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One of the key findings in our latest benchmark research into predictive analytics is that companies are incorporating predictive analytics into their operational systems more often than was the case three years ago. The research found that companies are less inclined to purchase stand-alone predictive analytics tools (29% vs 44% three years ago) and more inclined to purchase predictive analytics built into business intelligence systems (23% vs 20%), applications (12% vs 8%), databases (9% vs 7%) and middleware (9% vs 2%). This trend is not surprising since operationalizing predictive analytics – that is, building predictive analytics directly into business process workflows – improves companies’ ability to gain competitive advantage: those that deploy predictive analyticsvr_NG_Predictive_Analytics_12_frequency_of_updating_predictive_models within business processes are more likely to say they gain competitive advantage and improve revenue through predictive analytics than those that don’t.

In order to understand the shift that is underway, it is important to understand how predictive analytics has historically been executed within organizations. The marketing organization provides a useful example since it is the functional area where organizations most often deploy predictive analytics today. In a typical organization, those doing statistical analysis will export data from various sources into a flat file. (Often IT is responsible for pulling the data from the relational databases and passing it over to the statistician in a flat file format.) Data is cleansed, transformed, and merged so that the analytic data set is in a normalized format. It then is modeled with stand-alone tools and the model is applied to records to yield probability scores. In the case of a churn model, such a probability score represents how likely someone is to defect. For a marketing campaign, a probability score tells the marketer how likely someone is to respond to an offer. These scores are produced for marketers on a periodic basis – usually monthly. Marketers then work on the campaigns informed by these static models and scores until the cycle repeats itself.

The challenge presented by this traditional model is that a lot can happen in a month and the heavy reliance on process and people can hinder the organization’s ability to respond quickly to opportunities and threats. This is particularly true in fast-moving consumer categories such as telecommunications or retail. For instance, if a person visits the company’s cancelation policy web page the instant before he or she picks up the phone to cancel the contract, this customer’s churn score will change dramatically and the action that the call center agent should take will need to change as well. Perhaps, for example, that score change should mean that the person is now routed directly to an agent trained to deal with possible defections. But such operational integration requires that the analytic software be integrated with the call agent software and web tracking software in near-real time.

Similarly, the models themselves need to be constantly updated to deal with the fast pace of change. For instance, if a telecommunications carrier competitor offers a large rebate to customers to switch service providers, an organization’s churn model can be rendered out of date and should be updated. Our research shows that organizations that constantly update their models gain competitive advantage more often than those that only update them periodically (86% vs 60% average), more often show significant improvement in organizational activities and processes (73% vs 44%), and are more often very satisfied with their predictive analytics (57% vs 23%).

Building predictive analytics into business processes is more easily discussed than done; complex business and technical challenges must be addressed. The skills gap that I recently wrote about is a significant barrier to implementing predictive analytics. Making predictive analytics operational requires not only statistical and business skills but technical skills as well.   From a technical perspective, one of the biggest challenges for operationalizing predictive analytics is accessing and preparing data which I wrote about. Four out of ten companies say that this is the part of the predictive analytics process vr_NG_Predictive_Analytics_02_impact_of_doing_more_predictive_analyticswhere they spend the most time. Choosing the right software is another challenge that I wrote about. Making that choice includes identifying the specific integration points with business intelligence systems, applications, database systems, and middleware. These decisions will depend on how people use the various systems and what areas of the organization are looking to operationalize predictive analytics processes.

For those that are willing to take on the challenges of operationalizing predictive analytics the rewards can be significant, including significantly better competitive positioning and new revenue opportunities. Furthermore, once predictive analytics is initially deployed in the organization it snowballs, with more than nine in ten companies going on to increase their use of predictive analytics. Once companies reach that stage, one third of them (32%) say predictive analytics has had a transformational impact and another half (49%) say it provides a significant positive benefits.


Ventana Research

Our benchmark research into predictive analytics shows that lack of resources, including budget and skills, is the number-one business barrier to the effective deployment and use of predictive analytics; awareness – that is, an understanding of how to apply predictive analytics to business problems – is second. In order to secure resources and address awareness problems a business case needs to be created and communicated clearly wherever appropriate across the organization. A business case presents the reasoning for initiating a project or task. A compelling business case communicates the nature of the proposed project and the arguments, both quantified and unquantifiable, for its deployment.

The first steps in creating a business case for predictive analytics are to understand the audience and to communicate with the experts who will be involved in leading the project. Predictive analytics can be transformational in nature and therefore the audience potentially is broad, including many disciplines within the organization. Understand who should be involved in business case creation a list that may include business users, analytics users and IT. Those most often primarily responsible for designing and deploying predictive analytics are data scientists (in 31% of organizations), the business intelligence and data warehouse team (27%), those working in general IT (16%) and line of business analysts (13%), so be sure to involve these groups. Understand the specific value and challenges for each of the constituencies so the business case can represent the interests of these key stakeholders. I discuss the aspects of the business where these groups will see predictive analytics most adding value here and here.

For the business case for a predictive analytics deployment to be persuasive, executives also must understand how specifically the deployment will impact their areas of responsibilityvr_NG_Predictive_Analytics_01_front_office_functions_use_predictive_anal.._ and what the return on investment will be. For these stakeholders, the argument should be multifaceted. At a high level, the business case should explain why predictive analytics is important and how it fits with and enhances the organization’s overall business plan. Industry benchmark research and relevant case studies can be used to paint a picture of what predictive analytics can do for marketing (48%), operations (44%) and IT (40%), the functions where predictive analytics is used most.

A business case should show how predictive analytics relates to other relevant innovation and analytic initiatives in the company. For instance, companies have been spending money on big data, cloud and visualization initiatives where software returns can be more difficult to quantify. Our research into big data analytics and data and analytics in the cloud show that the top benefit for these initiatives are communication and knowledge sharing. Fortunately, the business case for predictive analytics can cite the tangible business benefits our research identified, the most often identified of which are achieving competitive advantage (57%), creating new revenue opportunities (50%), and increasing profitability vr_NG_Predictive_Analytics_03_benefits_of_predictive_analytics(46%). But the business case can be made even stronger by noting that predictive analytics can have added value when it is used to leverage other current technology investments. For instance, our big data analytics research shows that the most valuable type of analytics to be applied to big data is predictive analytics.

To craft the specifics of the business case, concisely define the business issue that will be addressed. Assess the current environment and offer a gap analysis to show the difference between the current environment and the future environment). Offer a recommended solution, but also offer alternatives. Detail the specific value propositions associated with the change. Create a financial analysis summarizing costs and benefits. Support the analysis with a timeline including roles and responsibilities. Finally, detail the major risk factors and opportunity costs associated with the project.

For complex initiatives, break the overall project into a series of shorter projects. If the business case is for a project that will involve substantial work, consider providing separate timelines and deliverables for each phase. Doing so will keep stakeholders both informed and engaged during the time it takes to complete the full project. For large predictive analytics projects, it is important to break out the due-diligence phase and try not to make any hard commitments until that phase is completed. After all, it is difficult to establish defensible budgets and timelines until one knows the complete scope of the project.

Ensure that the project time line is realistic and addresses all the key components needed for a successful deployment.  In particular with predictive analytics projects, make certain that it reflects a thoughtful approach to data access, data quality and data preparation. We note that four in 10 organizations say vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_processthat the most time spent in the predictive analytics process is in data preparation and another 22 percent say that they spend the most time accessing data sources. If data issues have not been well thought through, it is next to impossible for the predictive analytics initiative to be successful. Read my recent piece on operationalizing predictive analytics to show how predictive analytics will align with specific business processes.

If you are proposing the implementation of new predictive analytics software, highlight the multiple areas of return beyond competitive advantage and revenue benefits. Specifically, new software can have a total lower cost of ownership and generate direct cost savings from improved operating efficiencies. A software deployment also can yield benefits related to people (productivity, insight, fewer errors), management (creativity, speed of response), process (shorter time on task or time to complete) and information (easier access, more timely, accurate and consistent). Create a comprehensive list of the major benefits the software will provide compared to the existing approach, quantifying the impact wherever possible. Detail all major costs of ownership whether the implementation is on-premises or cloud-based: these will include licensing, maintenance, implementation consulting, internal deployment resources, training, hardware and other infrastructure costs. In other words, think broadly about both the costs and the sources of return in building the case for new technology. Also, read my recent piece on procuring predictive analytics software.

Understanding the audience, painting the vision, crafting the specific case, outlining areas of return, specifying software, noting risk factors, and being as comprehensive as possible are all part of a successful business plan process. Sometimes, the initial phase is really just a pitch for project funding and there won’t be any dollar allocation until people are convinced that the program will get them what they need.  In such situations multiple documents may be required, including a short one- to two-page document that outlines vision and makes a high-level argument for action from the organizational stakeholders. Once a cross functional team and executive support is in place, a more formal assessment and design plan following the principles above will have to be built.

Predictive analytics offers significant returns for organizations willing pursue it, but establishing a solid business case is the first step for any organization.


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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