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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 Untitledimportant 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.

vr_predanalytics_benefits_of_predictive_analytics_updatedAs 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.

Regards,

Tony Cosentino

VP & Research Director

R, the open source programming language for statistics and graphics, has now become established in academic computing and holds significant potential for businesses struggling to fill the analytics skills gap. The software industry has picked up on this potential, and the majority of business intelligence and analytics players have added an R-oriented strategy to their portfolio. In this context, it is relevant to look at some of the problems that R addresses and some of the challenges to its adoption.

As I mentioned, perhaps the most important potential for R is to address the analytic skills gap, which our research shows is a priority for vr_bigdata_obstacles_to_big_data_analytics (2)organizations. This is a serious and growing issue as more enterprises try to deal with the huge volumes of data they accumulate now, which continue to increase. Our benchmark research on big data identifies the biggest challenges to implementing big data as staffing (cited by 79% of organizations) and training (77%). Since R is a widely used statistical language used in academia today, current and future graduates may well help fill this gap with what they learned.

Another challenge facing companies is the lack of usability of advanced analytic languages and tools. Across our research, usability is rising in importance in just about every category. Analytical programming is not something the information consumer or the knowledge worker can do, as I outlined in a recent analysis on personas in business analytics, but those in the analyst community can readily learn the R language. R’s object-orientation is often put forth as providing an intuitive language that is easier to learn than conventional systems; this starts to explain its massive following, which already numbers in the millions of users.

On another front, R addresses the need for analytics to be part of larger analytic workflows. It is easier to embed into applications than other statistical languages, and unlike embeddable approaches such as Python, R does not require users to pull together a variety of elements to address a particular statistical problem. Fundamentally, R is more mature than Python from an algorithmic point of view, and its terminology is oriented more to the statistical user than the computer programmer.

Perhaps the broadest opportunity for R is to address new usevr_predanalytics_predictive_analytics_obstacles cases and the creation of innovative analytical assets for companies. The fact that it is open source means that each time a new analytical process is developed, it is released and tested almost immediately if submitted through the R project community. Furthermore, R does a nice job of using a diverse set of data which is an important part of doing predictive analytics on big data in today’s highly distributed environments. As I often mention, new information sources are more important than tools and techniques. R does not directly address the largest obstacle found in our predictive analytics research that over half (55%) of organizations found which is difficulty integrating into information architecture that is the ad-hoc or needs to be automated to support the integration of data to support the analytic processes.

Last, but not least in terms of opportunities, R addresses the cost pressures that face business users and IT professionals alike. Some might argue that R is free in the way a puppy is free (requiring lots of effort after adoption), but in the context of an organization’s ability to bootstrap an analytic initiative, low startup cost is a critical element. With online courses and a robust community available, analysts can get up to speed quickly and begin to add value with little direct investment from their employers.

Despite all these positive aspects, there are others holding back adoption of R in the enterprise. The downside of being free is the perceived lack of support for enterprises that commit to an open source application. This can be a particularly high barrier in industries with established analytic agendas, such as some areas of banking, consumer products, and Pharmaceutical companies. (Ironically, these industries are some of the biggest innovators with R in other parts of their business.)

And we must note that ease of use for R still seems to stop with an experienced analyst used to a coding paradigm. No graphical user environment such as SPSS Modeler or SAS Data Miner has emerged yet as a standard approach for R, thus the level of user sophistication has to be higher and analytical processes are more difficult to troubleshoot. We have seen offerings that are maturing rapidly that I have already covered as stand-alone tools like Revolution Analytics and also embedded within business intelligence tools like Information Builders.

Finally, the scalability of R is limited to what is loaded into memory. How large the data sets being analyzed can go is a matter of debate; one LinkedIn group discussion claimed that an R analytic data set can scale to a terabytes in-memory, while my discussions with users suggest that large production implementations are not viable without parallelizing the code in some sort of distributed architecture. Generally speaking, as analytic data sets get into the terabyte range, parallelization is necessary.

vr_predanalytics_benifits_of_predictive_analyticsIn my next analysis, I will look at some of the industries and companies that are using R to achieve competitive advantage, which according to our benchmark research into predictive analytics is the number one biggest benefit of predictive analytics for more than two-thirds of companies. I will also highlight more updated on how enterprise software vendors’ strategies and where they are incorporating R into their software portfolios.

Regards,

Tony Cosentino

VP and Research Director

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