Up your game with a Predictive Analytics Program

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Up your game with a

Predictive Analytics

Program

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The widespread adoption of predictive analytics has

been at the mercy of two opposing forces over the past two

decades. Business intelligence (BI) software have become

widely used- even to the point of being pervasive in many

organizations. But for the most part, predictive analytics

tools are still used by only the most sophisticated data-driven

enterprises. In this E-Guide, look at the critical things BI and

analytics professionals often overlook, hence depriving their

organizations of the substantial benefits of predictive

analytics.

Top five things business intelligence and analytics pros

overlook

The widespread adoption of predictive analytics has been at the mercy of two opposing forces over the past two decades. Frequent, compelling use cases from the few organizations that have properly implemented predictive analytics projects propelled the discipline into the mainstream. Yet its perceived complexity has slowed adoption.

Let’s take a look at five critical things business intelligence (BI) and analytics professionals often overlook, hence depriving their organizations of the substantial benefits of predictive analytics.

1. Getting started the right way.

Predictive analytics is not another flash-in-the-pan technology. Most Fortune 500 companies have established departments and practices that refine crude data into highoctane intelligence. But few beyond the largest corporations have formalized or even organized their approaches.

Why haven’t more jumped into the game? There many barriers and excuses, most notably:

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Why not approach predictive analytics like most IT and BI projects—with an emphasis on technical training? Most IT projects focus on fulfilling objectives at the operational level; their functions are more direct and tactical. Predictive and advanced analytics, however, rely heavily on strategic assessment, design and implementation. For practitioners and functional managers entrenched in typical IT projects, this requires a significant shift in mind-set.

Most are surprised to learn that the bulk of the training necessary for predictive analytics success is not technical. The mathematics involved can be very sophisticated, but modern software tools automate the complexity, allowing most business practitioners to build adequate predictive models with little training. Although it’s helpful to have some statistical grounding and a basic appreciation of the capabilities and limitations of various modeling methods, expertise in actual model development has little impact on the success or failure of a project.

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But it is imperative that at least one project manager or functional leader be well-versed in a formal, methodical, process-driven approach to predictive analytics— for example, the Cross-Industry Standard Process for Data Mining, or CRISP-DM—at the project level. Unfortunately, there are very few courses out there that provide this emphasis—particularly from a vendor- neutral perspective. But a search on data-mining and predictive-analytics strategic training will produce a few good options with a projec tlevel orientation.

3. Designing before building.

If you were building a new house, wouldn’t you first meet with designers and engineers to draw up blueprints? What would your home look like if the builder started construction before fully understanding your needs, preferences and site dynamics? The answer is obvious.

Most organizations start predictive analytics projects by jumping directly in with software and data and then hammer away on models without

understanding what they’re building. When predictive analytics is not

approached as a well-planned process, practitioners may still end up building very good models, but the models answer the wrong questions and can’t be properly interpreted or implemented.

A number of standardized processes spell out precisely how to plan and implement a successful predictive analytics project. Two of the major ones are vendor-neut ral CRISP-DM and SAS-specific SEMMA. But vendors are primarily interested in trumpeting and selling technology. Their marketing efforts have influenced misguided organizations to listen to the loudest voices and start with the easiest-to-obtain resource: software.

Even the few companies that are aware of standardized processes are resistant to start with training and follow a formal process like CRISPDM. When it comes to predictive analytics, nobody wants to have to sell the notion of a comprehensive assessment and resulting project definition. The exercise itself is not sexy. It requires an up-front investment of time, money and effort. And it will not produce a return on investment. But then again,

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Analytics revealed a focus on the wrong part of the problem. The majority of participants cited the performance or accuracy of predictive models as the most important factor in determining success. In the real world, however, practitioners are not rewarded for how well a model conforms to artificial metrics— but rather for how effectively it helps optimize the allocation and use of organizational resources.

While discovery is a function of predictive analytics, the derived information should support the organization’s priorities—and not the other way around. The first step should be establishing and validating strategic priorities across functional teams. Justifying the time to properly design an analytics project in these days of Agile BI and immediate results is not easy, but it’s mandatory. A comprehensive assessment, formal project definition and measurement framework must be established for results that are substantive and sustainable.

5. Avoiding distractions and buzz.

Just as the masses moved far enough down the BI chain to embrace predictive analytics as a formal practice and start deriving value, they were diverted to the next shiny, new thing upon hearing exciting terms like big data and data science.

Seasoned BI professionals will recall how chasing hype can lead to costly exploration of uncharted, underdeveloped and oversold technology.

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Vendors riding the coattails of the big data analytics buzz may argue that traditional analytics just won’t get the job done in the new world of ever-swelling data sets. When real-time analysis of high-velocity, multidimensional data is required, there may be a need for computational scalability. But applications that call for in-database analytics or distributed processing are certainly not the norm.

When sampled properly, only a small fraction of the data available is required to adequately represent the solution space—a mathematical term describing the entire area represented by multiple dimensions—and effectively train a predictive model to produce the desired information. Once such models are deployed, running them and scoring large volumes of new data can be done highly efficiently, suiting the vast majority of business applications.

Be wary of the hype. Those who maintain the course toward predictive analytics while gradually building other aspects of their BI chains toward big data scalability are due for early payback.

So don’t cheat the process. Don’t rush to grab software and dive headlong into your data. It’s simply not worth it, and that’s been proven time and time again. If you don’t succeed with predictive analytics on a small-scale pilot program driven by a sound assessment and project definition, then forging ahead into big data analytics will produce nothing more than a big noise.

About the author:

Eric King is president and founder of The Modeling Agency, a consulting and training firm with a focus on data mining and predictive analytics. Email him at [email protected].

Using predictive analytics tools and setting up an analytics

program

Business intelligence (BI) software has become widely used – even to the point of being pervasive in many organizations. But for the most part,

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In addition, whereas IT groups typically develop BI dashboards and reports for business users, predictive analytics models usually are created by a handful of highly skilled end users. It can be an eye-opening experience for IT workers to realize that the people who build predictive models are more data-savvy and technically oriented than they are. In fact, predictive model builders often view the IT staff merely as data gatherers whose purpose is to feed their data-hungry models.

More on predictive analytics tools and analytics programs

The industries that pioneered the use of predictive analytics software are insurance, financial services and retail. Companies in those industries share the need to understand who their customers and prospects are, how to up-sell and cross-up-sell products and services, and how to predict customer behavior (including bad behavior through processes such as fraud detection.) Predictive analytics tools can help in all of those areas. Other industries that have benefited from the technology include telecommunications, travel, healthcare and pharmaceuticals.

Across industries, there are common approaches that can be taken in building the required predictive models, selecting technology and staffing up for successful predictive analytics projects.

Building predictive models is a combination of science and art. It’s an iterative process in which a model is created from an initial hypothesis and then refined until it produces a valuable business outcome – or discarded in favor of another model with more potential. Developing and then using predictive models involves the following tasks:

1. Scope and define the predictive analytics project. What business processes will be analyzed as part of the initiative, and what are the desired business outcomes?

2. Explore and profile your data. Because predictive analytics is a data-intensive application, considerable effort is required to

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3. Gather, cleanse and integrate the data. Once the necessary data is located and evaluated, work often needs to be done to turn it into a clean, consistent and comprehensive set of information that is ready to be analyzed. That process may be minimized if an enterprise data warehouse is leveraged as the primary data source. But external and unstructured data is often used to augment warehoused information, which can add to the data integration and cleansing work.

4. Build the predictive models. The model builders take over here, testing models and their underlying hypotheses through steps such as including and ruling out different variables and factors; back -testing the models against historical data; and determining the potential business value of the analytical results produced by the models.

5. Incorporate analytics into business processes. Predictive analytics tools and models are of no business value unless they’re incorporated into business processes so that they can be used to help manage (and hopefully grow) business operations.

6. Monitor the models and measure their business results. Predictive models need to adapt to changing business conditions and data. And the results they’re producing need to be tracked so that you know which models are providing the most value to your organization.

7. Manage the models. Prune the models with little business value, improve the ones that may not yet be delivering on their expected outcome but still have potential, and tune the ones that are producing valuable results to further improve them.

With a typical BI project, business users define their report requirements to the IT or BI group, which then identifies the required data, creates the reports and hands them off to the users. Similarly, in predictive analytics

deployments, a joint business-IT team must scope and define the project, after which IT assesses, cleanses and integrates the required data. At this

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Most BI vendors sell integrated product suites that include query tools, dashboards and reporting software. But if they offer predictive analytics software, it tends to be sold as a separate and distinct product. While that’s starting to change, the predictive analytics tools now being used primarily come from vendors that specialize in statistical analysis, data mining or other advanced analytics.

Predictive analytics tools turn the BI software selection process on its head

Compared with a typical BI software evaluation, where the IT or BI group drives the software selection process while soliciting input and feedback from business users, an evaluation of predictive analytics tools is turned upside down – or at least it should be. Ideally, the statisticians and other users who build the predictive models take the lead in evaluating the predictive analysis tools that are being considered, with IT providing input on the software’s potential impact on the organization’s technology infrastructure. In this case, the users are likely to be the only ones who understand the statistical or data mining techniques they need and whether the various tools can support those requirements.

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Predictive model builders and users must have a strong knowledge of data, statistics, an organization’s business operations and the industry in which it competes. Companies, even very large ones, often have only a small number of people with such skills. As a result, predictive modelers and analysts are likely to be viewed as the star players on a data analytics team.

The typical organizational structure places predictive analytics experts in individual business units or departments. The analysts work with business executives to determine the business requirements for specific predictive models and then go to the IT or BI group to get access to the required data. In this kind of structure, IT and BI workers are enablers: Their primary tasks are to gather, cleanse and integrate the data that the predictive analytics gurus need to run their models.

In conclusion, the critical success factors for successful deployments of predictive analytics tools include having the right expertise (i.e., predictive modelers with a statistical pedigree); delivering a comprehensive and

consistent set of data for predictive analytics uses; and properly incorporating the predictive models into business processes so that they can be used help to improve business results.

About the author: Rick Sherman is the founder of Athena IT Solutions, a

Stow, Mass.-based firm that provides data warehouse and business intelligence consulting, training and vendor services. In addition to having more than 20 years of experience in the IT business, Sherman is a published author of more than 50 articles, a frequent industry speak er, an Information Management Innovative Solution Awards judge and an expert contributor to both SearchBusinessAnalytics.com and SearchDataManagement.com. He blogs at The Data Doghouse and can be reached at [email protected].

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Up your game with a Predictive Analytics Program