Narrator: To know data, you must know the grammar of data, information and knowledge. Depending on the language and its precision, accuracy, and richness, we will have a reasonable idea of approaching data and information modelling issues. To know data and information, you must understand the business well. People with a technical background and little or no business knowledge are often oblivious to their ignorance regarding business data and information. When you have a technician in the industry who doesn’t understand this, it’s a problem. If your technician is located thousands of kilometres from your company, it’s a disaster waiting to happen. Let’s listen to Pete and Dud wax lyrical about the importance of language and grammar.
Narrator: In past times of crisis, we depended more on who we considered to be experts. These days, rightly or wrongly, we have become more cynical about experts and their expert analysis and advice.
An old Russian proverb urges us “to trust and verify.”
Just because you can say it doesn’t mean that you can do it
Martyn Jones, New York, 25th September 2024
Narrator: There is a fascinating famous saying, “Just because you can write something, draw something, or say something, doesn’t mean that you can do it”, that I think could be further explored in the context of data and analytics. It seems to go hand in hand with expressions such as “It must be true because I read it on the internet”. Also, it can be pretty surprising how many businesses expect their people to lie where it’s practical and commercially beneficial.
Narrator: Consider this. If the originators, acolytes and sycophants of data mesh hadn’t been so free and easy with how they created and perpetrated technical debt, especially data model and data quality-related technical debt, then we wouldn’t need data mesh to try and get us out of the mess these sloppy developer people put us into in the first place.
Narrator: It doesn’t matter what you are trying to do, or even if you know what you are trying to do, but if in doubt, blame the quality of the data. There was a time when data quality was a massive impediment to business data integration.
Data quality assurance was a glint in IT’s eye. A far-away place that nobody knew how to get to.