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Some followers of Ventana Research may recall my work here several years ago. Here and elsewhere I have spent most of my career in the data and analytics markets matching user requirements with technologies to meet those needs. I’m happy to be returning to Ventana Research to resume investigating ways in which organizations can make the most of their data to improve their business processes; for a first look, please see our 2016 research agenda on Big Data and Information Optimization. I relish the opportunity to conduct primary market research in the form of Ventana’s well-known benchmark research and to help end users and vendors apply the information collected in those studies.

Much has happened since I was previously part of thevr_Big_Data_Analytics_18_big_data_technology_in_use Ventana Research team. One major change is the explosive growth in the use and acceptance of big data. For example, when I conducted the first benchmark research on big data, only 22 percent of participants were using Hadoop in production. Our more recent research shows more than 50 percent growth in the number of respondents using Hadoop in production (which is now 37%).

In the area of predictive analytics, another research study from my prior tenure identified a skills shortage. This shortage was identified in several ways. More than four out of five (83%) participants indicated that users did not have enough skills training, and more than half (58%) said they didn’t understand the mathematics needed to produce their own analyses. In the interim the numerous university programs have begun to help address this shortage. A Google search shows that New York University, Columbia, Indiana University, Wesleyan, University of Washington, University of Michigan, University of Rochester and University of Texas San Antonio have created data science programs – and this is just page one of the search results. I anticipate that these will be increasingly popular programs as the rise of big data will continue to drive demand for these skills. For the time being, our more recent study on predictive analytics suggests that these skill shortages still exist with very similar responses of 79 percent and 66 percent, respectively. I’ll be continuing to watch these and other analytics issues noted in our 2016 Business Analytics Research Agenda.

vr_NG_Predictive_Analytics_16_why_users_dont_produce_predictive_analysesWe have also seen a sea change in the acceptance of open source software in enterprises. I think it is fair to say that open source helped drive the growth in big data with various projects including Cassandra, Hadoop, MongoDB and Spark enabling organizations to experiment with large volumes of data before making significant license purchases to put those systems into production. The open source momentum is further evidenced by some large vendors taking formerly proprietary, “closed source” technologies and making them open source. Perhaps the biggest example is Microsoft making its .NET technology open source. My former employer Pivotal also converted its data management products, in which it had invested more than 10 years of proprietary development, to open source versions.

Another notable change is the growth of interest in the Internet of Things (IoT). Many years ago I considered a position with a vendor that helped organizations manage RFID data. Adoption was slow at the time, in part because of the cost of RFID tags but also because of the cost and challenges of collecting and analyzing very large volumes of data. As big data technologies have grown, so too has interest in IoT. Technologies exist today to make processing such large amounts of data possible in the time frames and at costs that make it practical to consider how instrumentation of devices can be used to enhance business performance. We’ll be undertaking specific research on this topic in 2016: See our Big Data and Information Optimization Research Agenda.

If he were alive today, Charles Darwin might have noted the emergence of a new species: the Unicorn, which Wikipedia defines as a startup company, often software-based, whose valuation exceeds US$1 billion. You might wonder how this financial trend impacts our research and the advice we provide. The answer is that such valuations have the potential to alter the behavior of the markets we cover. They provide these startup vendors access to funding great enough to change the competitive landscape. Such investments can put pressure on existing vendors to step up their game. In some cases it can also cause consolidation in the market or even cause certain vendors to exit markets, such as Intel did when it decided to get out of the Hadoop distribution market. At Ventana Research we are ready to help end-user organizations evaluate whether the unicorns are ready for prime time and how their existence might impact their existing software vendors. One way in which we help in this process is with our Ventana Research Value Indexes, which provide fact-based assessments of various software products within a variety of market segments.

So I hope you’ll pardon the interruption in our conversation. It’s good to be back, and I am looking forward to working with the entire Ventana Research team to provide research and insights that will help guide your use of technology to improve your business decisions and processes.

Regards,

David Menninger

SVP & Research Director

As a technology, predictive analytics has existed for years, but adoption has not been widespread among businesses. In our recent benchmark research on business analytics among more than 2,600 organizations, predictive analytics ranked only 10th among technologies they use to gene­rate analytics, and only one in eight of those companies use it. Predictive analytics has been costly to acquire, and while enterprises in a few vertical industries and specific lines of business have been willing to invest large sums in it, they constitute only a fraction of the organizations that could benefit from them. Ventana Research has just completed a benchmark re­search project to learn about how the organizations that have adopted predictive analytics are using it and to ac­quire real-world information about their levels of maturity, trends and best practices. In this post I want to share some of the key findings from our research.

As I have noted, varieties of predictive analytics are on the rise. The huge volumes of data that organizations accumulate are driving some of this interest. Our Hadoop research highlights the intersection of this big data and predictive analytics: More than two-thirds (69%) of Hadoop users perform advanced analytics such as data mining. Regardless of the reasons for the rise, our new research confirms the importance of predictive analytics. Participants overwhelmingly reported that these capabilities are important or very important to their organization (86%) and that they plan to deploy more predictive analytics (94%). One reason for the importance assigned to predictive analytics is that most organizations apply it to core functions that produce revenue. Marketing and sales are the most common of those. The top five sources of data tapped for predictive analytics also relate directly to revenue: customer, marketing, product, sales and financial.

Although participants are using predictive analytics for important purposes and are generally positive about the experience, they do not minimize its complexities. While now usable by more types of people, this technology still requires special skills to design and deploy, and in half of organizations the users of it don’t have them. Having worked for two different vendors in the predictive analytics space, I personally can testify that the mathematics of it requires special training. Our research bears this out. For example, 58 percent don’t understand the mathematics required. Although not a math major, I had always been analytically oriented, but to get involved in predictive analytics I had to learn new concepts or new ways to apply concepts I knew.

Organizations can overcome these issues with training and support. Unfortunately, most are not doing an adequate job in these areas. Not half (44%) said their training in predictive analytics concepts and techniques is adequate, and fewer than one-fourth (24%) provide adequate help desk resources. These are important places to invest because organizations that do an adequate job in these two areas have the highest levels of satisfaction with their use of predictive analytics; 89% of them are satisfied vs. 66% overall. But we note that product training is not the most important type. That also correlated to higher levels of satisfaction, but training in concepts and the application of those concepts to business problems showed stronger correlation.

Timeliness of results also has an impact on satisfaction. Organizations that use real-time scoring of records occasionally or regularly are more satisfied than those that use real-time scoring infrequently or not at all. Our research also shows that organizations need to update their models more frequently. Almost four in 10 update their models quarterly or less frequently, and they are less satisfied with their predictive analytics projects than those who update more frequently. In some ways model updates represent the “last mile” of the predictive analytics process. To be fully effective, organizations need to build predictive analytics into ongoing business processes so the results can be used in real time. Using models that aren’t up to date undermines the whole effort.

Thanks to our sponsors, IBM and Alpine Data Labs, for helping to make this research available. And thanks to our media sponsors, Information ManagementKD Nuggets and TechTarget, for helping in gaining participants and promoting the research and educating the market. I encourage you to explore these results in more detail to help ensure your organization maximizes the value of its predictive analytics efforts.

Regards,

David Menninger – VP & Research Director

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