Over the past several years, companies have moved from simply asking where to get the data from to support their decisions, to asking how they are going to leverage it to create actionable insights.
Predictive analytics use statistical or machine-learning techniques to analyze current and historical facts, and find relationships and patterns that can be used to predict future events. A basic example of its application is in retail banking. Banks can use predictive models to look at customers’ spending patterns. They can then use this information to predict their financial and life events, and send them more relevant offers, make a decision as to whether to give them a loan, and so forth.
By using predictive analytics, it is possible for a company to make better and faster decisions, and at a lower cost - either by using them to supplement human decision making processes, or replacing them entirely.
In decision making, the more that you know about the likely outcome, the more confident you can be that you decision is the right one. Predictive analytics allows you to get an idea of every possible eventuality, so you can weigh up the risks and the potential return on investment (ROI). It enables you to see every factor that could play a role in the outcome, including some that you may not have thought relevant.
Another advantage of predictive analytics is that it helps to remove politics from the decision making process. When making a big decision that has a company-wide impact, there are normally a number of departments with vested interests involved, often relying on different experiences and knowledge bases that may run contrary to those of others. Using predictive analytics, it is possible to gain a single version of the truth that can override such ulterior motives.
Awareness of predictive analytics is growing, and companies that have adopted the methods are reporting success and seeing increased ROIs. However, according to Wayne Eckerson’s 2014 study, Making Predictive Analytics Pervasive, the number of organizations that reported successfully implementing them actually dropped from 21% to 18% in 2014. One aid that could see this change is the new IBM Industry Analytics Solutions, which is designed to provide interactive and role-specific dashboards that business users can share predictive insights on. These are visible across teams and organizations, increasing the speed at which decisions are made.
Another useful tool in optimizing predictive analytics to produce better choices is decision-modeling. Decision-Modelling is the structure of the decision-making involved in a business scenario that has to be made repeatedly, such as pricing deals. It breaks the process down and identifies what information and knowledge was required and where. It is also possible to link decision requirements models to KPIs and metrics, making it apparent which metrics will improve if the decision making does. By doing this, it’s possible to see how predictive analytics can add value, and how ROI can be measured later.