Research Uncovers Keys to Using Predictive Analytics

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 Management, KD 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

Business Intelligence Revolution and Research Agenda for 2012

For most people involved with business intelligence (BI), these are exciting times. Using BI to improve business processes continues to motivate organizations to invest in BI. The focus on BI also empowers business analytics and can be rented in the cloud computing model of accessing software. New technologies are adding dimensions to BI and creating both excitement and confusion for enterprises implementing them. We offer a variety of accomplished research that can help organizations overcome the hype and understand how to use these technologies to improve business decision-making, and we’re planning new research in 2012 on these topics.

Big data continues to be one of hottest topics in the market. Every vendor wants to claim it has a solution for managing it. Multiple approaches exist to tackle the proliferation of huge volumes of data at accelerated paces, which we addressed recently in our Big Data benchmark research. We also see the pace of business forcing organizations to analyze data more frequently; one-fourth of research participants now analyze data hourly or even more often. In 2012 we will continue our research by exploring specific vendor offerings in big-data analytics. We’ll also be conducting new benchmark research into operational intelligence to explore how streaming data and event-based data impact organizations.

The march to add mobile capabilities across smart-phones and tablets to business intelligence is becoming a stampede. Nearly every vendor I cover has added such capabilities or significantly enhanced those it already has. However, questions remain on the best ways to utilize mobile capabilities and which parts of the organization really need them. Our upcoming Next-Generation BI benchmark research will look at how businesses are utilizing mobile BI or intend to. In addition, it will examine how collaborative technologies are influencing BI processes and organizational decision-making. We’ve already seen the consumerization of collaborative BI. This research will explore how well businesses understand the intersection of collaborative technologies and social media with BI. Vendors such as QlikTech, Tibco Spotfire and Yellowfin recently enhanced their products with collaborative capabilities joining others like IBM and SAP who have advanced in collaboration capabilities with BI. As you consider your organization’s requirements, the best practices identified in this research will help you plan to incorporate these capabilities into your business processes.

We also continue to explore the role of analytics, closely associated with BI. Next week we will share the results of our Predictive Analytics benchmark research Our published business analytics research shows only one-quarter of organizations using planning and forecasting and only 13 percent using predictive analytics. Yet nearly 80 percent said both of these capabilities are important or very important. Organizations need to address that gap to enhance their business decision-making abilities.

Because business intelligence also is closely entwined with information management, I encourage you to review our Information Management research agenda as well. Almost one-third of organizations have more than 10 sources of data they have to integrate, and more than two-thirds spend more time preparing data than analyzing it. Without addressing these issues, no organization is likely to realize the full potential of business intelligence.

Business intelligence continues to be a strategic business imperative. Our research will deliver education and best practices that can help you reduce the costs, time and risk of making the wrong choices or being uninformed of this strategic imperative.

Regards,

David Menninger – VP & Research Director