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Business Information INSIGHT ON MANAGING AND USING DATA

When Predictive Models FailWith the contentious 2016 race for the White House in the rearview mirror,important business lessons vault front and center for analytics managers charged with building predictive models and relying on their outcomes.

Predictive Analytics Needs New Data Prep ApproachEffective data preparation demands new toolsand techniques to meet the requirements ofbig data environments and advanced analytics.

EDITOR’S NOTE What Predictive Analytics Tools Can and Can’t Do

EXECUTIVE DASHBOARD

For Predictive, the Future Is Now

ONE ON ONE

‘Analytics Czar’ Says Hornets Find Plenty of Sizzle in Phizzle

VERBATIM

Peering Into Corporate Crystal Balls

DATA DECISIONS

Big Data’s Ugly Child

WHAT’S THE BUZZ?

Gut Instincts Take Back Seat to Data-Driven Business Decisions

HINDSIGHT

Building Real- Time Analytics Systems? Not So Fast

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WHEN PREDICTIVE MODELS ACT LESS THAN PRESIDENTIAL

EXECUTIVE DASHBOARD: FOR PREDICTIVE, THE FUTURE IS NOW

‘ANALYTICS CZAR’ SAYS HORNETS FIND PLENTY OF SIZZLE IN PHIZZLE

BIG DATA’S HUGE IMPACT ON DATA PREP FOR ANALYTICS

VERBATIM: PEERING INTO CORPORATE CRYSTAL BALLS

BIG DATA’S UGLY CHILD

GUT INSTINCTS TAKE BACK SEAT TO DATA-DRIVEN BUSINESS DECISIONS

BUILDING REAL- TIME ANALYTICS SYSTEMS? NOT SO FAST

2 BUSINESS INFORMATION • FEBRUARY 2017

BRIDGET BOTELHO

EDITOR’S NOTE

What Predictive Analytics Tools Can and Can’t Do

THEY SAY THE only thing certain in life is uncertainty, but there are at least some things we can predict with abso-lute confidence. I know with 100% surety, for example, that when I tell my 4-year-old daughter it’s time for bed, she’ll say she isn’t tired. My certainty of this response al-lows me to prepare for the resistance with a strategy that involves firm demands; some cajoling; and, ultimately, bribery.

Unfortunately, customer behaviors are much harder to predict, and that uncertainty can mean the difference between the success and failure of new products or ser-vices. Our need to control outcomes, in life and busi-ness, has placed us smack dab in the middle of today’s data-driven world. We now have an arsenal of predictive analytics tools at our disposal to forecast outcomes with greater certainty than ever before. These tools can vali-date business leaders’ gut instincts to invest in a prom-ising new product or dissuade them from launching a

product likely to go the way of Microsoft’s Zune.But it’s a tricky proposition to decide how much trust

to put into the data—especially when the data con-tradicts long-held beliefs. Ian Swanson, CEO and co-founder of DataScience Inc., says data analysis is a way to prove out a thesis; it provides the data to back it up, invalidate it or head in an entirely new direction. “Some-times data science just proves the assumption,” Swanson notes. “Then there’s the case where we’ve found a piece of gold, and we need to prove it’s worth exploring more. There’s value in both scenarios.”

Predicting the UnpredictableNow that I’ve touted the value of predictive analytics tools, I’ll caution that overreliance on them is just as damaging. As we explain in this month’s cover story, the insights are only as good as the data that fed them. Statis-tician George Box famously wrote, “All models are wrong, but some are useful.” And any data scientist will tell you the way to make predictive models useful is by putting business users at the table, along with the engineers who will translate predictive insights into new products.

“There is never going to be a silver bullet with some single software panacea; it will always take expertise in data and in the business,” says Kenneth Sanford, a lead analytics architect with Dataiku and an adjunct professor at Boston College.

Indeed, there is no substitute for expert knowledge,

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predictions would have been more accurate,” he adds. The reason, he explains, is that statisticians spend a lot of time figuring out regeneration and the meaning behind the variable, while predictive machine learning cares very little about how data is created. Had analysts spent more time scrutinizing who answers polls, we may not have seen the same kind of “Dewey Defeats Truman” assumptions in 2016 that sprang from rudimentary elec-tion polling of 68 years ago.

“It isn’t just about getting the machine model and slap-ping some algorithm on a data set to figure out what will happen,” DataScience’s Swanson says. “With the election, I agree with experts that the problem was there were voices missing in the data; there were problems with the integrity of the data from a validation standpoint.”

As explained in another feature, data integrity is the Achilles’ heel of predictive analytics. Get the data prep right, and you’re on your way. In that story and elsewhere in this issue, you’ll hear from companies that are doing predictive analytics correctly and reaping rewards in the form of new customers, more revenue, better efficiency and stronger insights to help them make smart decisions.

Speaking of smart decisions, I’ll end this column here. If you’d like to chat, reach out to me at [email protected] or find me on Twitter @BridgetBotelho. I’d love to hear from you. n

BRIDGET BOTELHOEditorial Director, Business Applications and Information

Management Media Group

but even when the right people are involved with ana-lytics projects, the predictions can be dead wrong—as we saw in the recent U.S. presidential election. Hillary Clinton’s campaign reportedly worked with a team of data scientists led by chief data analyst Elan Kriegel, co-founder of BlueLabs, who was involved with the suc-cessful 2012 Obama campaign. Clinton’s campaign relied on data analysis to inform strategic decisions, including where to work on potential voters and place ads. Those insights were supposed to provide a competitive advan-tage over presidential candidates who didn’t invest in predictive technologies. We all know how that turned out.

A request for comment from Kriegel went unan-swered, so we don’t know whether predictive models helped or hurt the campaign. But for sure, there were other moving parts that crashed the campaign during the final weeks—factors that perhaps not even the best pre-dictive models could have helped.

It’s All About IntegrityThat’s not to say companies should toss away their pre-dictive analytics projects and return to the old way of doing business. Data scientists assert the problem with Clinton’s strategy wasn’t the data-driven approach or the predictive model; rather, it was the integrity of the data which fed the algorithms—a problem that’s all too common in predictive modeling, Sanford says. “The funny thing about using machine learning in the election is that, probably, if they used traditional statistics, the

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WhenPredictiveModels ActLess ThanPresidential

Not Business as Usual

As a result of the turbulent 2016 presidential race, many analytics managers and election forecasters now know they can’t ignore best practices and ensure successful outcomes when deploying and using predictive modeling tools in the business world.

THE 2016 PRESIDENTIAL election ended in stunning fashion, and it wasn’t just because of who won. Indeed, Donald Trump’s upset victory over Hillary Clinton triggered a political earthquake of seismic proportions. But another big surprise was seeing a campaign so focused on big data and predictive analytics fall to the candidate driven more by emotion and intuition.

And it wasn’t just the Clinton campaign that got caught off guard when voting didn’t follow the path predicted by most analytical models. Virtually all ana-lytics-driven election forecasters projected that Clinton would win, some with a probability as high as 99%. Even Trump’s own data analytics team put his chances of pull-ing off a victory at only 30% the day before Election Day last November.

BY ED BURNS

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It’s often said that organizations should be more data- driven. Businesses that make decisions based on data analytics tend to outperform those that don’t, according to proponents. Undoubtedly, cutting-edge enterprises—from Google, Amazon and Facebook to the likes of Uber and Airbnb—are changing their industries partly by le-veraging data mining, machine learning and predictive modeling techniques.

But that doesn’t mean data-driven analytics projects are immune to errors and problems like the ones that befell Clinton’s campaign. Any predictive analytics ini-tiative can hit similar potholes that send it careening off track. Missteps like using low-quality data, measuring the wrong things and failing to give predictive models a suitable reality check should serve as a reminder to data scientists and other analysts that the analytics process isn’t simply a matter of collecting some data and develop-ing models to run against it.

Predict the Right ThingsIn business applications, building and rolling out predic-tive models doesn’t necessarily give you a better idea of what’s likely to happen in the future.

That was a lesson learned recently by Meridian En-ergy Ltd., an electricity generator and distributor that operates in New Zealand and Australia. Speaking at the IBM World of Watson 2016 conference in Las Vegas last October, Neil Gregory, the company’s reliability engi-neering manager, said his team was migrating away from an 8-year-old predictive maintenance system because of

shortcomings in the way it made predictions—a flaw that Meridian decided it couldn’t live with anymore.

The software, from a vendor Gregory declined to iden-tify, was intended to predict the maintenance needs of

In the final days leading up to the 2016 presidential elec-tion, despite national polling to the contrary, the Trump campaign’s predictive analytics models showed that three key battleground Rust Belt states—won handily by Barack Obama in 2012 and traditionally Democrat strongholds—may swing Republican. As a result, Donald Trump focused much of his campaign strategy on those states to pull out close victories in Pennsylvania, Michigan and Wisconsin and win the White House.

Presidential Predictive Postmortem

Wisconsin

Illinois Indiana

Ohio

Pennsylvania

New YorkMichigan

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assets like generators, wind turbines, transformers, cir-cuit breakers and industrial batteries—essentially all the large equipment the company owns and operates. “Bad things happen when you don’t understand the condition of your plant,” he said. “That’s what our real drive is to do predictive asset management: to avoid that kind of thing.”

But the outdated predictive modeling techniques sup-ported by the system weren’t actually predicting equip-ment failures. Instead, it ran simulations of different

scenarios and predicted when assets would fail the simu-lated tests. That may sound like a small distinction, but a failed test doesn’t necessarily mean a piece of equip-ment will fail in the real world. The discrepancy limited the degree to which plant maintenance teams could rely on predictive recommendations generated by the software.

To replace the old system, Gregory’s team was im-plementing IBM’s Predictive Maintenance and Quality software, which was scheduled to go live in January. He said the new application will let his data analysts incor-porate more-real-time data from equipment to feed their

predictive models. That in turn should enable them to better predict likely failures before operations are af-fected at Meridian.

Going forward, Gregory thinks his team will also be able to do more with machine learning to help ensure that the predictive capabilities of its models improve over time. Meridian is using IBM’s SPSS predictive analytics platform to power the machine learning efforts. As part of that, the data analysts should be able to build predic-tive models in SPSS and “drag and drop” them into the predictive maintenance application, Gregory said. “The ability to ‘learn’ is something we see a lot of value in,” he noted. “There’s some huge potential there for us because we’re a data-rich industry.”

Predictive Models Need Sanity ChecksAcross industries, the main point of adopting a data- driven strategy that leverages predictive modeling tech-niques is to make business decisions more informed and objective—and less influenced by people’s natural cognitive biases. But that doesn’t mean organizations should eliminate human judgment from the analytics process entirely.

“Over time, intuition has to come into play where you say, ‘This doesn’t look right,’” said Dennis Climer, direc-tor of commercial division pricing at Shaw Industries Group Inc., a large carpet and flooring manufacturer based in Dalton, Ga. Shaw uses predictive analytics to de-termine proposed prices for installations of its commer-cial products that are likely to maximize profit margins

“Bad things happen when you don’t understand the condition of your plant.”—Neil Gregory, Meridian Energy

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without giving customers sticker shock. The pricing optimization effort is customized to each sale and based on factors that include the customer’s size and previous order history, the type of project and details about the products involved. Data is pulled from Shaw’s Salesforce customer relationship management system into software from Zilliant Inc., where analytical models are run to predict optimal price ranges. The recommended prices are then fed back into Salesforce and put to use by sales teams.

Climer said the process has made price quotes more certain and, ultimately, profits on contracts more pre-dictable. But it doesn’t run on autopilot. His team con-tinually does health checks on the analytical models to make sure they’re delivering sensible recommendations,

and he makes changes to quotes if the suggested price ranges are outside the realm of what he knows to be rea-sonable based on his experience working with customers.

Monitoring the output of predictive models is im-portant because their performance tends to “drift” over time due to changes in customer behavior and broader trends such as the overall economic health of a market.

For Shaw, it’s also important because sometimes the company might not have enough data to be fully confi-dent that the software is modeling prices effectively—for example, when it’s rolling out new products or expanding into new territories. Climer said that’s when a data ana-lyst needs to step in and make sure the models are giving answers that can actually be used to set prices.

“As long as people are involved, it’s never going to be just [about] math,” he said. “Some things [recommended by predictive models] don’t make business sense.”

Create Clear Roles for Analytics TeamsAnalytics managers also need to be on guard to ensure that the data scientists working for them can focus on developing predictive insights and not get bogged down with more reactive—and less rewarding—duties such as basic business intelligence reporting or data manage-ment tasks.

That was the case at London-based insurance company Aviva PLC. Rod Moyse, Aviva’s head of analytics, said initial projects designed to improve fraud forecasting and predict appropriate monetary settlements for bodily injury claims flew under the radar internally. At the time, most of the company saw Moyse and his 40-person team as reporting specialists, not data scientists whose pri-mary job is to build and run complex predictive models.

“But we had started to think about how to move to something altogether different,” he said. “We needed to change [internal perceptions], and fast.”

“As long as people are involved, it’s never going to be just [about] math.”—Dennis Climer, Shaw Industries Group

(Continued on page 9)

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AMONG ENTERPRISE users of tools for predictive analyt-

ics, the general consensus is that data beats gut feel-

ings for driving business decision-making almost all the

time. But in the political realm, that calculation isn’t so

straightforward.

One big reason Donald Trump gained the upper hand

on Hillary Clinton in the 2016 presidential election is that

Clinton’s campaign based key strategic and tactical de-

cisions largely on data analytics, while Trump appealed

to voters on a more visceral level—seemingly without a

concerted analytics effort to direct his campaigning, at

least initially.

“When you adopt an analytical approach, you’re

looking at things as they are today, but what emotion

can do is change that by changing people’s behavior,”

said Pradeep Mutalik, an associate research scientist at

the Yale Center for Medical Informatics and an election

blogger for Quanta Magazine.

Mutalik said Trump created a so-called reality distor-

tion field, a term originally coined to describe how Apple

co-founder Steve Jobs was able to shape technology

development plans and schedules to his ideals and con-

vince others that the seemingly impossible could

be done. Whether you agree with Trump or not, his

personality radiates passion, just as Jobs’ did, Mutalik

added. That clearly had an effect in energizing his

supporters.

The divide between the approaches of the two cam-

paigns wasn’t entirely neat and clean. Early on, there

was plenty of talk about how unsophisticated the Trump

campaign was when it came to using data; Trump him-

self called big data “overrated.” Later, though, details

emerged that painted a somewhat different picture.

Trump’s campaign eventually invested heavily in an an-

alytics operation that used tools for predictive analytics

to guide messaging, fundraising and campaign

stops.

That said, there’s little doubt the Clinton campaign

put predictive analytics much closer to the center of its

operations. Virtually every decision, from donor out-

reach to messaging changes, was run through predic-

tive models to test its likely efficacy.

The election result doesn’t mean that a candidate

more focused on leveraging analytics can never beat

a more emotion-driven one. In practice, analytics and

emotion aren’t necessarily mutually exclusive. But Mu-

talik said Trump’s victory “exposes some cracks in the

purely data-driven approach”—cracks that corporate

analytics teams would be wise to keep in mind as

well. n

Predictive Methods Aren’t Always Political Winners

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Speaking at software vendor SAS Institute Inc.’s An-alytics Experience 2016 conference in Las Vegas last September, Moyse said the key to overcoming the per-ceptions was focusing on predictive analytics projects that were relevant to upper-level management to ensure they received recognition and acceptance inside Aviva. One project that helped put the analytics team on the map was creating an SAS-based tool to assess whether cars involved in accidents should be repaired or declared total losses. That used to be a lengthy process for claims agents; in the end, though, decisions were often based on

little more than calls between them and customers. Now, the tool looks at pre-accident car values and compares them to repair estimates from mechanics. It’s a fairly simple application, Moyse acknowledged. But the tech-nology turned a highly subjective process into one based more on predictive insights.

Within Aviva, Moyse also worked to define the role a predictive analytics team is supposed to play to avoid fur-ther confusion about that. The prescription, he said, is to not talk about what has happened in business operations but rather what is likely to happen in the future, while

also educating corporate executives and business manag-ers about predictive analytics—“how it works, what we can do.”

High-Quality Data a Must Of course, the most important thing is having good data to work with. Otherwise, even the best-planned predic-tive analytics efforts can go awry, as demonstrated by what happened in the presidential election.

For the Clinton campaign and election forecasters alike, the main problem with their predictions of the outcome appears to have been overreliance on data that turned out to be unreliable: poll results.

“If you’re making a call on an election based predom-inantly on one point of data, you’re making a mistake,” said Michael Cohen, CEO of Cohen Research Group, a public opinion and market research firm in Washington, D.C. “Polling has its place, but it’s not the only thing. When you’re looking at research asking people what they believe, you have to look at other metrics around it.”

Cohen thinks many of the polls before the election were skewed in favor of Clinton because of social de-sirability bias: people telling pollsters they supported Clinton because they thought that was more socially acceptable but then opting for Trump in the privacy of the voting booth. Also, polling didn’t do an effective job of capturing voter enthusiasm, he said. Trump had more energized supporters who showed up at polling places on Election Day in greater numbers than analytical models had predicted.

(Continued from page 7)

“Polling has its place, but it’s not the only thing.” —Michael Cohen, Cohen Research Group

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Predictive modelers looking to forecast elections need to find ways to incorporate these other factors into their models, said Cohen, who is also an adjunct professor at George Washington University’s Graduate School of Po-litical Management.

The good news for corporate enterprises is that their analytics teams are generally in a better position to make predictions compared to those working for campaigns. Businesses tend to have far more data about customer behavior or their operations than campaigns have about voters. In addition, each election is a unique head-to-head matchup, which limits how historical data can be used to model voting.

Even so, problems can arise in corporate applications due to a lack of sufficient data for “training” predictive

models to ensure they produce accurate results. That’s particularly an issue when data scientists are running machine learning applications, said Mike Gualtieri, a principal analyst at Forrester Research. “Machine learn-ing thrives when there’s a lot of historical use cases to learn from—when there are many training events,” he said.

And to avoid business missteps, analytics teams need to know when to hold off on touting the output of predic-tive models that aren’t rock-solid, even if it means scrap-ping them and moving on to something else. “There are just some things you can’t predict,” Gualtieri cautioned. n

ED BURNS is the senior news writer for SearchBusinessAnalytics. Email him at [email protected].


Executive Dashboard

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COMPILED BY RON KARJIAN

For Predictive, the Future Is NowB2B marketers responding to a Forrester Research survey see predictive analytics as an increasingly critical way to expose best-fit and most-likely-to-purchase accounts, increase conversion rates and provide guidance on spending. Sixty-two percent said they’re implementing, planning to implement or expanding predictive capabilities.

Predictive Gains MomentumGlobal data and analytics decision-makers were asked, “What types of analytics are most likely to drive innovation and create new growth for your department and/or firm?”

B2B Marketing’s Biggest ChallengesSenior marketers were asked, “Which of the following describes your top three marketing execution challenges?”

SOURCE: FORRESTER RESEARCH’S “Q2 2016 INTERNATIONAL B2B MARKETING STRATEGIES AND TACTICS ONLINE SURVEY.” BASE: 120 B2B SENIOR MARKETERS

SOURCE: FORRESTER RESEARCH’S “GLOBAL BUSINESS TECHNOGRAPHICS DATA AND ANALYTICS SURVEY, 2016.” BASE: 125 GLOBAL DATA AND ANALYTICS DECI-SION-MAKERS WORKING IN MARKETING AND PR DEPARTMENTS

44%

36%33%

28%

25%

24%

22%

21%

18%

17%

16%

Generating inbound demand that converts to pipeline, revenue

Attributing marketing activity and spend to revenue results

Executing marketing programs that interact on buyers’ terms

Deepening customer relationships to increase cross-sell, upsell

Determining effective tactics to create leads, drive conversions

Understanding the buyer’s journey and purchase process

Converting qualified/accepted leads to deals

Creating and managing content

Managing information about our customers and markets

Nurturing leads to develop sales opportunities

33%

27%

27%

26%

26%

22%

22%

22%

19%

Performance analytics

Predictive analytics

Reporting

Process analytics

Embedded analytics

Web analytics

Metadata-generated analytics

Dashboards

Advanced visualization

Exploration and interactive discovery

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Swamped by DataThe problem was not that the Hornets lacked data. In fact, they were drowning in it, thanks to the many ways modern fans can interact with a team—buying tickets over the internet, purchasing team gear in-store and online, eating and drinking at games, and talking about the team on social media. Every time a fan interacts with the team via one of these touchpoints, it creates a new bit of data about the fan. About four years ago, the Hornets CRM system began to strain at the seams to contain all this data. And even more troublesome for the Hornets, the data all lived in isolation.

Zeppenfeld, whose actual title is director of business intelligence, understood that the team needed a data warehouse to handle the fan

ONE ON ONE

‘Analytics Czar’ Says Hornets Find Plenty of Sizzle in Phizzle

THE CHARLOTTE HORNETS of the NBA have millions of fans who know a lot about the team, but the team didn’t know much about its fans. To be more precise, the Hornets had a lot of data about its fans, but it was all disjointed and lacked cohesion.

To remedy that, Chris Zeppenfeld, the Hornets’ self-proclaimed “customer relationship management (CRM) and analytics czar,” implemented Phizzle Fan-Tracker, a platform built on the SAP HANA database that combined millions of fan records to make one profile for each fan.

The benefit of having an accurate and up-to-date profile of every fan is that the team can build a better relationship with its fans, which can lead to a better fan experience and more revenue for the team as improved marketing efforts lead to more sales.

Analytics chief Chris Zeppenfeld put a full-court press on ownership to implement Phizzle FanTracker and boost Charlotte Hornets CRM and ROI.

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12 to 14 different data sources from about 12 dif-ferent vendors that would need to go in this data warehouse. The second one is, once you’ve done all that, now you have 12 fan profiles in the data warehouse, how do you get this down to one?”

Getting 12 Down to 1Zeppenfeld found the answer to both these ques-tions with Phizzle FanTracker, an application built on the in-memory SAP HANA database that provides a data warehouse and the ability to go through all the millions of fan profiles and eliminate duplicate data to end up with one fan profile. Phizzle also has tools for things like so-cial media listening and website tracking that further help analyze fan behavior.

“We went with the Phizzle FanTracker plat-form because we felt that [it was] going to do the best job of helping to get those 12 profiles down to one,” Zeppenfeld explained. “If I go to our store, I might be ‘Chris Zeppenfeld’ in the retail-ing data, but I might be ‘Christopher Zeppenfeld’

in the ticketing data, and I might be ‘[email protected]’ in the social media listening database. So you’ve got five or six versions of ‘Chris Zeppenfeld’ in all of our data sources, and when we bring them all together in the data warehouse, we really need to get it down to one so we can understand in totality what ‘Chris Zeppen-feld’ is doing, not six versions of him.”

Once the decision was made to go with Phizzle,

data even though this technology was relatively rare for sports organizations at the time. “We had basically maxed out our CRM, [Microsoft Dynamics], because we were trying to shove so many sources of data into it. People can touch our Hornets brand in many ways and a lot of those have different data sources,” Zeppenfeld said. “So we knew we’d have to get a data warehouse, and I saw it as two different challenges. One is we have about

With Phizzle FanTracker, a platform built on the SAP HANA database that combined millions of fan records, the Hornets have a nest full of accurate and up-to-date individual fan profiles. “It was a little bit of a sticker shock for a sports team to do this,” Chris Zeppenfeld said.

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reconciled. “Let’s say we had eight profiles and we got it down to one, but in the ninth database source, the fan does something new like showing up in the social listen-ing database where they’ve never been there before,” he said. “Now we’ve got two and we have to get that down to one again.”

Understanding Fans BetterThe next step is to tie all the data together to create a full fan profile that can help improve the fan experience and generate more revenue for the organization. Previously, the Hornets could analyze the individual data sets for each channel, but now they can compile a complete pro-file that includes real-time behavior across all the data

Zeppenfeld had to convince team ownership the project was worthwhile and there would be enough ROI to jus-tify the costs. Upfront costs in particular were significant due to setup requirements.

“It was a little bit of a sticker shock for a sports team to do this, and at the time, the concept of having a business intelligence department was just barely starting to get off the ground,” Zeppenfeld said. “CRM or analytics people might live in ticket sales, marketing or sponsorship, so they didn’t have their own budget or their own depart-ment. Now it’s much more commonplace, but four years ago we were one of the very first teams to do it.”

To get buy-in, Zeppenfeld had to convince the owners they had outgrown the existing CRM system and that the Phizzle data warehouse platform could help tell them how much each fan was spending and on what. It took the better part of a year, but Zeppenfeld won over own-ership and the Hornets IT team implemented Phizzle FanTracker.

The results have been very successful so far. By feeding all the records from about 20 data sources into the SAP HANA database and using the Phizzle FanTracker API to scrub them, the Hornets found that around 50% of the 19 million records were duplicates. Zeppenfeld estimates that this process saved around $1.5 million and 10,000 consulting hours to do data quality checks, scrubbing and removing the duplicates.

The process is ongoing, Zeppenfeld explained, because even after you’ve consolidated multiple profiles into one, further activities may create new profiles that need to be

n Eighth year with Charlotte Hornets/Hornets Sports & Entertainment as senior director of business intelligence, where he’s responsible for analytics, CRM, data warehouse, surveys, ticket pricing, email marketing and revenue strategy.

n Prior to Hornets Sports & Entertainment, worked four years at TeamWork Online as well as online sports executive re-cruiting firm as senior manager of client services.

n Born and raised in Pittsburgh, now lives in Charlotte, N.C.

n Graduated from John Carroll University.

n Usually spends weekend mornings yelling irrational things at the TV during his beloved Arsenal games.

CHRIS ZEPPENFELD

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was not associated with the season-ticket data. “In reality,” Zeppenfeld said, “they probably spend

$5,000 with us, so we should be dealing with you in two ways: One, we should see you more as a $5,000 account, and two, based on your full picture of what you’re doing in our building, we can do a better job servicing you. If we know you always buy nachos in the second quarter of every game or you bought a Kemba Walker jersey, we have a better way of servicing you. Maybe that’s our ser-vice rep giving you free nachos one game or you get in-cluded in a meet and greet with Kemba Walker. So that’s a powerful way that a data warehouse can get a return on investment.” n

JIM O’DONNELL is the news writer for SearchSAP and SearchManufac-turingERP. Email him at [email protected].

sources. So if a fan buys tickets online or tweets about the game, then it can all go into the fan profile.

“We’re making some sizable investments in sources like retail and food and beverage to better help our fans identify themselves at the point-of-sale so that this has more power,” Zeppenfeld said. “We know which ac-

tions are happening, we just don’t know which particular person it is, and combin-ing those two together will enable us to give our fans a

better experience when they come in the arena because we know what you’re doing.” For example, a season ticket holder who spends $3,000 on the tickets was accounted for as a $3,000 account, but that didn’t include money spent at the games or in the team store because this data

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Big Data’s Huge Impact on Data Prep for Analytics

Be Prepared

With the proliferation of big data environments as well as the expansion of predictive analytics applications, effective data preparation as a critical first step in analysis calls for new tools and methods.

THE BENEFICIAL CONNECTION between preparation and op-portunity has been noted by more than a few sages, from the Roman philosopher Seneca to American self-help entrepreneur Tony Robbins. But data preparation’s role in the opportunity known as big data analytics is often underappreciated, if not overlooked completely.

The data preparation process can be a stumbling block that stands between advanced analytics technologies and the business benefits organizations hope to get from them—increased revenues, more efficient operations, better decision-making and more. And as big data envi-ronments proliferate, the work involved in integrating and preparing data for analytics uses is changing in some notable ways.

On the front end, there are more—and more BY JACK VAUGHAN

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diverse—sources of data with which to work. All that variety spices up the big data analytics stew, but it upends traditional data pipelines. The days of a one-directional data flow into an enterprise data warehouse are ebbing; in the big data world, data often needs to move back and forth between data warehouses, Hadoop clusters, Spark systems and other platforms to support different analyt-ics applications.

There’s also more variety on the back end, more spe-cifically, in the needs of the people using the data. For example, data scientists are likely to want access to raw data so they can filter it as needed to support particular predictive analytics or machine learning applications. That creates more steps to navigate on data preparation than a typical business analyst would require.

To meet those increasingly complex needs, one data pipeline may support a lot of automation as part of the data preparation process, while another may have to be structured to enable data scientists to play around with data in the analytics equivalent of a sandbox, walled off from the main data store or set up on a separate system. For IT teams, that means incorporating a mix of capabili-ties into data workflows to ensure that different analytics users can access the right information for what they’re looking to do. And that’s not always easy to accomplish.

It’s Not Just About Size“Today, you see the rise of big data—but it’s not just about the size of the data, it’s also about multiple data sources,” said Jason Brannon, supervisor of data architecture at

insurance company USAble Life in Little Rock, Ark.USAble offers term life policies and a variety of med-

ical insurance options meant to supplement primary coverage in case of accidents, serious illnesses and other health issues. The insurer’s operations require accurate synchronization of insurance enrollment information be-tween the company, its Blue Cross and Blue Shield busi-ness partners, and its customers. From a data processing and analysis standpoint, “the requirements are increas-ing every day,” Brannon said.

The variety of data requiring preparation for analysis is also an issue for Brock Christoval, founder and CEO of Flyspan Systems Inc., Irvine, Calif. His company is building a data analytics platform for the commercial drone industry. The system, called FlyView, is being set up to pull in a wide range of sensor data from drones via the internet of things (IoT). Analytics teams at com-panies with fleets of drones will then be able to trawl the data both to analyze drone activity in the field and

“It allows us to … distill [the raw data] down to what the decision- makers need and put

that in their face.”—Brock Christoval, Flyspan Systems

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JEWELRY TELEVISION, a shop-from-home network and

online retailer known as JTV, is moving to add some an-

alytics sparkle that goes beyond after-the-fact reports.

It now puts information from predictive analytics mod-

els on the screens of on-air hosts to help them better

align their jewelry and gemstone pitches with viewer

demand.

But the new focus on predictive modeling is also

pushing the Knoxville, Tenn., company to polish up its

approach to integrating and preparing data for analysis.

Regression analysis algorithms that aim to iden-

tify relationships between different data variables are

among the types of predictive models being used at JTV,

according to CTO Chris Meystrik. The advanced analyt-

ics applications drive real-time business operations and

decisions; for example, their output shows up on dis-

plays in the broadcast studio that look like “an airplane

cockpit,” Meystrik said.

Getting predictive information on customer buying

patterns in front of JTV’s hosts helps them focus on

selling items that are on the way up the curve of fash-

ion popularity. At the same time, they can use analytics

data to avoid overhyping items that may be available

only in small quantities, a sales misstep that could

disappoint customers interested in those goods.

To make the predictive modeling possible, the

shopping network uses data integration tools from In-

formatica to move a mix of customer relationship man-

agement, inventory, order data and other information

into its analytics systems. Meystrik said a notable

difference from the past is that much of the required

data tends not to already reside in a data warehouse—

social media and website clickstream data, for

example.

There are also differences in data preparation be-

tween basic reporting and predictive analytics. In

Meystrik’s view, the initial steps required in preparing

data for analysis can be quite cumbersome. “But once

we have the algorithms on a solid footing and operat-

ing with acceptable precision, they generally become

self-sufficient,” he noted.

Yet things could get even more complicated. To push

its operational analytics efforts forward, JTV is currently

experimenting with machine learning and open source

data streaming and message queuing software. As jew-

elry sales move further into a post-brick-and-mortar era,

network executives see analytics applications as a com-

petitive differentiator for the company. n

Predictive models built on solid-gold data prep

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support predictive maintenance on the devices.“We’ve done work that required handling video data

streams, telemetry data and everything in between,” Christoval said. Increased use of the JavaScript Object Notation (JSON) data-interchange format may bring some uniformity to that process for things like telemetry, he added. But IoT and the drone industry are both rela-tively new, and Christoval expects the disparity of data types to remain the rule rather than the exception for Flyspan and its customers.

Data Prep Software Options GrowEstablished data management vendors like IBM, Infor-matica, SAS, Syncsort and Pentaho offer tools to help users handle the increasing data deluge. But the acute need to take in diverse data and ready it for different uses has given rise in recent years to a group of new vendors that focus on aspects of those issues through self-service data preparation software and other technol-ogies. The contenders include Alation, Alteryx, Attivio, Datameer, Looker, Paxata, RedPoint Global, Tamr and Trifacta.

The data management team at USAble Life uses Pen-taho’s namesake software, which Brannon said has been particularly helpful in reducing work related to scripting extract, transform and load (ETL) integration jobs. For example, a so-called metadata injection capability that Pentaho added in April 2016 automates ETL and data preparation steps, speeding repetitive workflows.

The software frees USAble’s developers from “having to support one form of scripting for file manipulation and another for ETL—it puts all the dependencies in one place,” Brannon explained. “As a result, it simplifies our development process, and allows us to be much more re-active to demands for data.” Cutting time out of the data integration and preparation cycle is especially important to him because he has to meet the analytical data needs of both internal and external users.

At Flyspan, Christoval sees benefits in the ability of Trifacta’s software to provide an overall view of the data preparation process and automate the required steps. “It allows us to wrangle the disparate types of data we get from telemetry, distill [the raw data] down to what the decision-makers need and put that in their face,” he said.

‘No Hands’ on DeckSuch automation is becoming an imperative as more and more data needs to be processed a lot faster. “The new tools fit well because we’re moving toward more ‘hands-off’ workflows,” said Dave Wells, an independent consultant and industry analyst who wrote a report on data preparation software and tools for managing data pipelines that was published last November by consult-ing firm Eckerson Group.

David Stodder, a director at IT research and education services provider TDWI, also credited self-service data preparation tools for his company’s automated approach and its focus on big data and advanced analytics applica-tions. “They’re all trying to reduce the number of steps

(Continued from page 17)

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required, to make things more repeatable and easier,” he said—something that’s more important for predictive an-alytics and machine learning involving large and diverse data sets than it is for mainstream business intelligence and reporting.

A TDWI report published in July 2016 that Stodder authored bears out the growing need to blend different types of data for analysis—and the challenges IT and ana-lytics teams face in doing so.

In the report, Stodder wrote that users increasingly want to see an integrated view of data to help them identify relationships, correlations and trends. Not sur-prisingly, relational databases and data warehouses lead the array of data sources enabled for self-service data preparation, according to a survey conducted for the re-port. But JSON, clickstream, social media and real-time streaming data are among newer types of information that survey respondents are adding to the analytics mix (see “What’s in the Data Blender?”).

Nonetheless, in many organizations, “users across the spectrum deal with data chaos every day,” Stodder wrote. Only 43% of 411 survey respondents said their users were satisfied with how easily they could find and under-stand relevant data; on the other hand, 37% said users were either somewhat dissatisfied or not satisfied at all. The increasing data demands and associated problems are prodding companies to rethink the traditional data preparation process, Stodder said, noting that improve-ments can “help both [the] business and IT become more productive and effective.”

Data Pipeline’s Shifting ShapeIn more and more businesses, the drive to set up big data architectures that can support predictive analytics, data mining and machine learning applications is changing the shape of the data pipeline as well as and the data preparation steps required to feed it.

“We used to live in a very straightforward world where data moved in one direction—it was a data flow into a data warehouse,” Wells said. “Now we have data ware-

houses, data lakes and data scientists’ sandboxes. There are many sources, and they’re processed in many ways. And the data pipelines now are multidirectional.”

The overall effect is that strictly linear approaches to data flows are breaking down. And data scientists and other users whose analytical interests are exploratory or discovery-oriented in nature must be served by data management teams, Wells explained. The nature of predictive analytics changes the way analysts deal with data, according to Jin On, a data scientist at Geneia LLC, a Harrisburg, Pa., company that provides analytics soft-ware and services to clients in the healthcare industry.

“You have to look at the actual data first and see what it’s really telling you.”

—Jin On, Geneia

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“At the beginning of my career, the [analytical] models I built out were more about descriptive statistics,” On said. “You’d ask a strict question about how many people have diabetes and get a blatant answer.”

Predictive modeling is different, she added. For

example, one of the applications she works on is aimed at predicting how likely it is that individual patients will need to be readmitted to a hospital. “For that type of analytics, you need more creativity,” On explained. “You have to look at the actual data first and see what it’s really

n CURRENTLY USE n PLAN TO ENABLE

What’s in the Data Blender?Self-service data preparation tools are primarily being used to pull together data from traditional

sources now. But newer types of data are on the rise.

SOURCE: TDWI’S “IMPROVING DATA PREPARATION FOR BUSINESS ANALYTICS.” BASED ON RESPONSES FROM 276 IT AND BUSINESS PROFESSIONALS WHO WERE ASKED ABOUT A TOTAL OF 16 DATA SOURCES AS PART OF A SURVEY CONDUCTED IN MARCH AND APRIL 2016

TOP DATA SOURCES UP-AND-COMING SOURCES

Web analytics/

clickstream data

Geospatial data

Social media data

Real-time streaming

data

32%29%31%

24%30% 29%

22%

16%

Relational databases

Data warehouses

Spread- sheets/ desktop

databases

BI reports

75%72%

68%

61%

18%

11%11%12%

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telling you about the attributes that contribute most to the likelihood of a readmission.”

Back for More on Data PrepSome of On’s work takes her into the realm of machine learning, which often requires that raw data be main-tained as is and then filtered in different ways to meet particular analytical needs. She said that after assessing the characteristics of the available data, the next step is to look at the types of machine learning algorithms that can be used to boost the accuracy of the planned model’s predictions.

The data requirements formulated by On, who uses SAS software to prepare data and build predictive mod-els, can vary with different machine learning algorithms. A case in point is the Random Forest algorithm, which places restrictions on the number of levels that certain categories of data variables can have, according to On. That often means additional data preparation steps to massage the data to match her specifications. “You have to go back into data preparation in that case to tweak the data so it works with a particular algorithm,” she noted. “That’s one reason I want to [examine] my data first, be-fore I start exploring it.”

Trash for Some, Treasure for OthersFor data managers, new approaches to data preparation— ones that are far from cookie-cutter methods—are neces-sary to support advanced analytics needs, said Eric King,

CEO of The Modeling Agency LLC, an analytics consult-ing and training services company.

Even one of computing’s most time-tested con-cepts may be in for reworking: garbage in, garbage out (GIGO), which holds that users will never get good re-sults out of bad data. “Back in the day, it was all about GIGO, but it isn’t anymore,” said King, whose company teaches courses on the subject of data preparation for predictive analytics. He said prescribed data preparation

steps typically involve a lot of binning, smoothing and fitting—much of it meant to discard outlier data as a way of “separating the signal from the noise.”

In big data environments, though, “such cleansing may not be what the data scientist or the successful predictive modeler wants,” King said. “At the same time, new algo-rithms can handle a good bit of noise and garbage.” As a result, he added, “it can be wasteful to over-cleanse the data.” There are steps that need to be taken—but when preparing data for analytics uses, sometimes it pays to be judicious. n

JACK VAUGHAN is news editor for SearchDataManagement. Email him at [email protected].

“Back in the day, it was all about GIGO, but it isn’t anymore.”—Eric King, The Modeling Agency


Verbatim

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Peering Into Corporate Crystal BallsBusiness Information asked IT and analytics professionals how predictive analytics has improved their business operations.

“ Our use and sophistication is growing, and with that growth, our understanding of this very complex business grows along with our ability to serve our customers.”CHRIS MEYSTRIK, CTO at shop-from- home network Jewelry Television

“ We’re a very data-driven company. If you show up for a meeting and you don’t have a piece of data— no.”PAWAN DIVAKARLA, data and analytics business leader at Progressive Casualty Insurance Co.

“ We apply all these statistical algorithms to help us see what’s happening with the business. It’s a key component.”SEETHA CHAKRAPANY, director of marketing analytics and customer relationship management systems at retailer Macy’s Inc.

“ There’s lots of momentum around predictive analytics. It’s a hot technology; machine learning is making it hotter.”FERN HALPER, vice president and research director at TDWI

“ In my experience, the degree to which predictive analytics improves operations is largely an organizational question. It’s cultural and behavioral—about how much people use it.” DAVE WELLS, independent consultant and industry analyst

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SCOTT ROBINSON

DATA DECISIONS

Big Data’s Ugly ChildDescriptive analytics is powerful. Predictive analytics, flashy. Prescriptive analytics? Boring, perhaps, but no less essential to improving operations in the enterprise.

IN THE BEGINNING, there was descriptive analytics: data- parsing methodologies that cleverly analyzed large amounts of data about customers, products, financials or most anything else and yielded insightful new catego-ries for those items. Predictive analytics then followed as an even more dazzling practice that could fine-tune our understanding of “what comes next” with great accuracy and granularity so we could maximize our time and in-vestment in planning for the outcome we want.

Next in line is prescriptive analytics: the science of out-comes. It’s less intuitive and much harder to embrace, yet it feeds the enterprise the kind of news we don’t nec-essarily want to hear. Descriptive and predictive results simply provide better data for making decisions—always a good thing—and an important refinement of what is already happening. But prescriptive results take it a step

further: They tell us what to do. That makes prescriptive at least as important as its siblings in moving the enter-prise forward.

Prescriptive models don’t just inform those who are involved in the decision-making process, they are the decision-making process. They articulate the best out-come, which can create friction among those who aren’t comfortable relinquishing their decision-making respon-sibilities to a machine.

Playing By the (Changing) RulesPrescriptive models also require careful framing, or rules, to produce outcomes according to the best interests of the business. When prescriptive analytics is applied, the process itself needs to include as much information as possible about the enterprise by creating a framework for interpreting the prescriptive results. That framework is built on business rules.

Business rules defining the enterprise’s operations serve to gauge the impact of prescriptive recommen-dations on operations, efficiency and the bottom line. Projected outcomes are brought in line with institutional priorities, values and goals. The rules are based on estab- lished policy, best practices, internal and external con-straints, local and global objectives, and so on. They determine to what degree prescriptive recommendations and anticipated outcomes truly work.

The rules must be dynamic; organic; and, to some

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speeches and white papers. Codifying and classifying this diverse amount of

data is cumbersome and expensive; perhaps the most off-putting of all are the prescriptive analytics compo-nents. Building processes to capture and format this kind of data can be viewed as a serious impediment to implementation. Yet, the task is essential. It can mean the difference between adequate and perfect modeling solutions.

The healthcare industry has been a leader in modeling prescriptive solutions with the environment. The service provider’s need for efficiency is greater than ever because of the massive changes in healthcare economics in recent years. Capacity planning is a key factor in optimizing lo-gistics and resources for service delivery. The models in-corporate vast amounts of environmental data, including highly granular demographics, trends in health by region, and economic conditions at both national and regional levels. By using these models, many healthcare providers are adjusting near-term and long-term investment plans for optimal service delivery.

Prescriptive analytics closes the big data loop. It’s a natural endpoint for the descriptive and predictive pro-cesses that precede it. Whatever the hype and hoopla surrounding prescriptive models, its success depends on a combination of mathematical innovation, mastery of data and old-fashioned hard work. n

SCOTT ROBINSON is a social collaboration consultant for the Louis-ville Metro government in Kentucky. He’s also a regular contributor to SearchContentManagement. Email him at [email protected].

degree, fluid. The entire point of an analytics-based in-stitutional culture is acquiescence to the objective reality of real-world data. A corporate self-image based on that data will necessarily evolve. It follows that the business rules driving prescriptive analytics must also evolve. Therefore, the prescriptive process and the successful outcomes it delivers will feed back into the rules and steadily refine them.

An electronics manufacturer in southern Indiana put this idea to work in selecting its optimum long-term cus-tomer contracts. Though its headquarters are in the U.S., most of its actual manufacturing facilities are located on other continents. Capacity to manufacture and deliver in those other countries is governed by a number of risk factors involving fluctuating availability of raw materials, economic conditions affecting logistics and employee turnover. So the business rules applied to the company’s contract evaluation process are critical to the accuracy of analysis and must be adjusted frequently.

Data Inside and OutAnother daunting challenge is hybridization of inputs into the prescriptive process. Descriptive and predictive processes use data that’s carefully preformatted and well-thought-out. Prescriptive processes must model diverse facts, features and events from inside and outside the enterprise. That’s called environmental data, and it can be messy because it’s composed of unstructured and multi-sourced data that potentially includes everything from internet posts and video to free-form text based on


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WHAT’S THE BUZZ?

Gut Instincts Take Back Seat to Data-Driven Business Decisions

DATA-DRIVEN BUSINESS models have caught fire as com-panies find ways to use the vast amounts of data they collect to gain a competitive edge. But dig deep into busi-ness decisions and you’ll likely find many of those deci-sions aren’t data-driven at all. Most traditional company executives still make decisions based on gut instincts and narrow observations. As one technology strategist said, “Data informs, but belief rules.”

The BuzzAccess to data has changed. More data than ever is being collected, and it’s available to people at all levels. Compa-nies gather data from a variety of sources and are learning to tell stories with that data. Analyst firms and consul-tants urge companies to rely heavily on data to drive their decision process. Moving to data-driven business models, they say, results in a quantifiable competitive edge.

The RealityUsing data to drive business decisions requires a change in strategy, culture and operations. It might mean hiring a chief data officer—Gartner predicted 90% of com-panies will hire a CDO by 2019. Companies must also invest in the infrastructure, analytics tools and technical know-how necessary to collect and manage multiple sources of data and then analyze that data effectively to predict business outcomes. Still, the biggest challenge is organizational as companies struggle to secure the right talent, balance intuition with hard data and implement business processes that turn data into dollars. n

—BRIDGET BOTELHO, Editorial Director

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CRAIG STEDMAN

real-time architecture and using it to run streaming data analytics applications is a complicated undertaking.

For starters, streaming analytics systems don’t come in a box—not even a large one. Setting them up is an artis-anal process that requires prospective users to piece to-gether various data processing technologies and analytics tools to meet their particular application needs. In addi-tion, the technology options have increased significantly over the past few years, thanks largely to the emergence of multiple big data platforms that provide stream pro-cessing capabilities in different ways.

Plethora of Streaming PlatformsSpark Streaming, Flink, Storm, Samza, Pulsar, Druid, Kylin—they’re all open source processing engines vying for a piece of the data streaming and real-time analytics action. Even Kafka, originally a messaging technology for feeding data from one system to another, now also func-tions as a stream processing platform in its own right. In addition to the open source tools, various IT vendors offer more traditional complex event processing systems that began emerging in the late 1990s. Specialized data-bases—in-memory ones, for example—are also built to handle streaming data analytics.

On the analytics software side, broader use of machine learning algorithms is making it more feasible to build predictive models that can churn through large amounts of streaming data on things like financial transactions,

HINDSIGHT

Building Real-Time Analytics Systems? Not So FastRunning predictive models against streaming data can be extremely advantageous to business operations, yet assembling real-time analytics systems to support those applications is a daunting task that requires careful contemplation upfront.

WHEN I WORKED at a fast-food restaurant in high school, a co-worker friend and I decided its motto should be “Speed, Not Perfection.” We silkscreened t-shirts for the two of us with that phrase embedded in the corporate logo—two smart-aleck teenagers gently sticking it to the man.

Nowadays, data management and analytics teams increasingly find themselves being asked to fulfill the speed part to enable real-time data analysis in their or-ganizations. But they don’t have the luxury of being able to get by with the same sort of occasional sloppiness that my friend and I did in slapping burgers together. And that puts them under a lot of pressure, because creating a

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time lets business operations act fast, and that clearly can be to their advantage. Predictive analytics applications run against streaming data on the web activity of con-sumers can drive website personalization programs and targeted online advertising and marketing campaigns. Fraud detection, predictive maintenance and satellite imaging are other applications that can benefit from streaming data analytics.

In many cases, real time might be the only time to take advantage of what’s in the data being collected. Stream-ing analytics tools point to “perishable insights” that need to be acted upon quickly before the opportunity is lost, Forrester Research analyst Mike Gualtieri and then-colleague Rowan Curran wrote in a 2016 Forrester Wave report. And you can’t get those kinds of insights simply by throwing data into a Hadoop cluster, as Darryl Smith, chief data platform architect at Dell EMC, said during a presentation on the data storage vendor’s re-al-time streaming efforts at Strata + Hadoop World 2016.

Speed is indeed a wonderful thing. Just be sure your team has a well-thought-out plan before turning up the heat on a streaming analytics initiative. Otherwise, it might end up getting flame-grilled by disappointed busi-ness executives. n

CRAIG STEDMAN is an executive editor in TechTarget’s Business Applications and Information Management Media Group. Email him at [email protected].

equipment performance and internet clickstreams. But again, there are a multitude of technology choices to consider: tools from mainstream analytics vendors and machine learning specialists, cloud-based services, open source platforms.

As with building a big data architecture in general, the surfeit of software available to underpin a real-time ana-lytics architecture can be a boon for users—or mire them in a veritable boondoggle of a deployment. Finding the right technologies and combining them into an effective analytics framework is a perilous process; missteps can send a project careening off the intended path.

Streaming Forward on PredictiveThat isn’t stopping companies, particularly large ones with lots of data and ample IT resources, from giving it a go. In an ongoing survey being conducted by SearchBusi-nessAnalytics publisher TechTarget Inc., 28.1% of the 7,000-plus IT, analytics and business professionals who had responded as of mid-January said their organizations were looking to invest in real-time analytics technology over the ensuing 12 months. In addition, 13.4% said they

planned to buy stream processing software.

Why do it? The ability to pull useful information out of data streams in real

Read more columns by Business Information editors.


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Business Information is a SearchDataManagement.com e-publication.

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