According to Thomas H. Davenport, director of research at the International Institute for Analytics, senior advisor to Deloitte Analytics, and distinguished professor at Babson College, over the years, there have been three types of analytics:
- Descriptive analytics, which describe something that happened in the past.
- Predictive analytics, which predict something that will occur in the future based on past data.
- Prescriptive analytics, which offer guidance to users on actions they should take (e.g. what price to charge for a product).
Now, says Davenport, it’s time to add a fourth category: automated analytics.
As Davenport points out, analytics are increasingly becoming automated. “Instead of presenting a suggestion to a business user, as in prescriptive analytics,” writes Davenport in a recent Wall St. Journal article, “automated analytics take action based on results of the analysis. For example, changing the price automatically, displaying the best landing page automatically, determining what email to send to a customer automatically, or even steering the car automatically.”
Some forms of automated analytics have been taking place for years, notes Davenport. He points to how airlines automatically calculate price changes for seats and how banks use automated analytics to provide approval for a credit card or a loan application.
“But automated analytics are becoming progressively necessary in a world where – customers demand real-time responses, every marketing promotion must be tailored and personalized, and data is everywhere and needs to be analyzed in order to be useful,” says Davenport. “We simply don’t have enough people to analyze all the data, make all the decisions, and take all the necessary actions.”
With customers behaving at the speed of thought and businesses operating at the speed of data, it has become imperative for organizations around the world, regardless of industry, to not only recognize the value of their Big Data assets, but more importantly, have the ability to process Big Data in real time.
Why? So they have the opportunity to analyze their data—identify trends or patterns or outliers, correlate that current data with past data for context, and then take appropriate action when time is of the essence. This transition from Big Data to Fast Data will fundamentally transform how organizations operate and go to market. Business leaders will be empowered to gather, analyze, and act on huge volumes of complex data as it is being generated or as events are occurring.
The opportunities for growth, profitability and beating competition are endless!
Next Steps:
- Try Spotfire and start discovering meaningful insights in your own data.
- Learn more about Spotfire Event Analytics and see how you can leverage your real-time data for optimal business value and transformational outcomes.
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