Predictive analytics in action: real-world examples and advice
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Predictive analytics in action: real-world examples and advice Predictive analytics in action: real-world examples and advice Document Transcript

  • Predictive Analytics in Action: Real-World Examples and Advice Predictive analytics projects are inherently complex and potentially costly. But for organizations that get it right, they can pay off in improved decision making and competitive advantages over business rivals. An Essential Guide 1 2 3 4Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles Virtualization Cloud ApplicationDevelopment Networking StorageArchitecture DataCenterManagement BusinessIntellegence/Applications DisasterRecovery/Compliance Security
  • 2   Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 1editor’s note Modeling the Future: A Challenging but Rewarding Proposition Who wouldn’t want to predict the future, especially when money is at stake? Alas, businesses can’t just rely on crystal balls, tarot cards and palm readers—at least if they want to stay in business. But companies can turn to predictive analytics software to help them peer into the business future—for example, to predict which customers are likely to be open to cross-selling of- fers and which ones might not be worth additional sales attention. But they don’t call it advanced analytics for nothing. If your organization is looking to deploy and use predictive analytics tools, you’ll need to make sure that you have the right level of analytics skills in place. Time for an infusion of data scientists, perhaps? Building predictive models is a complex, time- consuming process that requires trial-and-error testing in order to get the algorithms to produce the desired analytical results. And convincing busi- ness and operational managers to trust what the models are telling them and adjust their strategies and processes accordingly is another big challenge. This three-part guide offers practical advice from experienced analytics professionals and industry consultants on how to successfully manage a pre- dictive analytics program. The lead story details key steps to take in develop- ing and implementing a program, starting with ensuring that your company is open to the possibilities enabled by predictive analytics. Next, we recount the lessons that one analytics exec has learned about building a predictive analytics team. And we report on a pair of surveys pointing to a lack of skills and proper training as predictive analytics inhibitors. Craig Stedman Executive Editor, SearchBusinessAnalytics.com
  • 3  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 2Best Practices Predictive Analytics Programs Need Open Organizational Minds Has the current fervor to pounce on every piece of available data for po- tential analytical uses spawned a world in which information often is col- lected for its own sake? Sometimes it might seem that way. But in the ever-expanding universe of “big data,” predictive analytics software is one technology that can take advantage of the great variety of data accumulated by an organization as it works to model customer behavior and future busi- ness scenarios. And using predictive analytics tools to interpret data is becoming more important to businesses: The most successful companies and rising-star en- terprises sedulously employ them to help point the way forward on business strategies and operations, according to analysts who focus on advanced ana- lytics technologies. But that doesn’t happen magically, they cautioned; orga- nizations need to take the right steps to develop effective predictive analytics programs. In many industries, getting a leg up on the competition can be more chal- lenging than ever—especially if companies are set in their ways. The starting point in embracing predictive analytics should be ensuring that an organiza- tion has a proper frame of mind about using the technology, the analysts said. An open, dexterous attitude that’s naturally curious, eager to learn and will- ing to adapt will produce the best results. Douglas Laney, an analyst at Gartner Inc. in Stamford, Conn., thinks a pre- dictive analytics program should begin by questioning historical business methods while searching far and wide for better ones. Companies “should not only focus on how things have been done in the past but be open to big ideas for innovations and transformations,” he said.“This could mean
  • 4   Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 2Best Practices applying measures effective in other industries to your industry.” Such a mind-set should extend to the point of embracing approaches that “radically change the way business processes are done” in an organization, Laney added. With that in mind, the mentality of the players—particularly the business managers who are being asked to buy into the findings of predictive mod- els—is frequently the key variable that determines the success or failure of predictive analytics programs. A per- spicacious corporate culture champions objectivity, welcomes new ideas and is naturally flexible. Conversely, a retro- grade one resists change and draws heavily on existing biases and subjective formulas.“Resisting new ways of doing things is the reason most projects fail,” said John Lucker, head of Deloitte Consulting LLP’s advanced analytics and modeling practice. Keep Your Eyes on the Business Prize The grand plan of a predictive analytics deployment should also begin with a clear set of business objectives, said Thomas “Tony” Rathburn, a senior con- sultant at The Modeling Agency LLC, a Pittsburgh-based consulting com- pany that focuses on data mining and predictive analytics. Then, he added, a team-oriented strategy is needed to advance those objectives. That is best constructed through substantive discussions involving program managers, predictive modelers, data analysts and business representatives. So critical is the strategy development process that Eric King, president and founder of The Modeling Agency, recommends retaining “a seasoned strategic mentor” to help lead the effort and keep it on track. Once a predictive analytics strategy is in place, it’s time to begin the anal- ysis process. Laney said “chewy” questions that probe deeply into data will unearth findings with high operational value. The truly useful ones, he said, are multifaceted—for example,“How can we grow new customers by 20% Once a predictive analytics strategy is in place, it’s time to begin the analysis process.
  • 5  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 2Best Practices per year for a certain product line without cannibalizing other product lines given the range of economic forecasts, competitor trends and changing con- sumer demands?” Run through predictive models, such questions can con- tribute in a big way to driving new business, according to Laney. Building Models is a Testing Process After choosing and deploying the predictive analytics tools that best fit the job at hand, developing models is the next step. Mike Gualtieri, an analyst at Forrester Research Inc. in Cambridge, Mass., said analytics algorithms should be run on 70% of a data set to create an effective predictive model.“Then you test that model on the remaining 30%,” he said. Completed models should be regu- larly tested and enhanced as needed, and a set of performance metrics should be put in place for tracking their accuracy, Gualtieri added—all part of a process for “continuous monitoring of the predictive analytics model.” Moreover, said other analysts, the entire predictive analytics process requires regular monitoring as business needs and the nature of the data being collected by an organization change. Analytics strategies and tactics that worked initially will need to be revisited and perhaps revised in order to continue achieving optimal results. The mark of a truly successful predictive analytics program, Lucker said, is when some of the cost savings or business gains realized from an ongoing analysis project can be applied to pay for the next one so no new dollars need to be spent.“Using the value of each project to fund downstream efforts is an evolutionary approach that comes with a [built-in] return on investment,” he said. —Roger du Mars Analytics strategies and tactics that worked initially will need to be revisited and per- haps revised in order to continue achieving optimal results.
  • 6  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 3Team Building Recipe for Predictive Analytics Success Includes One Part Storyteller The secret to building a successful predictive analytics team is finding people with statistical analysis, programming and—perhaps most impor- tant—storytelling skills, according to one practitioner. It’s important to find multitalented people because, oftentimes, predictive analytics teams are rather small, said Jennifer Golec, vice president of strate- gic analytics at XL Insurance Inc. Multifaceted individuals offer a higher level of flexibility, she said, and that comes in handy when resources are tight. Ideally, predictive analytics professionals should be one part programmer, Golec said, because they’ll be working with a great deal of information and conducting exploratory analysis. Commercial software can help in these ar- eas, she explained, but some programming skills will still be helpful for tasks like manipulating or massaging data and creating new variables. Predictive analytics professionals should also focus on developing sta- tistical analysis skills because those are necessary for building multivariate models, statistical tools that use multi- ple variables to forecast outcomes. “The third piece is that you have to be part storyteller. You have to be able to interpret those results,” Golec said.“[That means] really being able to in- terpret the insight that you pull from the data. You have to be able to relay that because if you don’t, you’ll be sitting on this great model and you won’t be able to implement it.” Predictive analyt- ics professionals should focus on developing statis- tical analysis skills.
  • 7   Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 3Team Building More Than Just Crunching Numbers The popular 2011 film Moneyball—which tells the story of Oakland A’s gen- eral manager Billy Beane, who used analytics to find undervalued players and build a great baseball team—might give the mistaken impression that ana- lytics is all about crunching numbers. But it’s much more than that, accord- ing to Golec. Organizations must also strive to understand how the results of predictive models translate to the real business world. “Sometimes that is the danger with products like SAS,” Golec said.“They make it so easy to push the data in and hit the button and have something come out. But if you’re not trained to understand and interpret that output, you could end up with junk and you might not know it.” Golec, who has a doctorate in eco- nomics from the University of Missouri and previously ran a predictive ana- lytics program for insurance provider The Hartford, began working for XL Insurance and its global parent company, XL Group PLC, in October 2011. Analytics Goal: Ratcheting Back on Risk One of her first tasks was to find a software vendor that could help the prop- erty and casualty company build out its fledgling predictive analytics pro- gram. XL Insurance launched the program to do a better job of avoiding unnecessary risk and, ultimately, improving its loss ratio.“The loss ratio is losses over premiums,” Golec said.“The lower it is, the more profitable you are.” Golec took a close look at SPSS, which was acquired by IBM in 2011, and Wolfram Research’s Mathematica tools. But she had worked with software from SAS Institute Inc. in the past and decided to do so again. “Half the battle is working with the data, manipulating the data and get- ting it into a form that allows you to actually do the modeling,” she said. Organizations must also strive to under- stand how the results of predictive models translate to the real business world.
  • 8  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 3Team Building “SAS allows us to get the data into the shape and form that we want.” XL Insurance is using several SAS products, including SAS/STAT, a statis- tical analysis tool; SAS Graph, a visual tool that allows users to present in- formation in charts and graphs; SAS Enterprise Guide (EG), which makes it easier to do exploratory analysis of data stores; and JMP, a data visualization tool. Ensuring Adoption Central to Implementation Process The team at XL Insurance is in the process of building predictive models for risk assessment. The next step, according to Golec, is to implement those models and closely monitor and measure the results. Golec said the toughest aspects of the implementation phase will likely re- volve around change management and, specifically, getting the right people to adopt predictive analytics findings as part of their usual routines. Making sure that any workflow or architecture changes are properly documented is also a major challenge. Another is “making sure that we’ve come up with how we’re going to   track it and make sure it’s working,” she said.“But I think the big thing in   implementation is just achieving that buy-in and making sure that it’s   used.”—Mark Brunelli
  • 9  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 4Challenges Surveys Point to Skills, Training as Predictive Analytics Hurdles Businesses recognize the potential of predictive analytics, yet there’s a large gap between those who see it as important and those who actu- ally use the technology, according to a pair of surveys conducted by Ventana Research. The market research and consulting company, based in San Ramon, Calif., conducted an initial study in early 2011 which found that only 13% of the re- sponding organizations were using predictive analytics. But 80% indicated it was important or very important, said David Menninger, who was infor- mation technology research director at Ventana when he was interviewed for this story; he has since taken a job with a technology vendor. The reason for that gap? While most businesses consider predictive analyt- ics important, those that struggle with it lack both the skills and the training required to be successful with the technology, Menninger discovered in a fol- low-up study.“Organizations are least mature in the people aspect,”he said. That conclusion was drawn from the results of a three-month survey of 198 respondents measured against Ventana’s predictive analytics maturity model, which was used to rate the survey responses across the categories of process, information, technology and people. The survey revealed that self-service predictive analytics, or end users cre- ating and deploying their own analyses, has not been widely deployed, de- spite a wave of easier-to-use predictive analytics tools coming to market. Analytics Skills Not a Common Trait In fact, almost half of the respondents questioned whether users have the background to produce their own analyses. For the nonbelievers, Menninger
  • 10  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 4Challenges said it came down to two reasons: 83% reported users didn’t have enough skills, and 58% reported users didn’t understand the mathematics involved. “[Predictive analytics] requires the specialist skill set—the data scientist, the statistician, the data mining experts—to be successful,” he said. Instead of relying on users, 63% of respondents reported their organiza- tion had a specialized team for predictive analytics or that the task fell to the business intelligence (BI) and data warehousing (DW) team. But even then, Menninger’s research indicated that how satisfied respondents are with the way predictive analytics is used in their organizations (two-thirds said they’re satisfied) depends, in part, on who does the work. The highest levels of satisfaction, 70%, came from respondents who worked for organizations that employed specialists such as data scientists to produce the predictive analytics find- ings. The lowest levels of satisfaction, 59%, came from respondents whose   BI and DW teams were in charge of   the work.“I think it’s common for organizations to think this will naturally fall out of the BI and DW team,” Menninger said.“But what this tells me is that this is not a generalized skill of BI and DW teams.” Support Lacking for Predictive Analytics Users Many organizations are also not doing a great job providing the ongoing sup- port needed to successfully maintain a strong predictive analytics program, Menninger said. According to the survey results, businesses are most successful at provid- ing concept and technique training (44% of respondents felt this was ade- quate) and have the most trouble delivering help desk support (24% reported this was adequate). More than a third of respondents, 42%, also found prod- uct training to be adequate. Many organizations are not doing a great job providing the ongo- ing support needed to successfully maintain a strong predictive analytics program.
  • 11  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles 4Challenges Menninger said concept, technique and product training may drive a stron- ger sense of satisfaction because they require “specialized knowledge” over the broader needs—and the skills—required by something like a help desk. “I think it relates back to necessary skills,” he said.“How do you have peo- ple on the help desk supporting a more complicated topic? The help desk re- sources would need to have specialized training and skills to be able to provide meaningful support.” Yet respondents indicated that, in addition to concept and technique training, the most effective type of support was brought about by help desk resources. Organizations that provided either support feature adequately had an 89% satisfaction rating on average, according to the survey results. “I suspect that organizations probably think first about doing product training and less about this generalized set of skills and help desk resources,” Menninger said. While the level of satisfaction in a predictive analytics program may wax and wane based on training, Menninger said the root of that issue is most likely derived from what he considers to be an even bigger problem—a lack of skills. “The skills issue is significant,” he said.“It appears to have been preventing organizations in the past from either choosing to tackle predictive analytics or [being able] to tackle it successfully.” Menninger said predictive analytics requires a deeper kind of knowledge. “It’s unrealistic today to expect the technology to deliver self-service capa- bilities,” he said.“[But] if you have the right skills, the technology is available to be successful with predictive analytics.”—Nicole Laskowski The most effective type of support was brought about by help desk resources.
  • 12  Predictive Analytics in Action: Real-World Examples and Advice Home Editor’s Note Predictive Analytics Programs Need Open Organizational Minds Recipe for Predictive Analytics Success Includes One Part Storyteller Surveys Point to Skills, Training as Predictive Analytics Hurdles about the authors Roger du Mars is a freelance writer based in Redmond, Wash. He has writ- ten for Time, USA Today and The Bos- ton Globe, and he was the Seoul, South Korea, bureau chief of Asiaweek and the South China Morning Post. Email him at rogerdeandumars@yahoo.com. Mark Brunelli is news director for SearchBusinessAnalytics.com and the other websites in TechTarget Inc.’s Business Applications and Architecture Media Group. He oversees coverage of topics such as business intelligence and analytics, data management, customer relationship management and business applications. Email him at mbrunelli@ techtarget.com and follow him on Twitter: @Brunola88. Nicole Laskowski is the news editor for SearchBusinessAnalytics.com. She covers business intelligence, analytics and data visualization technologies   and trends. Email her at nlaskowski  @techtarget.com and follow her on   Twitter: @TT_Nicole. Predictive Analytics in Action: Real-World Examples and Advice is a   SearchBusinessAnalytics.com   e-publication.    Barney Beal Senior Executive Editor Jason Sparapani Managing Editor, E-Publications Nicole Laskowski News Editor Craig Stedman Executive Editor Linda Koury Director of Online Design Mike Bolduc Publisher mbolduc@techtarget.com Ed Laplante Director of Sales elaplante@techtarget.com TechTarget 275 Grove Street, Newton, MA 02466 www.techtarget.com © 2012 TechTarget Inc. No part of this publication may be transmitted or reproduced in any form or by any means without written permission from the publisher. TechTarget reprints are available through The YGS Group. About TechTarget: TechTarget publishes media for information technology professionals. More than 100 focused websites enable quick access to a deep store of news, advice and analysis about the tech- nologies, products and processes crucial to your job. Our live and virtual events give you direct access to independent expert commentary and advice. At IT Knowledge Exchange, our social community, you can get advice and share solutions with peers and experts.