about month 9 of your governance program, if you are lucky. Note that the analytics use cases can use a catalog early; governance does not often need a catalog until much later.
- Don’t ever start a governance program by asking about data issues. Also, don’t go on about data principles such as ‘data is an asset’ and ‘data should be reused’. Don’t go on about data standards. Principles are not actionable and most organizations have the same ones anyway, and no one will care. Instead, ask about the most important or opportunistic business outcomes. Invariably any outcome will require data from more than one domain, so a focus on a single domain at a time, such as customer or citizen, is doomed to failure too.
1-1 and Individual Interactions
What follows is a summary of the topics, roles, and organizations I spoke with.
Topics and their relative frequency:
- Business Impact/Value of 37
- D&A Governance/MDM/Getting re-started 24
- Data & Analytics Strategy 12
- Building/Starting a D&A Org/Practice/Stewardship 12
- D&A Governance specific to analytics pipeline 9
- Application Data Mgt/ERP Data Governance 7
- Analytics/BI/Data Science 6
- D&A Road Map (systems, programs and tech) 6
- Becoming Data Driven/Data Literacy 5
- Product Delivery/Marketplaces/DevOps 4
- AI and ML Strategy and Leverage 2
- Data Fabric and/versus Data Mesh 2
- Cloud Infrastructure Implications/complexity 2
- Data Mesh (and therefore Data Fabric) 2
- D&A Trends 1
- Sovereign Data Strategies 1
- Data Security 1
1-1’s: 43 overall
- Tax & Audit; Insurance; Fin Serve 5
- Public Sector 6
- Healthcare 4
- Food 4
- Consumer Goods 4
- Investment/VC 2
- Regulator/Standards 2
- Banking 2
- Industrial 2
- Higher Education 2
- Research 2
- Defense 2
- Fashion 1
- Consumer Electronics 1
- Conglomerate 1
- Construction 1
- Energy 1
- High Tech/Vendor 1
- Services 18
- Manufacturing 8
- Public Sector 7
- Distribution 6
- Conglomerate/Co-op 2
- Retail 1
- SVP IT/CIO 14
- CDO/CDAO 7
- CTO (with CDAO responsibilities) 5
- Snr/Dir Enterprise Analytics/Data Science 4
- Dir D&A Governance 2
- Director/VP Enterprise Apps/Systems 2
- Supervisor 1
- Dir Data and User Productivity 1
- Leader, Innovation 1
- VPLeader, Innovation 1
- VP Enterprise Architecture 1
- Dir Trasnformation 1
- VP Data Services 1
- Software Developer 1
- Credit Data Engineer Manager 1
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