They bring their answer to the higher-ups, who, nonplussed, ask them about the number of those same cases in which a paramedic was on board. The plumbing starts up again. Six months later, they return with their answer – and are asked, in how many cases was a defibrillator on board? Right back to the plumbing. Another six months, another question from the higher-ups: Was the paramedic on board trained to use the defibrillator? This time, no plumbing:
This project – and the many others like it that are undertaken every day – was led by developers as opposed to data scientists. It was not business-centric, nor did it deploy the kinds of knowledge graph thailand whatsapp number data architectures discussed above. What it points to is the importance of creating a proper data culture, of leveraging your data stewards, modeling your data correctly, and fighting with IT when business objectives are on the line.
The ultimate objective here is fluidity: You want to be able to rewash your data at any time in line with shifting business requirements, to answer new questions as quickly as possible without necessarily having to get IT involved. Internal data, leveraged properly, can be a fantastic source of growth and revenue, but only when you have a 360-degree view of your data. That is what the data mesh and the data fabric – brought alive through knowledge graph architectures – can offer.
This is not data quality.