The other night my wife asked me what I wanted for desert. She works really hard, so my first instinct was to say something that was easy to make and good for me. Then I realized that I might as well say nothing and eat a carrot. Or, I could shoot for the moon and tell her what I really wanted and promise to clean the garage at some point.
Since my birthday was coming up I decided to eat the carrot now, but ask for a special homemade cake with cherries, chocolate in 4 layers mixed with fluffy white stuff and ice cream on top. Oh ya, I almost forgot, it had ribbons of carmel throughout. My favorite. I knew this cake was not your ordinary run of the mill cake and took quite a lot of effort to prepare, but the selfish birthday boy in me did not care. Before I could begin to ask her, she took one look at me and said, “You want the same cake I made you last year don’t you?” Yes!
For me, the whole experience was a mouthwatering, sweet, cottony piece of Valhalla lashing of the tongue, which took about 2 minutes to eat. For her, on the other hand, it was a day of preparation starting with raw ingredients, recipes from the old country, the finest assortment of cooking paraphernalia, and years of experience in timings, mixing, ordering, temperature management, and some secret stuff I will never understand. In the end, she worked tirelessly for me and my birthday and I got to enjoy, once again, the cake of all cakes.
Understanding Data is like understanding how to make a good cake. It is not just about the multiple layers associated with process; it is about the blending the right ingredients to make the right cake. For instance, if she went through all this trouble and never asked me what I liked in a cake, the whole process would have been a waste, as I would have politely consumed a few bites and never asked for it again. However, because she spent a lot of time understanding the core ingredients and how they worked together, she could predict there was a good chance I would like the result.
In data parlance, this is equivalent to Information Value Management® (DATUM's Data Governance & Stewardship Solution) 4 layers and the 12 elements within the platform that blend everything together:
1. Discovery Layer: Search, Discovery, Lineage
- Do I have the right ingredients for my cake and where did this recipe come from? Is it the right cake?
2. Metadata Management Layer: Dictionaries, Glossary, Catalog
- What’s my recipe, cookbook, and how do those ingredients relate together? Does butter go with lime juice?
3. Stewardship Layer: Rules, Issues, Standards
- Which items do I mix first? Does that matter? What temperature do I cook at and how long do I set the pan out before adding another layer.
4. Governance Layer: Processes, Objectives, Metrics
- Who are the people eating my cake? Will they all like it? Do they have allergies? Will this cake make them happy and want to clean up the garage. How will I know if they like it?
Through 2021, successful CDOs will be spending more than 70% of their time on driving new solutions1 (or building cakes that people will eat). It’s a changing world we live in and we can no longer rely on a Safeway cake to sooth the palate or activate us to clean the garage. We must understand the evolving dimensions that are now upon us to consume and relate to the big data out there. Like building a cake full of layers, understanding all the elements involved will ensure our success with the right level of effort.
Feeling satisfied after eating the cake and a little guilty about all the work my wife put in, I spent the next day cleaning the entire garage. It wasn’t until years later she told me that the cake only took her 20 minutes to make the second time around.
1. Gartner's 2nd Annual CDO Survey, 2016