Historically, business intelligence initiatives have focussed on providing self service dashboards and reports which are used to surface information to business stakeholders.
Typically, the data shown on these reports is over long term, strategic time horizons - showing things such as sales by quarter or customer renewals over the last week. Decisions can made from this data, but they are longer term and more strategic than operational.
Businesses are increasingly looking to move towards more operational scenarios that enable their people to see what is happening in the business right now. This turns business intelligence from a backward-looking activity into a real-time live view of your business, which can guide your employees “next best actions” in order to improve business outcomes.
Faster operational business intelligence is an improvement, but only an incremental one. Now we have this faster data infrastructure, we need to move beyond real-time dashboards and reports towards automating responses to situations before they even impact a KPI.
Some industry specific examples of this include:
- Logistics - Having identified that a certain warehouse is shipping orders late, we might wish to re-route orders to another destination until the original warehouse has caught up;
- eCommerce - Having identified that a customer is at risk of churn, we could automatically issue a customised offer to improve the prospect of retention;
- Telecommunications - Having identified that a high value customer is contacting a call centre, we could automatically prioritise their call to be answered quickly.
This is the definition of an intelligent and real time business, where we are observing the state of the world, processing data intelligently and in real time, and automating responses intelligently and automatically. Any of the above algorithms could create more business value than yet-another-dashboard which nobody ever checks.
As we mention in a recent article, reports and dashboards are really in danger of limiting the potential of data and analytics initiatives. Let's move beyond theis towards using our analytics to drive intelligent automation.