Our benchmark research consistently shows that business analytics is the most significant technology trend in business today and acquiring effective predictive analytics is organizations’ top priority for analytics. It enables them to look forward rather than backward and, participate organizations reported, leads to competitive advantage and operational efficiencies.
In our benchmark research on big data analytics, for example, 64 percent of organizations ranked predictive analytics as the most important analytics category for working with big data. Yet a majority indicated that they do not have enough experience in applying predictive analytics to business problems and lack training on the tools themselves.
Predictive analytics improves an organization’s ability to understand potential future outcomes of variables that matter. Its results enable an organization to decide correct courses of action in key areas of the business. Predictive analytics can enhance the people, process, information and technology components of an organization’s future performance.
In our most recent research on this topic, more than half (58%) of participants indicated that predictive analytics is very important to their organization, but only one in five said they are very satisfied with their use of those analytics. Furthermore, our research found that implementing predictive analysis would have a transformational impact in one-third of organizations and a significant positive impact in more than half of other ones.
In our new research project, The Next Generation of Predictive Analytics, we will revisit predictive analysis with an eye to determining how attitudes toward it have changed, along with its current and planned use, and its importance in business. There are significant changes in this area, including where, how, why, and when predictive analytics are applied. We expect to find changes not only in forecasting and analyzing customer churn but also in operational use at the front lines of the organization and in improving the analytic process itself. The research will also look at the progress of emerging statistical languages such as R and Python, which I have written about.
As does big data analytics, predictive analytics involves sourcing data, creating models, deploying them and managing them to understand when an analytic model has become stale and ought to be revised or replaced. It should be obvious that only the most technically advanced users will be familiar with all this, so to achieve broad adoption, predictive analytics products must mask the complexity and be easy to use. Our research will determine the extent to which usability and manageability are being built into product offerings.
The promise of predictive analytics, including competitive advantage (68%), new revenue opportunities (55%), and increased profitability (52%), is significant. But to realize the advantages of predictive analytics, companies must transform how they work. In terms of people and processes a more collaborative strategy may be necessary. Analysts need tools and skills in order to use predictive analytics effectively. A new generation of technology is also becoming available where predictive analytics are easier to apply and use, along with deploy into line of business processes. This will help organizations significantly as there are not enough data scientists and specially trained professionals in predictive analytics that will be available for organizations to utilize or afford to hire.
This benchmark research will look closely at the evolving use of predictive analytics to establish how it equips business to make decisions based on likely futures, not just the past.