Embedding advanced analytics into business intelligence (BI) and changing enterprise mindsets can revolutionise business's ability to adapt and respond, says SAS Institute.
Craig Stephens, principle solution manager - Information Management Practice at SAS Institute South Africa, says to enable decisions that drive business benefits, traditional BI mindsets must be turned on their head.
"Some years ago, a key requirement for BI was the ability to work offline, unconnected to a network to retrieve data. That requirement has shifted completely, and now the primary requirement of a BI solution is to be mobile and connected. Now the enterprise needs the latest, most relevant information immediately to hand, so now the need is to be always connected with access to analytics to support better decisions instantly," says Stephens.
As requirements for BI change, Gartner reports that companies are increasingly embedding traditional reporting, dashboards, interactive analysis, and prescriptive analytics into business processes or applications. "Locally, embedded analytics for forecasting is generating significant interest. Our data visualisation product, SAS Visual Analytics, which contains embedded advanced analytics, was relaunched in South Africa two-and-a-half years ago and is outselling all our other products by unit count," he says.
Embedded advanced analytics enables instant decision-making in scenarios that previously may have taken hours or even days. Risk assessments, customer value determination and customer behaviour forecasts can be made on the fly at a contact centre or point of sale, supporting sales and customer retention while reducing risk, for example.
In addition to embedding advanced analytics within BI, the mindset around the role of analytics in BI needs to change, says Stephens. "Instead of turning to analytics as a last step, it needs to become the first," he says. "This requires a complete mindset shift."
Stephens cites as an example a segment manager within a telco who notices many small prepaid customers have stopped recharging their airtime. "In a traditional approach, he would notice customers leaving, but would first seek a churn report, then throw the problem to a traditional analyst who would go through a traditional BI approach, do ad hoc analysis of the historical, and finally, look to analytics." This is time-consuming and reactive.
"In the new approach, the segment manager might first use advanced analytics to profile those customers who are leaving, predict which customers have a similar profile and might also leave, and create a proactive SMS campaign to retain those customers. This is far faster and more cost-effective."
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