Day 4 (final) Day in the Life of an Expert…at a Gartner Data and Analytics Conference – March 5th, 2025
3.45am?A new pattern has been established!??Once you wake up early for a few consecutive days, it becomes a routine you must break.??I’m assuming when I go home and sleep in my own bed, I will.
5.40am?Awake.??Tired but not able to get back to sleep.?
6.20am?Time enough to play with email.??It helps to get most of the work out the way before the day starts.??Re-read 1-1 plan for the day.?
My Oura scores are down, and my body battery is not even past 50%.? The event is clearly taking a toll.? My HRV is down…
I forgot to Muse (drat!) so I can spare you the level of brain activity in my head.?
7.15am?Headed down for needed coffee and breakfast bite.??Gathered my Lego building blocks for our D&A operating model, just in case.
8am?1on1:?Data Governance
8.30am?1on1: data strategy
8.55am break – need more coffee!? Called into work for a team call about Content Strategy. ?Sounds like fun!
9.30am?1on1: Data Products
10am?1on1: Data governance
10.32am break.?
11am?1on1: value
11.30am?1on1: Data Management and services
12 noon Lunch and prep for next presentation.? Trying to figure out my opening.? Something about how we all tend to over complicate stuff in our lives…since the session is all about getting to a simpler life!? Dry sandwich and chips.? Bumped into Saul doing email and Mark and Ehtisham preparing for their session (which was entertaining and encouraging).
1.30pm?Presentation: How to Streamline Data Quality, MDM, and Data Governance Programs for a Simpler Life.
2.10pm After taking a few questions from those in the audience, I headed back for a water and to get my table ready for more 1-1s.
2.30pm 1-1 Data Governance
3pm 1-1 Data Governance organization
3.30pm Break.? Not sure I can fit another coffee in but will try.? Are we almost done?
4pm?1on1: governance
4.30pm?1on1: analytics (but ended up as a no-show) so was able to get back to my room, change, and head out to the airport to try to avoid any rush.
After all the interactions over the conference (details below), I thought I would summarize the key themes I heard.??They are in some sense of scale and intensity.
Of course, figuring out how to demonstrate, communicate and account for business impact of D&A was top and almost universal.??I would struggle to think of any interaction that didn’t incite this.
Second was the heavy intensity with data and analytics governance and the return of MDM.??It seems this was expected given the number of vendors on the show floor for the governance category was very high.?
MDM is back.??Not that it really ever went away.??But it did certainly drift into the trough of disillusionment as it was meant to do.??It seems a fresh, new, lean MDM is the order of the day.??It seems a lot of organizations get the idea that master data really cannot be ignored, nor will it ever go away.??And technology helps but does not, cannot, replace the needed involvement of business roles.
Third, and quite pleasing for me, is the hype and trouble with data and analytics products.??I met several firms all who had similar experiences with their data or analytic product programs.
Many agreed they had built too many products that were not being reused.??Most interestingly there were several organizations that had discovered what I had spotted a year or so ago: the need to explicitly recognize semi-finished products.??More importantly, these semi-finished products need to be owned by D&A experts and will not be owned by business roles.
I saw this firsthand with quite mature clients 12-18 months ago.??But I was not able to find the challenge consistently.??Now it’s everywhere.??I don’t research data mesh but what happened???How can that idea have been so half baked???Anyone who worked in manufacturing would know that before you make a product, you must study the market.?
More usefully are the ideas of Make to Order (MTO) and Make to Stock (MTS).??A MTS item is a classic reused product design.??A MTO is a custom one-off. In D&A parlance, MTO is a data science project.??If you make or manage it as a product given its unique nature, you’ll have a product on the shelf with all the overhead and no reuse.? That’s bad.? But so many firms are doing this.
Lastly, if you plot out all the ‘where used’ pathways (for D&A, that would be akin to lineage), you will spot a big tradeoff between where to build and maintain reusable (intermediate or semi-finished) data sets, and the speed to complete Final Assembly Scheduling.??My father would be proud, God rest his soul, that I remember his teaching me these things so many years ago.? They all have total relevance with D&A today.
It seems a lot of firms are struggling.??Data mesh and D&A expert consulting is not helping.??I’d recommend hiring a retired production planner from a defunct factory and put them in charge of your data and analytics product program.
Those were the big ideas this year.??The details are below.??And some of the classic images of tools and ideas explored and used in 1-1s and interactions are shown here:
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Industries:
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