We recently completed our 2013 Predictive Analytics in the Cloud Research, and the results are clear. Predictive analytics in the cloud is becoming mainstream, with broader and accelerating adoption.
You can download the full report from the Information Management white paper library, but in this month’s column I thought I would highlight a few interesting results.
This is the second time we have done such a survey, the first was in 2011, and the most striking result is that the number of companies reporting a positive impact from predictive analytics has risen dramatically since 2011. Two-thirds of this year’s respondents have seen a positive impact from using predictive analytics in their business. There’s been similar rise in both current and planned deployment of predictive analytics in the cloud since 2011. For instance, more than 60 percent of survey respondents said they were using at least one kind of predictive analytics in the cloud — a significant increase over 2011. An astonishing 90 percent said it was likely they would have at least one class of solution widely deployed in the next few years.
The top driver for all this activity was reduced cost. Advanced analytic applications used to be very high ROI but also very high cost. The market has experienced constant pressure to deliver solutions more cost-effectively, and this is clearly driving cloud deployments of predictive analytics. Meanwhile, data security and privacy, along with regulatory and compliance concerns, remain the primary obstacles reported.
The big focus area for predictive analytics among respondents is customer engagement. In particular, customer satisfaction, customer profitability, customer retention and customer management. While the use of predictive analytics in marketing and cross-sell/up-sell is important, the clear message uncovered from the study is that customer management and engagement can be improved using predictive analytics, too.
Big data is a hot topic, and when asked about new data types, more experienced analytic teams show much higher usage than in 2011. Social media, sensor, weblog, audio and image data types are all rated as much more important in analytic models among those with successful analytic deployments. With more successful, more established teams using big data more broadly, it seems likely that there will soon be rapid and significant growth in the use of new data types in building predictive analytics. Nevertheless, more traditional structured data remains broadly central to effective predictive analytic models.
The velocity of data also matters. Predictive analytics is increasingly focused on near real-time, operational data, which grew the most in importance between 2011 and 2013. This reflects a general shift in predictive analytics from batch scoring to real-time scoring. This change is reflected in the increased use of intra-day and real-time data, though scoring streaming data is not yet a mainstream use case.
In 2011 it was clear that early adopters were going to get an edge. They were more likely to have plans for broader deployment and saw the solutions as more valuable. This trend strongly repeated in 2013. Once again, early adopters with one or more use cases deployed were significantly more likely to have plans to expand deployment. Similarly, those with experience were likely to rate the value of each scenario more highly. Exposure to predictive analytics in the cloud breeds enthusiasm. Those who buy into the promise of predictive analytics and get started like the results and want to do more. Organizations that get started have the opportunity to create differentiation from slower-moving competitors.
One last result deserves a special call out. As someone who writes about decision management and the importance of embedding predictive analytics in operational systems, I found one result especially dramatic. More than 95 percent of survey respondents who reported tightly integrating predictive analytics into operations (decision management in other words) also reported transformative or significant impact. The percentage reporting such integration has risen significantly since 2011.
Decision management, with its systematic embedding of predictive analytics into automated decision-making systems, is an ideal approach to maximize the transformative power of predictive analytics and a rapidly growing area.
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