Helping figure out the people, process, management, and governance of #Data & #Analytics where the majority go wrong before even getting to the technical stuff.
Even with the best #Data requirements, problems happen. There is no such thing as perfect data. Not every issue makes data unsalvageable, and it is up to the #Analytics practitioner to figure out how to work effectively with the data. #Statistics#DataScience#MachineLearning#AI
When asked about something you've already told them, we are annoyed. #Analytics practitioners, of all people, should understand the importance of interrogating existing #Data. We do that for a living! But I get it all the time from them. #Statistics#DataScience#AI#BI
You can provide #Analytics practitioners with instructions on how to do something. They may think they are learning. But it does not mean they understand. Often, they are unable to extend it even to closely adjacent situations. I see this a lot. #Data#Statistics#DataScience
Technical knowledge is straightforward to assess. But many hiring managers, some themselves #Analytics practitioners, do not know what they need or what makes an analytics practitioner effective, let alone how to evaluate candidates for it. #Statistics#DataScience#AI#Data
With or without automation, a compliance-first approach to #DataGovernance is harder and slower to react in practice. It ends in a bureaucracy because people have many hoops to jump through just to get simple things done, and the response is slow. #Data#DataManagement#Analytics
Regulations are always reactive. As an extreme example, I remember a case in which a politician personally experienced something he did not like from #Analytics and went on to draft a regulation that probably did more damage than solve problems. #Data#DataGovernance