Our AMEX credit card was recently compromised. Someone got hold of the card information and Petro Canada charges started to rack up. Amex spotted this suspicious pattern and immediately initiated a fraud alert thru multiple touch points.
What does your credit card company know about you? A lot…maybe more than your spouse. A study of how customers of Canadian Tire were using the company’s credit cards found that 2200 of 100,000 cardholders who used their card at drinking places missed four payments within the next 12 months. By contrast, only 530 of the cardholders who used their card at the dentist missed four payments within the next 12 months. So drinking is a predictor of credit risk.
Predictive analytics is not a fad. It’s not a trend. In a real-time world, Analytics is a core business requirement/capability. However, many organizations flounder in their efforts not because they lack analytics capability but because they lack clear objectives. So the first question is, What do you want to achieve?
Analytics so far has largely been a departmental ad hoc activity. Even at the most sophisticated corporations, data analytics is a cumbersome affair. Information accumulates in “data warehouses,” and if a user had a question about some trend, they request “data priests/analysts” to tease the answers out of their costly, fragile systems. This resulted in a situation where the analytics are done looking in the rearview mirror, hypothesis testing to find out what happened six months ago.
Today it’s possible to gather huge volumes of data and analyze it in near real-time speed. A retailer such as Macy’s that once pored over last season’s sales information could shift to looking instantly at how an e-mail coupon impacts sales in different regions. Moving to a realtime model and also building an enterprise level “shared services” model is going to be the next big wave of activity.