Enterprise applications began as systems of record. They stored what happened in the past and were the official record of business transactions. They answered questions like: What are my orders? What are my opportunities? Who are my prospects? What assets are in my client’s account? What procedures did the patient receive in the hospital? Who are my employees?
Then the next generation of applications was planning applications. They allowed the enterprise to generate plans based on forecasts. Usually, these forecasts just projected what was most likely to happen based on previous periods, but did not sufficiently adapt to changing business conditions. These plans were typically generated annually, monthly, and in best cases, weekly. Eventually, these systems were improved to also react to exogenous events and replan. Then business intelligence tools and data warehouses emerged, enabling organizations to analyze data coming from their systems of record to derive insights. These systems answered questions like: Who are my most profitable customers?
Recently, with the maturation of scale-out architectures that enable petabyte scale analysis and widespread adoption of Machine Learning methods – we are seeing an explosion of predictive analytics. In Forbes, Louis Columbus reports that, “By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics.” These systems use predictive algorithms to analyze historical data to predict possible futures and answer questions like: What customers are likely to churn? What offers are the most effective?
The New Generation of Applications: Predictive Applications
Now, the new generation of applications naturally evolving puts all of these capabilities together. Predictive applications continuously monitor conditions to predict what could happen, and then enable you to react accordingly. These applications are in a learn-predict-plan-and-act cycle. They are continuously monitoring changing conditions and adapting to them. They are more real-time. They use data to learn not only from the past, but also find patterns leading up to events, and then apply these patterns to predict an upcoming event.
Predictive applications don’t stop at anticipating events; they help you react to them. They allow you to project what the world would be like if anticipated events occur, so that you can plan around them. They don’t just plan on a regular schedule; they can plan very quickly when they need to react to unexpected events. And these reactions are not limited to events that occurred, but also to events that are likely to occur.
But it does not stop here. The world is constantly changing. Even if you built the best predictive application, in a year, a month, or even in some cases in an hour, something will change, and you will want to be ready.