Broadly speaking all Business Analytics serves one of two goals: decision support or decision automation. One way to idealise these is as either reports (decision support) or algorithms (decision automation).
Algorithms reduce the need for humans to think. Picture the in-database credit scoring function embedded deep in your bank’s systems and firing thousands of times an hour. This kind of decision automation (or decision replacement) is a common operational analytics endpoint.
Reports, on the other hand, make decisions more difficult. The simplest decision support system is a coin toss, but a business relying only on heads and tails will not survive for long. Real decision support adds ambiguity, complexity, uncertainty, and necessitates human judgement. This makes decisions harder, not easier.
If you enjoyed this article, please consider sharing it!
3 Responses to Decision support versus decision automation
Leave a Reply
Follow Us!
[...] Decision support versus decision automation [...]
[...] focus on operational analytics (e.g. “dynamic dashboards using real-time data”) and decision automation. I see this as a shortcoming of the McKinsey report and as more understandable in IBM’s case [...]
[...] automation. It recognises that decision support leads to higher value decisions but in practice makes decision making harder, not easier. As a recent Analyst First post argued, new information is not always “actionable”: Comey [...]