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The Performance Index analysis we performed as part of our next-generation predictive analytics benchmark research shows that only one in four organizations, those functioning at the highest Innovative level of performance, can use predictive analytics to compete effectively against others that use this technology less well. We analyze performance in detail in four dimensions (People, Process, Information and Technology), and for predictive analytics we find that organizations perform best in the Technology dimension, with 38 percent reaching the top Innovative level. This is often the case in our analyses, as organizations initially perform better in the details of selectingvr_NG_Predictive_Analytics_performance_06_dimensions and managing new tools than in the other dimensions. Predictive analytics is not a new technology per se, but the difference is that it is becoming more common in business units, as I have written.

In contrast to organizations’ performance in the Technology dimension, only 10 percent reach the Innovative level in People and only 11 percent in Process. This disparity uncovered by the research analysis suggests there is value in focusing on the skills that are used to design and deploy predictive analytics. In particular, we found that one of the two most-often cited reasons why participants are not fully satisfied with the organization’s use of predictive analytics is that there are not enough skilled resources (cited by 62%). In addition, 29 percent said that the need for too much training or customized skills is a barrier to changing their predictive analytics.

The challenge for many organizations is to find the combination of domain knowledge, statistical and mathematical knowledge, and technical knowledge that it needs to be able to integrate predictive analytics into other technology systems and into operations in the lines of business, which I also have discussed. The need for technical knowledge is evident in the research findings on the jobs held by individual participants: Three out of four require technical sophistication. More than one-third (35%) are data scientists who have a deep understanding of predictive analytics and its use as well as of data-related technology; one-fourth are data analysts who understand the organization’s data and systems but have limited knowledge of predictive analytics; and 16 percent described themselves as predictive analytics experts who have a deep understanding of this topic but not of technology in general. The research also finds that those most often primarily responsible for designing and deploying predictive analytics are data scientists (in 31% of organizations) or members of the business intelligence and data warehouse team (27%). This focus on business intelligence and data warehousing vr_NG_Predictive_Analytics_16_why_users_dont_produce_predictive_analysesrepresents a shift toward integrating predictive analytics with other technologies and indicates a need to scale predictive analytics across the organization.

In only about half (52%) of organizations are the people who design and deploy predictive analytics the same people who utilize the output of these processes. The most common reasons cited by research participants that users of predictive analytics don’t produce their own analyses are that they don’t have enough skills training (79%) and don’t understand the mathematics involved (66%). The research also finds evidence that skills training pays off: Fully half of those who said they received adequate training in applying predictive analytics to business problems also said they are very satisfied with their predictive analytics; percentages dropped precipitously for those who said the training was somewhat adequate (8%) and inadequate (6%). It is clear that professionals trained in both business and technology are necessary for an organization to successfully understand, deploy and use predictive analytics.

To determine the technical skills and training necessary for predictive analytics, it is important to understand which languages and libraries are used. The research shows that the most common are SQL (used by 67% of organizations) and Microsoft Excel (64%), with which many people are familiar and which are relatively easy to use. The three next-most commonly used are much more sophisticated: the open source language R (by 58%), Java (42%) and Python (36%). Overall, many languages are in use: Three out of five organizations use four or more of them. This array reflects the diversity of approaches to predictive analytics. Organizations must assess what languages make sense for their uses, and vendors must support many languages for predictive analytics to meet the demands of all customers.

The research thus makes clear that organizations must pay attention to a variety of skills and how to combine them with technology to ensure success in using predictive analytics. Not all the skills necessary in an analytics-driven organization can be combined in one person, as I discussed in my analysis of analytic personas. We recommend that as organizations focus on the skills discussed above, they consider creating cross-functional teams from both business and technology groups.


Ventana Research

To impact business success, Ventana Research recommends viewing predictive analytics as a business investment rather than an IT investment.  Our recent benchmark research into next-generation predictive analytics  reveals that since our previous research on the topic in 2012, funding has shifted from general business budgets (previously 44%) to line of business IT budgets (previously 19%). Now more than vr_NG_Predictive_Analytics_15_preferences_in_purchasing_predictive_analy.._  half of organizations fund such projects from business budgets: 29 percent from general business budgets and 27 percent from a line of business IT budget. This shift in buying reflects the mainstreaming of predictive analytics in organizations,  which I recently wrote about .

This shift in funding of initiatives coincides with a change in the preferred format for predictive analytics. The research reveals that 15 percent fewer organizations prefer to purchase predictive analytics as stand-alone technology today than did in the previous research (29% now vs. 44% then). Instead we find growing demand for predictive analytics tools that can be integrated with operational environments such as business intelligence or transaction applications. More than two in five (43%) organizations now prefer predictive analytics embedded in other technologies. This integration can help businesses respond faster to market opportunities and competitive threats without having to switch applications.

  vr_NG_Predictive_Analytics_14_considerations_in_evaluating_predictive_an.._ The features most often sought in predictive analytics products further confirm business interest. Usability (very important to 67%) and capability (59%) are the top buying criteria, followed by reliability (52%) and manageability (49%). This is consistent with the priorities of organizations three years ago with one important exception: Manageability was one of the two least important criteria then (33%) but today is nearly tied with reliability for third place. This change makes sense in light of a broader use of predictive analytics and the need to manage an increasing variety of models and input variables.

Further, as a business investment predictive analytics is most often used in front-office functions, but the research shows that IT and operations are closely associated with these functions. The top four areas of predictive analytics use are marketing (48%), operations (44%), IT (40%) and sales (38%). In the previous research operations ranked much lower on the list.

To select the most useful product, organizations must understand where IT and business buyers agree and disagree on what matters. The research shows that they agree closely on how to deploy the tools: Both expressed a greater preference to deploy on-premises (business 53%, IT 55%) but also agree in the number of those who prefer it on demand through cloud computing (business 22%, IT 23%). More than 90 percent on both sides said the organization plans to deploy more predictive analytics, and they also were in close agreement (business 32%, IT 33%) that doing so would have a transformational impact, enabling the organization to do things it couldn’t do before.

However, some distinctions are important to consider, especially when looking at the business case for predictive analytics. Business users more often focus on the benefit of achieving competitive advantage (60% vs. 50% of IT) and creating new revenue opportunities (55% vs. 41%), which are the two benefits most often cited overall. On the other hand, IT professionals more often focus on the benefits of in­creased upselling and cross-selling (53% vs. 32%), reduced risk (26% vs. 21%) and better compliance (26% vs. 19%); the last two reflect key responsibilities of the IT group.

Despite strong business involvement, when it comes to products, IT, technical and data experts are indispensable for the evaluation and use of predictive analytics. Data scientists or the head of data management are most often involved in recommending (52%) and evaluating (56%) predictive analytics technologies. Reflecting the need to deploy predictive analytics to business units, analysts and IT staff are the next-most influential roles for evaluating and recommending. This involvement of technically sophisticated individuals combined with the movement away from organizations buying stand-alone tools indicates an increasingly team-oriented approach.

Purchase of predictive analytics often requires approval from high up in the organization, which underscores the degree of enterprise-wide interest in this technology. The CEO or president is most likely to be involved in the final decision in small (87%) and midsize (76%) companies. In contrast, large companies rely most on IT management (40%), and very large companies rely most on the CIO or head of IT (60%). We again note the importance of IT in the predictive analytics decision-making process in larger organizations. In the previous research, in large companies IT management was involved in approval in 9 percent of them and the CIO was involved in only 40 percent.

As predictive analytics becomes more widely used, buyers should take a broad view of the design and deployment requirements of the organization and specific lines of business. They should consider which functional areas will use the tools and consider issues involving people, processes and information as well as technology when evaluating such systems. We urge business and IT buyers to work together during the buying process with the common goal of using predictive analytics to deliver value to the enterprise.


Ventana Research

Our recently released benchmark research into next-generation predictive analytics  shows that in this increasingly important area many organizations are moving forward in the dimensions of information and technology, but most are challenged to find people with the right skills and to align organizationalVentanaResearch_NextGenPredictiveAnalytics_BenchmarkResearch processes to derive business value from predictive analytics.

For those that have done so, the rewards can be significant. One-third of organizations participating in the research said that using predictive  analytics leads to transformational change – that is, it enables them to do things they couldn’t do before – and at least half said that it provides competitive advantage or creates new revenue opportunities. Reflecting the  vr_NG_Predictive_Analytics_03_benefits_of_predictive_analytics momentum behind predic­tive analytics today, virtually all participants (98%) that have engaged in predictive analytics said that they will be rolling out more of it.

Our research shows that predictive analytics is being used most often in the front offices of organizations, specifically in marketing (48%), operations (44%) and IT (40%). While operations and IT are not often considered front-office functions, we find that they are using predictive analytics in service to customers. For instance, the ability to manage and impact the customer experience by applying analytics to big data is an increasingly important approach that  I recently wrote about . As conventional channels of communication give way to digital channels, the use of predictive analytics in operations and IT becomes more valuable for marketing and customer service.

However, the most widespread barrier to making changes in predictive analytics is lack of resources (cited by 52% of organizations), which includes finding the necessary skills to design and deploy programs. The research shows that currently consultants and data scientists are those most often needed. Half the time those designing the system are also the end users of it, which indicates that using predictive analytics still requires advanced skills. Lack of awareness (cited by 48%) is the second-most common barrier; many organizations fail to understand the vr_NG_Predictive_Analytics_06_technical_challenges_to_predictive_analyti.._  value of predictive analytics in their business. Some of the reluctance to implement predictive analytics may be because doing so can require significant change. Predictive analytics often represents a new way of thinking and can necessitate revamping of key organizational processes.

From a technical perspective, the most common deployment challenge is difficulty in integrating predictive analytics into the information architecture, an issue cited by half of participants. This is not surprising given the diversity of tools and databases involved in big data. Problems with accessing source data (30%), inappropriate algorithms (26%) and inaccurate results (21%) also impede use. Accessing and normalizing data sources is a significant issue as many different types of data must be incorporated to use predictive analytics optimally. Blending this data and turning it into a clean analytic data set often takes significant effort. Confirming this is the finding that data preparation is the most challenging part of the analytic process for half of the organizations in the research.

Regarding interaction with other established systems, business intelligence is most often the integration point (for 56% of companies). However, it also is increasingly embedded in databases and middleware. The ability to perform modeling in databases is important since it enables analysts to work with large data sets and do more timely model updates and scoring. Embedding into middleware has grown fourfold since our previous research on predictive analytics in 2012; this has implications for the emerging Internet of Things (IoT), through which people will interact with an increasing array of devices.

Another sign of the broader adoption of predictive analytics is how and where buying decisions are made. Budgets for  vr_NG_Predictive_Analytics_07_funding_improvement_in_predictive_analytic.._ predictive analytics are shifting. Since the previous research, funding sourced from general business budgets has declined 9 percent and increased 8 percent in line-of-business IT budgets. This comports with a shift in the form in which organizations prefer to buy predictive analytics, which now is less as a stand-alone product and more embedded in other systems. Usability and functionality are still the top buying criteria, reflecting needs to simplify predictive analytics tools and address the skills gap while still being able to access a range of capabilities.

Overall the research shows that the application of predictive analytics to business processes sets high-performing organizations apart from others. Companies more often achieve competitive advantage with predictive analytics when they support the deployment of predictive analytics in business processes (66% vs. 57% overall), use business intelligence and data warehouse teams to design and deploy predictive analytics (71% vs. 58%) and fund predictive analytics as a shared service (73% vs. 58%). Similarly, those that train employees in the application of predictive analytics to business problems achieve more satisfaction and better outcomes.

Organizations looking to improve their business through predictive analytics should examine what others are doing. Since the time of our previous research, innovation has expanded and there are more peer organizations across industries and business functions that can be emulated. And the search for such innovation need not be limited to within one’s industry; cross-industry examples also can be enlightening. More concretely, the research finds that people and processes are where organizations can improve most in predictive analytics. We advise them to concentrate on streamlining processes, acquiring necessary skills and supporting both with technology available in the market. To begin, develop a practical predictive analytics strategy and enlist all stakeholders in the organization to support initiatives.


Ventana Research

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