For some of us having started in analytics in or before “Decision Support” which was the predecessor to Business Intelligence, with AI, ML, and advanced analytics we are now moving into “Decision Intelligence”. This is a smooth hybrid of terms.
This is now a cross discipline filed which looks across all areas of analytics and statistics integrating technology to calculate (hopefully repeatedly) predictions and other outputs that turn loads of data into information. The width of knowledge and the depth of skillset almost makes it to broad for one or even a handful of people to tackle in a medium or large organization. Especially in a large organization where disparate business areas are fundamentally separated on cause and initiative not to mention budget and accountability demands. With data science attempting to be central in an organization it is still fundamentally lopsided due to constraints on well defined purpose or initiative and lack of data accessibility or skillset.
As we look forward in moving to a more democratized Decision Intelligence team, we’ll be addressing some ways to mitigate the cold starts of AI, data wrangling with lack of data, and interpretation of results which add business value not those that subtract from perceived benefit of establishing a Data Intelligence team in the first place.
