Pre-sales consultant

In my previous article, I introduced the concept of augmented analytics that unites the best of machine intelligence and human intuition with the goal of speeding up the process of insight generation. Today’s machine intelligence uses a variety of techniques to automatically generate analyses and insights in visual and narrative form, based on user selections, natural language search and advanced analysis types.

Are you suffering from artificial intelligence (AI) fatigue? Like the words of the song, “every move you make, every step you take, I’ll be watching you,” AI pervades all our activities, from work to leisure, analysing data on every action, from a simple grocery shop to clothing preferences. While expectations are high, the reality is that true value is not always realised from AI in analytics at this stage of the game.

A recent conversation on Power BI touched on the subject of data modelling; namely, why would you model data for a wider business need than the report you happen to be building? What are the pros and cons of that approach, and which one outweighs the other? In true analytics terms, ‘data modelling’ is also known as ‘dimensional modelling’, but during this blog, ‘data modelling’ is the term we will use.