february 2015, volume 3, number 1 business...

Passion, Political Roots Push Exec on HR Upgrade

A Spark of Interest Celso Mello: On Becoming a CIO

Jack Gold: Better Together?

For Many, BI Glitter Is Not Yet Gold

FEBRUARY 2015, VOLUME 3, NUMBER 1

Business InformationINSIGHT ON MANAGING AND USING DATA

The Right Stuff To make sense of their banks of big data, organizations have to zero in on the information they really need—and put aside what they don’t.

PLUS: A Dip in the Data Lake

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PASSION, POLITICAL ROOTS PUSH EXEC ON HR UPGRADE

A SPARK OF INTEREST

ALPHA ANYWHERE 3.0 TAKES MOBILE APPS OFFLINE

CELSO MELLO: ON BECOMING A CIO

BIG DATA ANALYTICS NOT JUST A GRAB- AND-GO PROCESS

DIP IN DATA LAKE CAN BE BRACING FOR BIG DATA USERS

JACK GOLD: BETTER TOGETHER?

FOR MANY, BI GLITTER IS NOT YET GOLD

2 BUSINESS INFORMATION • FEBRUARY 2015

THE STORIES YOU hear about what can be accomplished with big data can be truly mind-boggling. The real-time stats package Major League Baseball generates with each pitch and at-bat elicited the biggest oohs and aahs at the AWS Re:Invent conference late last year. Super-accurate weather and soil condition forecasts are helping Georgia farmers optimize irrigation plans.

Whatever the reason for running or experimenting with big data, the same filters must be applied to big data as to any big IT project: What’s the business problem to be solved? Can big data technology solve it? Are we ana-lyzing the right (or right amount of) data?

The consequences can be overwhelming. As Harvard Business School professor Clayton Christensen says in the cover story in this issue of Business Information, “Big data for big data’s sake just gives us more data, and that’s not the insight I think we need.”

At Allstate Insurance Co., quantitative research and analytics fellow Mark Slusar found the right mix in a Hadoop data lake: “Previously, a lot of the data we looked at was only at the state level because data at the country-wide level was so large that we didn’t have an effective way to work with it,” he says. “Now it’s more or-ganized and centrally located, and the computing power is leaps and bounds faster than before. What used to take

months now takes hours.” Analyzing that data enabled Allstate to reduce the

number of property inspections it does, saving the in-surer about $3 million in 2014.

Still, big data projects shouldn’t be approached with-out careful thought. The investment in technology needs to be matched by investment in human expertise—to de-fine the problems and data sets, to ask the right questions about the data and to pull relevant information from the data.

Speaking of expertise, I’d like to welcome a new columnist to the Business Information lineup: Celso Mello, CIO of Reliance Home Comfort, a supplier of home heating and cooling systems in Canada. In each issue he will bring an IT executive’s perspective to solv-ing business problems with technology. His message in this issue, about how to become a CIO, even applies to the problem of big data. “A high-profile project can trans-form a business, so it requires executive involvement,” he writes.

Good advice for anyone who understands the value of technology. n

Are you venturing into big data landscape? Write me at [email protected].

EDITOR’S NOTE | SCOT PETERSEN

The Right Insight

mailto:[email protected]

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TREND SPOTTER | EXECUTIVE DASHBOARD

HARMON.IE’S THE STATE OF MOBILE ENTERPRISE COLLABORATION 2014; BASED ON RESPONSES FROM MORE THAN 1,400 BUSINESS AND IT PROFESSIONALS

HR’s HorizonsOrganizations are still looking for core, administrative functions in human resources software. But a sharpening focus on employee self-service, social media and the use of analytics to determine workforce size and compen-sation will lead to huge increases in demand for more cutting-edge features.

n IN USE TODAY n WITHIN THREE YEARS

Administrative

Service delivery

Workforce management

Talent management

Social media tools

Business intelligence and reporting tools

Workforce optimization

SIERRA-CEDAR’S 2014-2015 HUMAN RESOURCES SYSTEMS STUDY, WHICH SURVEYED 1,063 ORGANIZATIONS WITH AT LEAST 100 EMPLOYEES

92%

49%

46%

55%

41%

41%

12%

96%

76%

65%

80%

68%

65%

44%

Can access email on mobile

devices

Know there’s a mobile device

policy

n IT PROFESSIONALS n BUSINESS PROFESSIONALS

80% 83%

46%

96%

Time to Move OnMore organizations today are giving employees mobile access to internal systems, but that doesn’t close the gulf between business and IT.

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TREND SPOTTER | VERBATIM

“ One is building analytical skills in the organization. We have all this data, but that doesn’t mean we’re turning it all into real business information. To do that, you need analytical skills, and I think those skills are at a premium.”MIKE MASCIANDARO, director of BI and reporting at Dow Chemical

“ Dashboards. We need to know when we aren’t meeting our compliance requirements in claims processing. We want to make obvious the things that need to be obvious.”SCOTT DOSCHER, director of government programs at pharmacy benefits manager Express Scripts

“ We’ve just created a shared BI infrastructure. Now we’re putting similar types of [technologies] together to create an analytical hub that any of the business units can use.” BOB FITZGERALD, director of the BI center of excellence at Prudential Financial

“ It should be data governance. Everything’s a one-off project now. If we did data governance, the momentum would carry on to a lot of things, like building a real data warehouse.”MICHAEL BERGELSON, BI analyst and data architect at New York-Presbyterian Hospital

“ We were just doing BI reporting, but now we’re moving more toward predictive modeling. It’s tough because we don’t know if we have the expertise.”NAVEEN PAHILWANI, manager of BI at a financial services company

Learn more about priorities for 2015 in an episode of the news show BizApps Today.

BI To-Do ItemsBusiness information asked attendees at TechTarget’s 2014 BI Leadership Summit in December what their top business intelligence and analytics priorities were.

http://searchdatamanagement.techtarget.com/video/Geolocation-tools-IoT-make-data-governance-paramount

http://searchdatamanagement.techtarget.com/video/Geolocation-tools-IoT-make-data-governance-paramount

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CHERYL CAREY SAYS passion for her nonprofit’s mission was critical in helping her lead a dramatic change in hu-man resources technology.

When she faced resistance from employees and other hurdles during a human capital management system up-grade, Carey, an IT manager at Boston-based Year Up, fo-cused on its purpose, which is to train low-income young adults for jobs in technology, finance and other fields.

“I have great passion for what the organization does,” Carey said. “Their mission is nothing short of amazing. I know that what we do today will help achieve the growth plans tomorrow, which then helps more students. That really gave me the energy and the team the energy to say ‘OK, this is a change. And it is a big change, but it is a needed change.’”

Implementing the cloud-based software from Corner-stone OnDemand will give Year Up the world-class HCM system it needs to achieve plans for doubling the number of employees over the next two years, Carey said. The agency currently employs 453 people at 13 offices around the U.S. According to its website, it offers 18-to-24-year-olds six months of classroom training and a six-month internship at a long list of corporate partners, including Microsoft, Fidelity Investments and Workday.

Carey, 48, is director of application operations and

TREND SPOTTER | MEETING ROOM


Passion, Political Roots Push Exec on HR Upgrade

Cheryl Carey, director of

application operations and

advancement at Boston nonprofit

Year Up, works from her home office in Man-

chester, N.H.

PHOTO: SEVANS/TECHTARGET

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advancement. In 2014, she led the deployment of Cor-nerstone for application tracking, which went live in September, and performance management, launched on Dec. 1. This year, she plans to add Cornerstone processes for succession planning and improving feedback from employees, as well as ramping up formal learning. The latter was in place before her arrival but was never fully used.

A Family AffairCarey grew up in an Irish family, the younger of two daughters of Timothy Creedon, a now-former longtime Somerville, Mass., city alderman, and Elaine Creedon, who was a part-time pharmacy clerk and homemaker. Both parents were Irish immigrants.

As a political family, they often took different sides of an issue around the dinner table.

In light of the family debates, she said it’s ironic that her sister, Joan Peddle, became a vice president of human resources at Fidelity Investments, while she became a di-rector of human resources information systems, or HRIS.

“We were brought up to look at both sides of a situa-tion,” she said. “Now, it’s as if we are in the same field but on opposite sides of the coin.”

Carey believes that being steeped in politics as a child improved her skills in dealing with people. She and her sister held signs during their father’s campaigns and helped design some of the literature. “It was a great ex-perience,” she said. “It gave us a lot of diverse exposure, which I do think was helpful.”

She obtained a bachelor’s degree in IT and then a law degree from Suffolk University in Boston, later earning a graduate certificate in human resources from Southern New Hampshire University.

Before starting at Year Up, she was director of HRIS and compensation at Comcast Corp. from 2007 to 2011 and then senior manager of shared services at Comcast for two years.

She received plenty of preparation for her current assignment when she spent three years helping lead de-velopment and deployment of an SAP human resources system at Comcast, including a self-service portal for em-ployee transactions, a time-tracking system and an over-haul of payroll and application tracking. “It’s very much a shadow of what we are doing here at Year Up,” she said.

Before choosing Cornerstone, Year Up used some-times-chaotic manual processes for tracking applications and managing employee goals and performance, Carey said. Hiring managers struggled to access candidate pools, and job applications and postings weren’t being tracked accurately. Performance reviews were spotty and sometimes disorganized.

CAREY BELIEVES THAT BEING STEEPED IN POLITICS AS A CHILD IMPROVED HER SKILLS IN DEALING WITH PEOPLE.

Read more profiles of business and IT professionals.

http://searchbusinessanalytics.techtarget.com/essentialguide/Change-agents-Leaders-in-information-management-technology

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Year Up used Taleo software for applicant tracking, but it wasn’t structured properly, and a lot of manual work was still being done, she said.

The organization chose Cornerstone partly because it could easily be integrated with existing platforms, in-cluding SAP-owned Concur for travel and expense, and ADP for payroll and for managing employee promotions and demotions, she added.

People SkillsIt took a lot of work to prepare the organization for the new processes, including building a project team and es-tablishing clear requirements, according to Carey. “What did we need from the product? Most of the time was spent there.” Any disagreements were settled by a sepa-rate steering panel of senior staff in HR, IT and finance. Operational leaders, or “champions,” were named to manage the changes at each office.

She said it was difficult to establish new standards for HR processes at sites that largely functioned as their own businesses. First, she needed to survey each office to learn its existing requirements for processes, such as performance reviews. The team would then boil them down to a single requirement for the entire organization. “We are certainly positioning ourselves to grow,” she said. “To do that, there are operational pieces that need to be

standardized. That was the biggest change element.”Knowing that some employees would resist the new

technology, she set up a team to guide the HR changes. Members traveled to Year Up offices to try to reassure people and calm their fears, in part by emphasizing that the changes would be exciting.

She also worked to use any opposition to the nonprof-it’s advantage. “We engaged those groups and brought them into the project and asked for their concerns,” she said. “We also tapped those groups as testers. They would test the hardest.”

The easy part was deploying the Cornerstone products for application tracking and performance management, she said. Employees are just starting to use performance management while application tracking is resulting in more job postings and improved screening. —DAN RING

“ WE ARE CERTAINLY POSITIONING OURSELVES TO GROW. TO DO THAT, THERE ARE OPERATIONAL PIECES THAT NEED TO BE STANDARD-IZED,” CAREY SAYS.

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APACHE SPARK IS an open source data processing engine that emerged from the labs at the University of Califor-nia, Berkeley, in 2010 and burst onto the big data scene in a big way last year. The Apache Software Foundation released version 1.0.0 of Spark last May, and big data ven-dors have lit a marketing fire under the technology, tout-ing it as a faster and more flexible alternative to MapReduce for processing and analyzing Hadoop data.

THE BUZZSpark addresses some of the shortcomings of Map- Reduce, Hadoop’s original processing engine. At Spark’s heart is an in-memory computing layer that propo-nents say can run batch-processing programs up to 100 times faster than MapReduce can. Spark also is a more

general-purpose technology that’s suited to machine learning, streaming, graph processing and SQL querying applications in addition to batch jobs. And it uses high-level APIs and libraries, making application development easier than it is with greasy and grimy MapReduce.

THE REALITYThus far, Spark has gotten far more vendor hype than user adoption. And it has plenty of maturing to do. For example, tools that connect it to SQL are very new. Also, its in-memory capabilities may prove to be expensive for some uses. And while its APIs are less complex than MapReduce’s, they’re beyond the ken of most enterprise developers. It’s still possible that Spark could flame out instead of burning brightly. —JACK VAUGHAN

TREND SPOTTER | WHAT’S THE BUZZ?

A Spark of Interest

Lighting a Spark

2009: Computer scientist Matei Zaharia creates Spark at UC Berkeley’s AmpLab as part of his doctoral studies

2010: Spark is made open source software; it draws a development community on code management site GitHub

2013: Project is donated to The Apache Software Foundation; an inaugural Spark Summit is held in San Francisco with more than 450 attendees

2014: Apache releases Spark 1.0.0, followed by two more releases; big data vendor Databricks, co-founded by Zaharia, uses Spark to set a new benchmark record in large-scale sorting—100 TB of data in 23 minutes

2015: Spark Summit East to be held in New York in March


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INNOVATION SPOTLIGHT

Alpha Anywhere 3.0 Takes Mobile Apps Offline

For more details on Alpha Anywhere 3.0, read the full story on Search-SoftwareQuality.

WHAT IT IS

Alpha Anywhere is a tool for developing mobile applica-tions. Vendor Alpha Software calls it “low-code,” meaning it doesn’t require much hand coding to build apps. CTO Dan Bricklin said the platform also gives developers the option to customize code when needed.

WHY IT MATTERS

Developers can use Alpha Anywhere to quickly construct prototype appli-cations. And with the new 3.0 release, they can build Web-based mobile apps that can operate when an Internet con-

nection is lost—without the need for other tools.

WHAT USERS SAY

It works in a pinch, says Sagrika Mehta, a developer at Oxford Instruments. She used Alpha Anywhere to build a workaround app after a data migration nearly crippled the company. Consultant Uday Ondhia says the dozen-plus features Alpha plans to add

is cause for concern: “As you add more features, things get more complicated.”

WHAT IT COSTS

Alpha Anywhere is available on a per- license basis. A de-veloper license costs $1,999 a year. Appli-cation server licenses range from $1,999 a year for systems running four CPUs to $7,996 for machines

with up to 16 CPUs.

http://searchsoftwarequality.techtarget.com/feature/Alpha-Anywhere-brings-mobile-applications-offline

http://searchsoftwarequality.techtarget.com/feature/Alpha-Anywhere-brings-mobile-applications-offline


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On Becoming a CIOClimbing the IT ladder means taking on high-profile projects and speaking in business terms to the right people.

THE CORNER OFFICE CELSO MELLO

RECENTLY, A YOUNG student who was applying to a lead-ership development program at my company, Reliance Home Comfort, asked me, “How do I become a CIO?”

I wanted to answer this question in practical terms, avoiding leadership jargon. I didn’t answer it fully during the event where it was asked. Here’s an attempt to make it right for that student—and for others with the same aspirations.

1. Take a key role in a high-profile project. A big ini-tiative can transform a business, so it requires executive involvement. Examples include implementing an ERP system or launching a digital business. This type of proj-ect enables you to showcase your technical and leader-ship skills, and it builds credibility and reputation. But

be aware: If a project is unsuccessful, you run the risk of being blamed, even if you were not responsible for the failure. It also requires a great deal of effort, so expect to put in lots of hours, become emotionally committed and stressed beyond reason. It’s the most high-risk, high-re-ward approach.

2. Apply and simplify. Business leaders hire IT profes-sionals to understand technology so they don’t have to. They look for IT leaders who can navigate through tech-nology concepts, filter what’s relevant and turn it into useful business scenarios. Consider that MP3 players used to be advertised in terms like “256 MB, 1.8 TFT display, built-in speaker.” Then the iPod came along with the tagline “One thousand songs in your pocket.” The latter resonates much better with users because it em-phasizes the value of the technology rather than granular features.

Early in their careers, IT professionals are rewarded for their technical prowess. But as they advance into leadership positions, business acumen becomes more critical. It’s never too late to develop the skill. Spend time with front-line employees, learn about their chal-lenges—and not just IT ones. Think about how tech-nology can improve the business and storyboard your vision as though you were presenting it to a fifth-grader.

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THE CORNER OFFICE | CELSO MELLO

Summarize your ideas in a 30-second pitch, and when you encounter the CEO in the elevator, make your mark.

3. Take ownership of your domain. Define your area of expertise and responsibility as broadly as possible. For example, the domain of an IT support analyst might in-clude infrastructure, software code and specific business processes.

Here’s where it gets complicated: Owning a domain means having decision-making authority over anything that affects it. Astute business leaders slowly expand their domains by gradually gaining decision power over portions of others’ domains. The most ethical way to do so would be to manage your domain as though you own the company.

When it comes to making improvements, don’t ask for permission; ask for forgiveness later on—if necessary. Get involved in every discussion that’s connected to your domain, even when it’s not IT-related. Ask thoughtful questions, push back when something doesn’t make sense and express your opinions. Hold peers account-able—don’t avoid conflict. You may be asked to join meetings so you can weigh in on issues. Continue on and you’ll be promoted—that is, get a bigger domain. Repeat from the start.

4. Make promises, meet promises. IT projects are typically associated with being over budget, missing deadlines and failing to deliver on scope. The key to counteract this reputation is making promises and then

making good on them—delivering on time, on budget and to requirements. IT professionals tend to over-promise because they don’t anticipate priority swaps, changes in scope, resource shortages, technical failures. At the other extreme, they might sandbag and build too much slack into the project timeline in their efforts to risk-manage future problems.

Take a page from manufacturing, construction and operations leaders, who have to deliver in spite of con-straints. They do so by learning from past experiences and refining their estimation models. Let’s apply this concept to IT projects. Keep a list of items that are af-fected by deadline-budget-scope problems. When I plan for my next project, I account for these constraints. At the same time, note that planning for everything that can go wrong often chips away at credibility, so keep some of your optimism—and hedge it with a plan B.

5. Build credibility. How you navigate your company and build trust is critical to rising in the ranks—and to having the support you need once you get there. The preceding recommendations all involve demonstrating the ability to execute. Building credibility and trust will put you in good stead no matter your role in your company—it will also come in handy when you’re responsible for projects, budgets and staff resources. n

CELSO MELLO is the CIO of Reliance Home Comfort, a supplier of home heating and cooling systems throughout Canada. Email him at [email protected].



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BUSINESS INTELLIGENCE | ED BURNS

BIG DATA ANALYTICS NOT JUST A GRAB-AND-GO PROCESSTo get business value out of their collections of big data, and avoid overwhelming their systems, organizations need to be smart about what they analyze and what they leave untapped.

RichRelevance Inc. faces a prototyp-ical big data challenge: lots of data and not a lot of time to analyze it.

For example, the marketing analytics services provider runs an online recommendation engine for Target, Sears, Neiman Marcus, Kohl’s and other retailers. Its predictive models, running on a Hadoop cluster, must be able to deliver product recommendations to shoppers in 40 to 60 milliseconds—not a simple task for a company that has two petabytes of customer and product data in its systems, a total that grows as retailers update and expand their online product catalogs.

“We go through a lot of data,” said Marc Hayem, vice president in charge of RichRelevance’s service-oriented architecture platform.

It would be easy to drown in all that data. Hayem said that managing it smartly is critical, both to ensure that the recommendations the San Francisco company gen-erates are relevant to shoppers and to avoid spending too much time—and too many resources—analyzing unim-portant data. The approach it adopted involves whittling down the data being analyzed to the essential elements needed to quickly produce recommendations.



The full breadth of the historical data that RichRele-vance stores on customers of its clients is used to define customer profiles, which helps enable the recommen-dation engine to match up shoppers and products. But when the analytical algorithms in the predictive mod-els are deciding in real time what specific products to recommend, they look at data on just four factors: the recent browsing history of shoppers, their demographic data, the products available on a retailer’s website and special promotions being offered by the retailer.

“With those four elements, we can decide what to do,” Hayem said, adding that data on things such as past pur-

chases, how much customers typically spend and other retailers where they also shop isn’t important at that point in the process.

In the age of big data, knowing what information is needed in analytics applications, and what isn’t, has never been more important—or in many cases, more difficult. The sinking cost of data storage and the rise of the Hadoop data lake concept are making it more feasible

for organizations to stash huge amounts of structured, unstructured and semi-structured data collected from both internal systems and external sources. But miscal-culating what to use, what to hold onto for the future and what to jettison can have both immediate and long-term consequences.

Even though a particular data set may seem unimport-ant now, it could have uses down the line. On the other hand, cluttering up Hadoop systems, data warehouses and other repositories with useless data could cause un-necessary costs and make it hard to find the true gems of information amid all the clutter. And not thinking care-fully about the data that needs to be analyzed for particu-lar applications could make it harder to get real business benefits from big data analytics programs.

Know When to Say ‘When’In a survey conducted by Capgemini Consulting last November, only 35% of the 226 analytics, IT and business professionals who responded described their big data

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IN THE AGE OF BIG DATA, KNOWING WHAT INFORMA-TION IS NEEDED IN ANALYTICS APPLICATIONS, AND WHAT ISN’T, HAS NEVER BEEN MORE IMPORTANT.

MARC HAYEM, of marketing

analytics services provider

RichRelevance, looks to four fac-

tors to decide what products to

recommend to its retail clients’

customers: browsing history,

demographic data, the products

available on a retailer’s website

and promotional offers.



initiatives as successful or very successful. One of the big reasons, according to a report on the survey, is that most organizations “are far from being able to use [big] data ef-fectively.” For example, only 35% of the respondents said their organizations had strong processes for capturing, curating, validating and retaining data, while 79% said they had yet to fully integrate all of their data sources.

In addition, the top big data implementation challenges they cited included data silos, a lack of coordination be-tween different groups and ineffective data governance.

Like RichRelevance, The Lucky Group Inc. tries to get value from its analytics efforts by keeping things in context. The Santa Monica, Calif., company publishes Lucky, a magazine that focuses on shopping, and operates

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THERE ARE TIMES when it makes sense for data analysts

to use an entire set of big data that’s at their disposal,

according to Vince Dell’Anno, an information man-

agement consultant in Accenture’s analytics group.

But he said there’s no magic formula—organizations

should come to their own conclusions about what

data, and how much of it, is needed to get the desired

analytical results.

Dell’Anno recommended that analytics teams

start by assessing the business problems that need

to be solved and then work to identify the data that

can help them address those problems. In some

cases, the answer might involve fully analyzing a

relatively small data set, such as some sales info. In

others, using data sampling techniques to pare down

a larger data set could be the right approach—for

example, in predictive modeling applications where

patterns often become apparent after analyzing a

subset of the available data. Then there are cases

when working with a full data set containing a mas-

sive amount of information is necessary, such as in

fraud detection.

And sometimes, Dell’Anno said, the wisest ap-

proach is to wait until a good business reason pres-

ents itself to even start collecting some types of data.

For example, data analysts at a retailer might think

that social media data is likely to hold valuable in-

sights into customer sentiment and preferences. But,

he said, it could end up being a big waste of time to

begin pulling in the data if there isn’t a clear idea of

how the analytical findings will influence the compa-

ny’s marketing and sales practices. —ED BURNS

Application, Business Needs Should Drive Data Decisions

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several membership-based retail websites tied to the pub-lication. Lucky Group tracks a variety of things. It col-lects internal data on monthly revenue, product sales and what pages visitors are looking at on its sites, which in-clude JewelMint.com and StyleMint.com. The company also gathers customer data, including what products peo-ple buy and how much they spend. It uses Pentaho’s data

integration and analytics tools to pull the information into a MySQL database and then analyze it.

But when analyzing current sales performance or projecting future demand, Lucky Group’s executives and other end users typically don’t need all the data that’s on hand. The mix of products it sells changes constantly, and customer tastes often change as well. As a result, fresh data is the most valuable, said Jay Khavani, the company’s senior manager of business intelligence and data warehousing.

“What was relevant in 2010 is not necessarily relevant right now,” he noted. “We wouldn’t analyze all our data.”

Instead of simply dumping data into a central repos-itory for business users and analysts to explore, Lucky

“ WHAT WAS RELEVANT IN 2010 IS NOT NECESSARILY RELEVANT RIGHT NOW.” —Jay Khavani, The Lucky Group Inc.

Big Data’s Big RoadblocksProblems in managing data and coordinating analytics efforts were among the top challenges to successful big data implementations cited by survey respondents.

46%

Data scattered in silos across various units

39%

Lack of a clear business case for deployments

35%

Ineffective coordination of different big data teams

31%

Dependency on legacy systems for data processing

27%

Ineffective processes for governing big data

27%

Lack of support and sponsorship from top execs

CAPGEMINI CONSULTING’S CRACKING THE DATA CONUNDRUM: HOW SUCCESSFUL COMPANIES MAKE BIG DATA OPERATIONAL; BASED ON RESPONSES FROM 226 ANALYTICS, IT AND BUSINESS PROFESSIONALS

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Group partitions the information, primarily by year. In addition to producing more relevant results, Khavani said that approach saves time and resources by enabling anal-yses to be run more quickly than they otherwise might be. But, he added, users can still get what they need in order to make more-informed business decisions—for example, what products are performing well and how customer preferences have evolved in recent months.

Right People, Meet Right DataEven if you narrow down the types of data you want to look at, predictive analytics and data mining applications might not benefit from using the full amount that’s left. Speaking at software vendor SAS Institute’s 2014 Premier Business Leadership Series conference in Las Vegas last October, Harvard Business School professor Clayton Christensen said he’s skeptical about the value of running predictive models against larger and broader data sets. “Big data for big data’s sake just gives us more data, and that’s not the insight I think we need,” he said.

The key to effective predictive modeling is to find the right data to accurately and quickly answer the questions being asked, Christensen added. To make that feasible, he said, organizations should make sure they have skilled data scientists or other experienced analytics profession-als who can meticulously aggregate the required data and then build well-designed analytical models to pull out the desired findings in an objective way.

But data scientists can’t do it on their own, said Sarah Biller, president of Capital Market Exchange in Boston.


Where It’s AtMost survey respondents said their organiza-tions are still in the early stages of big data deployments or have yet to start them.

5%

No budget allocated for an implementation yet

19%

Budget allocated and focus areas identified, but no implementation yet

29%

Proof-of-concept projects under way on some initial applications

35%

Partial production use, with analytics being done for some business operations

13%

Full-scale production use, with extensive analytics being done for business operations

CAPGEMINI CONSULTING’S CRACKING THE DATA CONUNDRUM: HOW SUCCESSFUL COMPANIES MAKE BIG DATA OPERATIONAL; BASED ON RESPONSES FROM 226 ANALYTICS, IT AND BUSINESS PROFESSIONALS. PERCENTAGES ADD UP TO MORE THAN 100% BECAUSE OF ROUNDING.

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The analytics services company provides investment portfolio managers with projections of how corporate bonds will perform and other information on the bond market, based on analysis of social media posts and news

stories from a list of market watchers. It combines the analytical results with more traditional data, like past performance of particular bonds and the market in general, to produce the projections.

IN A SURVEY conducted by The Data Warehousing In-

stitute last summer, organizations that have adopted

advanced analytics applications, including forms

of big data analytics, were still distinctly a minority

group. For example, just 39% of the 328 respondents

said their organizations were doing predictive analyt-

ics. Location analytics came in at 30%, social media

analytics at 26% and text analytics at 22%.

Advanced analytics can take companies beyond

basic business intelligence processes, helping them

to improve business operations, predict customer

behavior and identify and act on market trends, TDWI

analyst Fern Halper wrote in a report about the survey

that was published in December. But, she said, “most

organizations are somewhere in between [that] vision

and today’s reality of BI and dashboards.”

Many do want to move forward—another 46% of

the survey respondents said they planned to start us-

ing predictive analytics tools in the next three years.

In the report, Halper offered a list of tips on how to

manage the process of building an advanced analyt-

ics program, including the following:

n Start with a proof-of-concept project to demon-

strate the business value of analytics applications.

n Take training seriously. New data management and

analytics skills likely will be needed, especially if big

data platforms and tools are involved.

n Develop processes to ensure that business units are

ready to act on findings so the work of data scien-

tists and other analysts doesn’t go to waste.

n Monitor and assess analytics applications on a reg-

ular basis to make sure the data being analyzed is

still relevant and the analytical models being run

against it are still valid. —CRAIG STEDMAN

Starter Kit for Big Data, Advanced Analytics

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To make sense of such diverse data, Biller said Capital Market Exchange has invested in a team of people with specialized data management and analytics skills. The process starts with a data architect who structures the data for analysis. Then a couple of data scientists develop and run the algorithms that analyze the data, using a homegrown system and the R programming language. Next up is a group of business analysts and data visual-ization specialists who interpret the results and prepare the findings to be presented to the company’s clients in a Web-based dashboard.

Manage Complexity, Not Just VolumeAs Biller’s experience shows, there’s more to big data than just volume. The wide variety of data types that many organizations are trying to incorporate into big data analytics applications also makes the job difficult for program managers. In addition to the technical chal-lenges of bringing all that data together and figuring out what to analyze when, organizational issues can compli-cate the process.

Eugene Kolker, chief data officer at Seattle Children’s Hospital, said during a panel discussion hosted by IBM last October that his principal job duty involves manag-ing the complexity created by the need to analyze many different types of data. Like other healthcare providers, Seattle Children’s relies on a multitude of systems in various departments, including electronic health re-cords, laboratory information systems and scheduling

applications. Kolker said the systems generate data in dif-ferent formats, making it a challenge to combine all the information for analysis.

He added that the technical aspects of reconciling the different data types can be sticky, but they’re the least of his worries. The bigger issue is data owners who are protective of the information in their systems. Kolker said that to make effective analytics possible, he works closely with departmental managers and tries to build a good working relationship with them. “The people angle isn’t just important,” he said. “It’s a major deal.”

It’s that kind of focus on getting at business value that can make big data analytics initiatives manageable—and successful. The bottom line is that collecting data isn’t the important part—it’s what you do with the data that really counts. n

ED BURNS is site editor of SearchBusinessAnalytics. Email him at [email protected] and follow him on Twitter: @EdBurnsTT.

EUGENE KOLKER, chief data

officer at Seattle Children’s

Hospital, works closely with

department managers to build

good relationships with them.

In data analytics, he said, “The

people angle isn’t just import-

ant. It’s a major deal.”


https://twitter.com/EdBurnsTT


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DIP IN DATA LAKE CAN BE BRACING FOR BIG DATA USERSEarly adopters are testing the waters, using Hadoop as a central reservoir for their analytics data. Cost savings and other benefits can be had—if you can stay afloat.

BI ARCHITECTURE | STEPHANIE NEIL

Customer relations is the cornerstoneof service-oriented companies. A slogan like Allstate Insurance Co.’s “You’re in good hands” says it all.But behind that friendly tagline, there’s a business to be run. Creating a great customer experience in every aspect of the insurance process is one of Allstate’s goals—but so is making money.

To help it meet those twin goals, Allstate has deployed a Hadoop-based data lake to support advanced analytics applications aimed at improving its business opera-tions. Data analysts such as Mark Slusar, a quantitative research and analytics fellow at the Northbrook, Ill., insurer, are using the Hadoop system to fish through de-cades’ worth of data that until now was floating around in different databases. The open source framework’s distributed processing capabilities let Slusar and his colleagues explore large sets of data on policies, claims and property losses in an effort to identify patterns, trends and insights that point to new business opportu-nities and beneficial changes in Allstate’s strategies and processes.

“Previously, a lot of the data we looked at was only at

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the state level because data at the country-wide level was so large that we didn’t have an effective way to work with it,” Slusar said. But the data lake, based on Cloudera’s Hadoop distribution, puts nationwide information in the grasp of the analytics team. Now the data “is more orga-nized and centrally located, and the computing power is leaps and bounds faster than before,” he said. “What used to take month s now takes hours.”

Hadoop—once used primarily by large Web compa-nies like Yahoo, Facebook, Twitter and Google—works across clusters of low-cost, commodity servers and stor-age systems. The cost savings it offers is one reason why companies in other data-intensive industries, such as telecommunications, healthcare, manufacturing and fi-nancial services, are jumping on the Hadoop bandwagon.

The data lake concept takes Hadoop deployments to their extreme, creating a potentially limitless reservoir for disparate collections of structured, semi-structured and unstructured data generated by transaction systems, social networks, server logs, sensors and other sources. And in the most extreme cases, Hadoop becomes the centerpiece of analytics architectures.

Once all that info is in the pool together, the theory is companies can apply analytics across the top to help increase operational efficiencies, boost sales and create a more connected and personalized experience for cus-tomers. In addition, filtered subsets of the data can be made available for analysis inside the Hadoop system or sent off to data warehouses and NoSQL databases for end users to access.

A Prize Analytics CatchOne area ripe for improvement that emerged from the depths of Allstate’s databases through its data lake was the process of underwriting homeowner policies, which typically can’t be done until a property inspection takes place. That costs Allstate a few hundred dollars for a home inspector, plus it disrupts the prospective customer’s day. And sometimes, it’s just not necessary, Slusar said.

So Allstate’s analytics team used the Hadoop system to identify when it was OK to skip an inspection.

“We were able to go through historical data for dif-ferent neighborhoods and apply predictive algorithms, which identified areas where we could cut out inspec-tions,” Slusar said, adding that the number of inspections being done was reduced by 20%. That saved the company more than $3 million in 2014.

The data lake blends the historical data with new info—for example, sensor data transmitted via cellular networks from cars, which Allstate uses to monitor mile-age, driving and braking speeds, hours spent on the road

MARK SLUSAR, a quantitative

research and analytics fellow

at Allstate Insurance Co., is

using a Hadoop-based data lake

to support advanced analytics

applications and improve the

company’s business operations.


and other metrics for auto insurance customers who can qualify for premium discounts if they’re deemed to be safe drivers.

Hadoop clusters often start out as less grandiose data stores that function more as feeder systems than alterna-tives to traditional data warehouses.

“Some treat it as an initial landing zone and use it to figure out what data will be processed and sent down-stream,” said Jack Norris, chief marketing officer at Hadoop vendor MapR Technologies. Turning such sys-tems into full-fledged data lakes that support a variety of analytics uses and applications is a big step up. “To


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A DATA LAKE might sound like the perfect getaway from

rigid relational databases. But the dream of lower IT

costs and increased data flexibility gets a dose of

cold-water reality when it comes to achieving the

promises of deeper analytics leading to increased

business and competitive advantages.

A recent Gartner report, The Data Lake Fallacy: All

Water and Little Substance, highlights some inherent

problems in this big data basin, including data gover-

nance challenges and the culture and personnel shifts

required to make it work in many organizations.

“The cost story gets Hadoop in the door, but the

skill it takes to realize value from disparate data

sources is rare,” said Nick Heudecker, a Gartner ana-

lyst and co-author of the report.

Before you jump in, here are a few things to con-

sider, gleaned from the Gartner report and various

interviews:

n Recognize that data lakes won’t deliver increased

business value without an appropriate invest-

ment in skills, tools and training.

n Be aware of the risks of putting a wide variety

of data types in one place. Make sure there is de-

scriptive metadata and mechanisms to maintain

it, or the data lake could become a swamp.

n Build small teams of data scientists and embed

them in business units.

n Focus on ensuring semantic consistency in

upstream applications and data stores.

n Don’t open the floodgates and try to fill a data

lake all at once. Start small and then expand the

deployment once you get your feet wet. n

No Lounging at the Data Lake

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make that leap,” Norris added, “you have to have enter-prise-grade features that include the same SLA and data protection capabilities that are in the data center today.”

That’s where Cloudera, MapR, Hortonworks, Pivotal Software, IBM and other Hadoop providers come in—they say. MapR, for example, takes open source Apache Hadoop and makes the data layer more easily accessible, Norris said. “A lot of what we built in smooths out the rough edges to support broader workloads.”

Broader Horizons for HadoopIn addition, the release of Hadoop 2 in late 2013 broke the technology’s dependence on the MapReduce batch- processing engine and programming framework. Now Hadoop can also run other types of applications—stream processing and interactive querying, for example. And SQL-on-Hadoop tools designed to make using the tech-nology easier for end users are starting to emerge—things like Cloudera’s Impala and Apache Drill, which runs SQL queries natively against multi-structured data sets.

Solutionary Inc., a managed security services and threat intelligence provider in Omaha, Neb., uses MapR’s Enterprise Database Edition distribution (formerly called M7) to speed up analysis processes on its cloud-based ActiveGuard security platform. Solutionary collects mas-sive amounts of structured and unstructured information from its clients’ networks, databases and applications; processes and stores the data in a MapR-based data lake; and then analyzes it in an effort to detect security threats.

In the Hadoop system, Solutionary’s information

security and threat research teams can use tools such as Drill to do what-if analyses with subsecond response times, said Scott Russmann, the company’s director of software development. The data lake setup also enables Solutionary to maintain the information in the varying data structures and formats used by different clients, instead of having to force-fit it all into a single fixed schema.

“It really is an upside-down concept considering where we’ve been historically,” Russmann said. “Tradi-tionally, you have a database administrator who defines the data model—and thou shall not operate outside of the data model. This changes that significantly.”

But Russmann cautioned that data lakes aren’t for ev-eryone. For one thing, too much data flexibility can be a dangerous thing. “It’s a huge culture change,” he said. “A lot of people buy into the hype and don’t think about how to structure the data. They just dump data into this thing and structure it on the fly. That can be a huge cost and burden, and you can dig yourself into a hole.”

JACK NORRIS, chief marketing

officer at Hadoop vendor MapR

Technologies, said data lake

architectures must provide

the same “enterprise-grade

features” mainstream data

center systems do, including

service-level agreements on

processing and data protection

capabilities.

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Swimming With the Fishes?One thing is crystal clear about data lakes: If a project isn’t done right, it could end up dead in the water. Nick Heudecker, an analyst at consulting and market research outfit Gartner, said that not paying proper attention to issues such as security and data governance “could result in piles of information that could be breached, or from which bad decisions could be made.”

In addition, he said, it takes analytics skills that are in short supply to wring tangible business value out of the information in a data lake.

Even Hadoop vendors acknowledge that it’s a compli-cated process. “The challenge is, it’s not that easy,” said Matt Brandwein, director of product marketing at Cloud-era. Sai Devulapalli, head of product marketing and data analytics at Pivotal, noted that data lake technology is still in the nascent stage of development and that the required technologies aren’t simple to use. Nor are there a lot of examples to point to as deployment guides—de-spite all the hoopla about Hadoop, surveys still show its overall adoption rate in the low double digits (see the fig-ure, “Lonesome Lakes,” to the right).

But the potential benefits are as vast as the amount of data that can be supported. At Allstate, the data lake is enabling the company’s data scientists, IT team and busi-ness users to work together to use data proactively, not just reactively, to find ways to reduce costs and improve customer service.

“Efficiency is important here,” Slusar said. “Having the ability to use big data to do something in a matter of

hours frees us up to do so much more. Not just with the data, but with the business.” n

STEPHANIE NEIL is a freelance writer and a correspondent for Business Information. Email her at [email protected].

Lonesome LakesMainstream data management platforms are being used to support advanced analytics much more widely than Hadoop systems are.

86%

Relational database/data mart

78%

Data warehouse

75%

Desktop/server file systems

13%

Cluster based on commercial Hadoop distribution

12%

Cluster based on open source Apache Hadoop

THE DATA WAREHOUSING INSTITUTE’S NEXT-GENERATION ANALYTICS AND PLATFORMS FOR BUSINESS SUCCESS; BASED ON RESPONSES FROM 328 BUSINESS, IT, ANALYTICS AND DATA WAREHOUSING PROFESSIONALS



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CONNECT IT JACK GOLD

ADVANCING MOBILE TECHNOLOGY and implementing a big data strategy will be top priorities for most organizations over the next few years. And while both are important in their own right, can they unite, creating a mobile big data system that delivers real value? It’s not at all guaranteed.

Anyone who doesn’t live in a cave has seen the dramatic impact smartphones and tablets have had on businesses and individuals. Prompted by the bring-your-own-device juggernaut, organizations have discovered how to better give their workers mobile capabilities, but not always effortlessly or securely.

I suspect that in most organizations, smartphone pen-etration exceeds 75%; tablets, while still a much smaller footprint, should reach 35% to 45% in the next two to three years. But on the management and security side,

organizations are struggling to keep up—and often they don’t fully understand the risks of giving workers mobile access or measure them realistically.

In my research firm’s recent survey of about 250 companies, 64% said they’ve never had, or don’t know whether they’ve had, a mobile security breach. In all likelihood, many of them have had one, unknowingly. So while there is great benefit to mobility, the risk is high.

A Big, Big ProblemBig data technology is another must-have that many com-panies are still trying to implement. While the data they generate is growing astronomically, much of it is never used. And the mobile devices that connect to back-office systems are starting to generate almost as much data as desktops do.

Plus, in the next several years, the “Enterprise of Things”—the machine-to-machine network connected to corporate systems, also known as the Internet of Things—will generate massive amounts of data from remote sensors, portable devices such as smartphones and bar-code readers, and autonomous machines. So the need to capture big data and do something meaningful with it will grow dramatically.

How can organizations tailor their big data environ-ments to the needs of mobile workers, particularly as

Better Together?Lots of organizations will try to supercharge their mobile systems with big data—but giving users access to that much information won’t work.

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corporate systems are accessed more and more by mobile devices?

Role-specific analytics is the key to making this work. To analyze big data, organizations need to know what in-formation workers need, when they need it and how the information should be presented. And it all needs to be done securely. Few organizations can do anything close to this today.

Be Specific, PleaseWith any big data project, the goal should be this: create intelligence you can act on and present it in contextually relevant, role-specific ways.

For example, a 5 GB database of prospects won’t do a thing for a salesperson by itself—neither will all the ERP data for California if she covers only Los Angeles. What’s needed is a way to parse the data to make it relevant, giv-ing the user the data needed for a particular time and a particular part of the job. The challenge is compounded by having to package much of the information on mobile

devices, with their smaller screens and unreliable connections.

Most organizations don’t know how to analyze data for their mobile workers. The failure rate over the next few years will probably be in the 55%-to-65% range. Indeed, we most likely won’t see a good coupling of mobile tech-nology and actionable analysis of big data until organiza-tions figure out how best to present and collect data. For most, it will be a trial-and-error process that will last a few years at least.

Over the long term, such a marriage holds the promise of better, more efficient ways of doing business. But until organizations learn how to work better with big data, an-alyze that information for mobile workers and determine ways to tailor it to specific tasks, most will fail.

But don’t let that scare you away from trying. n

JACK GOLD is founder and principal analyst of J. Gold Associates LLC. Email him at [email protected] and follow him on Twitter: @jckgld.

CONNECT IT | JACK GOLD


https://twitter.com/jckgld


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DURING A WEBCAST on IT market predictions for 2015, IDC analysts focused on what they call “the third platform” and others have dubbed the SMAC stack: a modern mix of social, mobile, analytics and cloud technologies. IDC expects third-platform spending to “finally reach the large-scale stage” this year, accounting for 30% of total IT expenditures and a full 100% of the expected market growth, analyst Frank Gens said.

IDC’s focus made perfect sense: Its audience primar-ily consisted of technology vendors. But looking back at TechTarget’s 2014 BI Leadership Summit and other busi-ness intelligence conferences I covered last year, I was struck by the difference between the shiny future of the SMAC environment and the more prosaic present of the BI process in many organizations. For a lot of users, the

primary task at hand is still getting the basics—straight-forward reporting and querying—right in the first place.

Early Days in the BI ProcessThe BI Leadership Summit’s agenda did include sessions on managing and analyzing big data. But others revolved around classic BI fundamentals, such as boost-ing adoption and designing effective data visualizations. And many attendees at the event, held in New York in December, cited those kinds of things as their top BI priorities. (See some of their comments on page 4.)

It was also easy to find BI managers who were knee-deep in such issues at the TDWI Executive Summit in Boston last summer. For example, Patrick Osineye, di-rector of BI and analytics at East Boston Neighborhood Health Center, cited user adoption as his biggest chal-lenge. The healthcare provider has set up a central BI team and started building an enterprise data warehouse. “But having people start to use data to make decisions is difficult,” he said.

Fallon Health in Worcester, Mass., was at a similar point. In June, the medical insurer replaced a less-than-ideal data warehouse with a new one. Then in July, Fallon created its first centralized BI unit. “Now we have to build a BI organization from scratch,” said Irma Murillo, manager of the new group.

For Many, BI Glitter Is Not Yet GoldThe shiny promise of new analytics technologies obscures the truth: A lot of organizations are still in the early stages of putting basic business intelligence capabilities in place.

HINDSIGHT CRAIG STEDMAN

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HINDSIGHT | CRAIG STEDMAN

Hands Down on Advanced Analytics There were similar dashes of reality at the Pacific North- west BI Summit, a gathering of IT consultants and ven-dor executives held in Grants Pass, Ore., in July. Claudia Imhoff, president of consultancy Intelligent Solutions, said that during presentations, she asks how many at-tendees are doing more than basic reporting and analy-sis. Out of 100-plus people, “you might get two or three hands,” she lamented. Shawn Rogers, then a consultant and now a chief research officer at Dell, said he got the same kind of response when he asked attendees at educa- tional sessions on big data how many had active programs.

So how to move forward? Putting a proper data man-agement framework in place is a good start. Building up BI and analytics skills, and putting a capable manager in charge of the program, are essential. So is communicat-ing the potential business value of BI projects.

It’s hard work, and it takes time—a lot of time. But an effective BI process is becoming a fundamental compo-nent of business success, and that’s a basic fact compa-nies can either take to the bank or ignore at their peril. n

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

Read more columns by Business Infor-mation editors.

Business Information is a SearchDataManagement.com e-publication.

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