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NOV. 19, 2012

PLUS

Predictive analysis is getting faster, more accurate and more accessible.

By Doug Henschen

informationweek.com

THE BUSINESS VALUE OF TECHNOLOGY

Combined with big data, its driving a new age of experimentation. >>


2/17

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CONTENTSTHE BUSINESS VALUE OF TECHNOLOGY Nov. 19, 2012 Is sue 1,351

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COVER STORY

7 Advanced AnalyticsPredictive analysis is getting faster, more accurate

and more accessible. Combined with big data,

itsdriving a new age of experimentation.

3 CIO Profiles

Chasing technology doesnt work, says this CIO

4 Global CIOAre project management offices a mistake?

5 CommentaryVMwares doing a tricky tango with open source

CONTACTS

16 Editorial Contacts17 Business Contacts


3/17

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Title: Senior VP and CIO

Degree: Creighton

University, BS in

computer science

Favorite pro sports

team coach: Mike

Tomlin of the Pittsburgh

Steelers, because he

coaches his team like I

feel I coach my team

were both involved and

personal

Best book read

recently: Unbroken,

a well-written and

incredible story about

Louis Zamperini

If I werent CIO, Id be ...

a COO I love

operations

CAREER TRACK

How long at UniGroup: About a year at thislogistics services company.

Career accomplishment Im most proud of:

Making the transition from the business to IT.

Education in IT made me a better business leader;

business experience makes me a better IT leader.

Decision I wish I could do over: In the past, I

made the mistake of chasing technology.

Theres a balance between implementing newtechnologies that offer a competitive advantage

and developing reliable, consistent systems.

ON THE JOB

IT budget: $30 million

Size of IT team: 180

Top initiatives:

>> Building a supply chain management

system.

>> Moving more systems to an open source

stack from a mainframe

>> Engaging a colocation facility.

VISION

One thing Im looking to do better: I want to

improve collaboration. We just have too many

walls today.

How I give my team room to innovate:

I ask for individuals or teams to present ideas

to me that they feel have potential to make a

difference. This demonstrates initiative and

passion for the idea. If an idea has merit,I free up their time to pursue the idea and

ask for regular updates.

The most common cause w

go wrong: A breakdown in

between IT and the busines

support from the business is

projects fail.

What I want from tech ven

truly unique, innovative app

lems from our vendors inste

repackaging of the same old

The most overrated IT mo

computing. Its not that I do

believe that its going to hathere are so many case stud

ply not true. Lets face it, the

work to be done to make it

affordable.

Kids and tech careers: Im

to steer my kids toward tech

Theyre still young, but I bel

ogy will always have a futur

Ranked No. 12 in the 2012

CIO

profilesPrevious Next

Table of Contents

ANTHONY DECANTI UniGroup


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Will most companies that implement a proj-

ect management office take on higher IT costs

without improving performance?

Tha ts the bold con cl us ion of a Hacket t

Group study of more than 200 organizations.

And I agree that the risks of a disastrous PMO

implementation have never been greater.

PMOs can be incredibly valuable when they

manage the right projects through to busi-

ness-focused completion and kill the projects

that dont measure up. Trouble is, creating a

PMO under the wrong circumstances is likely

to produce only more project overhead.The Hackett Group, a research and consulting

firm, found that PMO use for companies of

every stripe grew from 2007 through 2009 then

steadily declined. Its report backs up some find-

ings in InformationWeeks 2012 Enterprise Proj-

ect Management Survey, which traced a reduc-

tion in PMOs and formal PMO skills. Hacketts

bombshell: In some cases, IT performance actu-

ally improved once the PMO was eliminated.

Hackett also found that more PMO oversight

doesnt necessarily improve results. PMOs that

dont spot and manage risks may only increase

pressure to push through badly designed proj-

ects. In a weak PMO, poor management of

time, resources, requirements or customer ex-

pectations encourages shortcuts that increase

design weaknesses that drive higher mainte-

nance and support costs, Hackett said.

Many poor-performing PMOs have staff with

Project Management Institute and other cer-

tifications, Hackett found. The problem is that

those employees often lack a strong k nowl-

edge of the business or its technology infra-

structure, so theyre mainly task-list keepers

and process cops. Ive seen this problem mostoften when management doesnt want to pay

extra for business leadership.

Hackett found four practices as key to PMO

success: centralized IT demand management,

accountability for business benefits, standard-

ization of processes and architecture, and

program and project reviews. Translating con-

sultant-speak into English: PMOs work with

business units to review and set priorities for

the IT services they use. Theyre responsible for

results and cant say, Well, you didnt listen to

me! They revisit projects after theyre com-

pleted to assess lessons an

Yet those key practices st

a PMO. Hackett said that u

ment and collaboration m

as Scrum sometimes can

for heavyweight PMOs.

I dont think the PMO is d

research findings and my

proceed with caution. Wa

builders who prioritize pad

(I built a PMO!) over deliv

Be minimalist: Anything

mented should have a plaiAbove all, ensure that th

committed to the PMO. W

wont get the resources or

it needs to succeed. Tha

linger on, inflicting cost a

both project managers and

til its put out of everyones

Jonathan Feldman is director of

growing North Carolina city. See

informationweek.com/jonathanfeld

[email protected].

Are Project Management Offices A Waste Of Money?

global

CIOPrevious Next

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JONA

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When it comes to the future data center, its

well understood that servers and storage will

function as pools of virtualized resources that

can automatically switch from task to task.

Virtualizing the network also will be essen-

tial, but its a much harder nut to crack than

servers and storage. It remains unvirtualized

and has lagged far behind the other two. But

without it, well never get to the flexible, auto-

mated data center of the future whats en-

visioned as a private cloud.

Thats why I sat down to ta lk to Martin Ca-

sado, the former Stanford grad student whosePh.D. thesis turned into the OpenFlow net-

working protocol. Casado co-founded virtual

networking firm Nicira, which sells software to

program and manage virtual networks using

the principles of OpenFlow. Nicira is the lead

contributor to an open source virtual network-

ing project, Quantum. Quantum is the net-

working piece of OpenStack, the broader

open source platform for managing virtual-

ized data centers.

Heres the rub: VMware bought Nicira for

$1.26 billion in July. (Not bad for a startup with

100 employees.) So I asked Casado: How can

Nicira continue leading development of vir-

tual networking in OpenStack?

Its a key question because OpenStack com-

petes with VMware to manage the virtualized

part of the data center. It seeks to let IT create

systems that allow employee self-provision-

ing, automated expansion to meet demand

and charging business units for use all key

options in a private cloud. VMware aims to

manage private clouds through its vCloud Di-

rector and vCloud Suite.

After listening to Casado, I felt I understoodhow these dissimilar pieces proprietary

product line and open source code contribu-

tions fit together. See what you think.

First, Casado notes that Quantum isnt a par-

ticular set of networking features or a new kind

of hardware that combines switches, routers

and controllers. It is a framework of open in-

terfaces you use to build up virtual networking

for a software-defined data center or a cloud.

Virtualized networks are essential to pr ivate

clouds because, without them, the virtual ma-

chines connection to a network is buried as a

software switch in a hyper

switch functions more effi

ing its processing to a near

as Cisco and Hewlett-Packa

be easier if the server, stora

could all be virtualized up f

sources, with capacity s

when a virtual machine is

provides the virtual netw

OpenStack provides the

three fit in, says Casado.

The Quantum network vir

embeds route-building andcapabilities into a network

ages the switches and route

change. (Nicira offers comm

the name Network Virtual

the size of a virtual server ne

to match its growing traffi

can be increased at the sam

Both VMware-owned N

the open source Quantum

the principles of the Ope

working standard. Open

oped as a cooperative e

VMwares Tricky Tango With Open Source CHAR

Previous Next

Table of Contents Commentary


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informationweek.com

vanced networking groups at Stanford and

Berkeley. (How often does that happen?)

The OpenFlow prot ocol in many sett ings

could gradually displace the widely used span-

ning tree networking protocol, which applies a

hardwired answer to the question of what

route and on what type of network a particular

message should travel. OpenFlow lets a net-

work controller react to demand, then deter-

mine what resources and routes to use.

To make use of that flexibility, the Quantum

part of OpenStack attempts to put a pro-

grammable, vendor-neutral interface be-

tween a human network manager and the

network. The operational interface to the

network has always been a proprietary one

for the last 20 years, says Casado.

The vendor that provides this programmatic

interface will be in a strong position to man-

age the whole private cloud. But if that inter-

face isnt able to handle virtualized gear and

software from many vendors, the private

cloud wont function in the way it was con-

ceived. OpenStack and VMware both say

theyll provide that neutral virtualization man-

agement platform for the private cloud.

The benevolent view is that both have rea-

son to support Quantums development as

the virtual network platform. Without Quan-

tum, OpenStack doesnt offer a complete pri-

vate cloud because it cant handle virtual net-works. VMware needs the Quantum-based

Nicira products to help companies build the

software-defined data center it envisions.

Asked about VMwares commitment to Open-

Stack, Casado says: It is important that we sup-

port our customers deploying on OpenStack

and other open source technologies. VMware

will add that support, keep making code contri-

butions through Nicira and fund OpenStacks

development foundation as a sponsor.

Boris Renski, co-founder of OpenStack con-

sultancy Mirantis, doubts VMware as an Open-

Stack backer: VMware ca

Stack and compete with it

But Frank Rego, business d

ager at Novells SUSE Linux

open source strategy is ch

Microsofts did, when Micro

posing Linux to now supp

virtual machines in the Mic

VMware has had such an a

I agree, except that VMwa

a conversion to make. Whil

etary company, VMware ne

ment opposition to open so

But the main point is thatis essential to the expansio

in general, and VMware

more companies running a

software-defined data cen

as the management softw

much interest as anyone i

cles from the virtual nets p

it will keep backing OpenS

Y ou ca n rea d more stori es fro

informationweek.com/charlesba

[email protected].

The benevolent view is that both

VMware and OpenStack have

reason to support Quantums

continued development as

the virtual network platform.

Previous Next

Table of Contents Commentary


7/17

Five years ago, companies were standardizing

on one or a couple of business intelligence prod-

ucts. Broad interest in advanced analytics, espe-

cially the predictive kind, was just emerging.

Today, companies of all sizes and industries

are experimenting with and using analytics, and

veteran users are going for new levels of sophis-

tication, according to our new InformationWeek

Analytics, Business Intelligence and Information

Management Survey. Companies are embrac-

ing analytics to optimize operations, identify

risks and spot new business opportunities.

Advanced analytics is all about statistical

analysis and predictive modeling being able

to see whats coming and take action before

its too late, rather than just reacting to what

has already happened. That latter practice, de-

risively known as rearview-mirror reporting,

is associated with conventional BI.

The more data companies use, the more accu-

rate their predictions become. But the big data

movement isnt just about using more data. Its

also about taking advantage of new data ty

such as social media conversations, clickstre

and log files, sensor information and other

time feeds. Experienced practitioners are ta

cutting-edge approaches, including in-data

analytics, text mining and sentiment analys

In each of the past six years, responden

our analytics and BI survey have rated the

terest in 10 leading-edge technologies, and

vanced analytics has always been the N

choice. Advanced data visualization is No. 2

Previous Next

Table of Contents

Predictive analysis is getting faster, more accurate

and more accessible. Combined with big data,

its driving a new age of experimentation.

By Doug Henschen

informationweek.com


8/17

informationweek.com

year, up from being ranked third in 2009 (see

chart at right). Last year we added big data

analysis to the list of cutting-edge pursuits,

and this year it ranked No. 4 along with collab-

orative BI.

We also see clear evidence that companies

are investing in software, people and ad-

vanced techniques. For starters, this year we

added in-database analysis for predictive or

statistical modeling to our list of leading-

edge technologies, and respondents rated

their interest higher than for more-estab-

lished categories such as mobile BI and cloud-

based BI.

With in-database analysis, statistical and pre-

dictive algorithms are rewritten to operate in-

side databases that run on massively parallelprocessing (MPP) platforms. In-database analy-

sis is faster than the old approach to data min-

ing, where analysts moved data sets from data

warehouses into specialized analytic servers to

create and test predictive models. Data move-

ment delays plagued the old approach, and

the analytic servers were underpowered. As

data sets have grown, time and power con-

straints limit work to small data samples rather

than all available information, limiting the ac-

curacy of the resulting models.

Businesses that have embraced in-database

approaches say they can develop models in

less time for more precisely targeted seg-

ments, whether theyre trying to predict cus-

tomer behavior, product performance, busi-

ness risks or other variables. Whats more, MPP

power lets them crunch through massive data

sets, so they can use all ava

liver far more accurate mo

In-Database-Enabled Tex

In-database approaches

breaking into new areas. T

ADVANCED ANALYTIPrevious Next

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Table of Contents

Whats Your Companys Level Of Interest In Analytics And BI Techno

Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 417 busine

at companies using or planning to deploy data analytics, BI or statistical analysis software, October 2012

Advanced analytics (predictive and statistical analysis, etc.)

Advanced data visualization capabilities (sparklines, treemaps, heat maps, etc.)

Embedded BI (reports and visualizations deployed within enterprise apps, portals, etc.)

Analysis of big data, particularly unstructured and nonrelational data

Collaborative BI (tools promoting broad sharing and input on analyses)

In-database analysis for predictive or statistical modeling

In-memory BI and analytics (fast analysis and what-if planning on large data sets)

Mobile BI (alerts, reports and visualizations delivered to smartphones and other devices)

Software-as-a-service and cloud computing-based BI and analytics

Social media and social network analysis (sentiment analysis, customer influencer and behavior analysis)

3.6

3.5

3.5

3.4

3.4

3.3

3.2

3.2

2.8

2.7

1 Not interested

(Mean average)
http://prevpage/http://prevpage/http://prevpage/


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vanced analytics technique applied to unstruc-

tured text, has become popular for social media

analysis, according to our survey respondents.

Areas include competitive intelligence gather-

ing, customer behavior analysis, and brand and

product reputation analysis (see chart, below).

Health insurer UnitedHealthcare, for exam-

ple, is testing text mining using SASs in-data-

base High Performance Analytics software,

which runs on Teradata and EMC Greenplum

platforms, as well as commodity grids. Work-

ing in partnership with SAS and EMC, United-

Healthcare is using HPA to develop deeper,

more accurate predictive models to analyze

customers call center communications.

In the past 18 months, UnitedHealthcare has

collected more than 160 million rows of un-

structured information from its call center notes

and call recording transcriptions. It uses SASs

text mining software to look for indications that

customers didnt have their questions or con-

cerns resolved during a call, email or postal

mail interaction. The softwares predictive

models identify suspect customer interac-

tions that require follow-u

The scale of all of this uns

tion, coupled with the com

performance of conventio

tiprocessor servers runnin

ware, previously made th

time consuming and inacc

UnitedHealthcare used a

cal data to train the pre

the data to be analyzed. T

the more accurate the mod

it used about 50,000 rows

set, but with SASs HPA in-d

UnitedHealthcare can use

develop the model in the

time, says Mark Pitts, direc

solutions and strategy for Our data scientists are m

with more training data, ou

discriminating, Pitts says. T

Healthcare is finding more c

low-up and getting fewer fa

line: Customer satisfaction s

Customer Retention 101

The University of Kentu

advanced analytics this ye

dent retention. Since the 2

many colleges and univ


Table of Contents

Previous Next

What factors are driving, or would drive, your companys interest in using social media and social media analysistechnologies?

The Social Factor

Competitive intelligence

Customer behavior analysis (spotting influencers and churn threats)

Brand/product/reputation management (including voice of the customer apps)

Customer service (including product and service quality and warranty apps)

Customer segmentation (for up-sell and cross-sell)

Compliance (financial services, insurance risk management and fraud detection)

Social media and social network analysis technologies arent a priority for my company

Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 417 business technology pros at companies

using or planning to deploy data analytics, BI or statistical analysis software, October 2012

41%

38%

34%

27%

25%

24%

27%


10/17

informationweek.com

country are seeing a higher percentage of

their students drop out before they complete

their degrees. Lower retention rates can lead

to shrinking enrollment and contraction of

courses, degree programs and faculties.

The baseline measures universi ties use to

predict whether a student will fit in and suc-

ceed include high school grade point average

and ACT and SAT test score

also factors in ethnicity, so

ground, alumni status of p

formation in deciding wh


Table of Contents

Previous Next

Taylor University, a small, private Chris-

tian university in Upland, Ind., with

1,900 undergrads, 320 distance learners

and 120 grad students, is proof that analytics

is filtering down to small organizations.

Late last year the university bought Informa-

tion Builders RStat module for its existing Web-

Focus BI deployment. RStat provides predictive

analytics and modeling capabilities based onthe R programming language. We knew it

would be important for us to be able to do data

mining to pull data together from divergent

systems and begin to answer a variety of oper-

ational questions, says Edwin Welch, director

of institutional research and associate registrar.

Taylors student retention rate began to

fall five years ago, and it wanted to turn that

number around. Instead of hiring a statisti-

cian to lead the effort, Welch boned up on

analytics and started experimenting with

the RStat software. With a Ph.D. in comput-

erized instructional systems, he wasnt an

analytics neophyte. He read up on the latest

techniques and turned to Taylors sociology

professors for advice on statistical methods.

Nevertheless, Welch admits he faced a

number of challenges. For starters, he had dif-

ficulty integrating data from disparate sys-

tems using batch extract, transform and loadprocesses and was able to come up with only

6,300 complete student records, 460 of which

pertained to students who had dropped out

after their freshman year. Thats not a large

sample, particularly when you consider that

the Information Builders software splits that

data, randomly choosing 70% of the records

to build models and holding aside 30% for

testing accuracy.

Despite the thin sample, Welch built a variety

of models that varied from 78% to 86% accu-

racy in predicting whether

would leave the university.

best predictors were a stud

GPA, SAT and ACT scores, G

percentage of credit hours co

term, number of midterm gr

the fall term and the number

student registered for in the

Not satisfied with the accmodels, Welch combined th

best models, improving ov

90%, he says. The combin

dicted that 25 students wou

end of the 2010-2011 acad

the 68 students who actua

Welch says. That result was s

than the nine students out o

been identified by faculty

on Taylors academic aler

school year.

Small University Graduates To Advanced Analytics

AT-RISK STUDENTS


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plicants. Once students are enrolled, collegesand universities collect all sorts of new data

that shows how theyre faring.

How does UK predict whos in danger of

dropping out so that it can intervene? It ana-

lyzes student record data going back to 1988

to model the characteristics of students who

did and didnt drop out. In addition to taking

into account preadmission variables, the uni-

versity can tap information on scholarships,

parental participation, courses of study, in-

structors, degree programs, dorms students

lived in, extracurricular activities and, of

course, grades and attendance. Given the factthat UK has about 22,000 undergrads and

6,000 to 7,000 grad students enrolled at any

one time, thats a lot of data.

UK began a proof-of-concept project in

March, using the past six years of student

data. Within two months, it had a baseline pre-

dictive model using high school GPA and col-

lege entrance exam scores, and since May a

statistician hired from another university has

been adding variables to the model from the

universitys Blackboard learning management

system. UK thinks retention is closely related

to the level of student en

adding data on the numbe

log in to their class Web

labuses, download home

collaborate online with c

in homework assignments

The university is using SAAnalysis software in large

ready uses SAPs ERP syste

jects BI software. UK is doin

ses and statistical mod

programming language,

Hana in-memory databas

fast, ad hoc analysis acro

variables. Hana handles qu

out the need to constantly

mensional cubes and dataIf we have people on ca

know what the prediction

certain socioeconomic ba

tain combinations of any

ables, we can quickly ask t

adjust our model, as oppo

nightly [batch] loading job

tenwald, UKs enterprise a

UK built an iPad-based a

the universitys business an

data, selecting the dimensio

in. They can look at data acr


Table of Contents

Previous Next

How does your company deploy, or plan to deploy, analytics and BI technologies?

Where Analytics And BI Fit In

Data:InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 417 business technology pros at companies


20%

30%30%

17%

3%

We deploy them as part of othertechnology initiatives

We deploy them on aproject-by-project basis

We have standardized on oneor a few analytics and BIproducts company-wide

We have many productsscattered throughout

departments, operations andlocations

We dont plan on deploying


12/17

informationweek.com

or graduating class, examining segments and

microsegments based on those 85 variables.

Right now, the retention analytics that most

large schools are doing tend to look at stu-

dents in large swaths, such as high-perform-

ing students and low-performing students,

Recktenwald says. We want to bring thisdown to microsegments, so we can better un-

derstand the needs of individual students.

Once the retention program is proved, UK

plans to develop models that will help it pre-

dict revenue and physical facility needs.

Crowdsourcing Analysis

As corporate data stores grow in volume, va-riety and complexity, they provide advanced

analytics practitioners wit

predictive models. But the

theyre making full use of a

Property and casualty insu

that conclusion when it st

Kaggle, a big data crowdso

With more than 16 millstate has no shortage of d

shortage of analytics expe

predictive modeling team

for the companys produ

helping guide coverage an

Allstates analytics gur

about what they do, so mu

them participate in Kagg

their spare time. Founded

ganizes competitions in government agencies and

data sets and problems. Ka

61,700 participants submit

entries to more than 65 co

sors pay prize money in e

tellectual property behind

In early 2011, Allstates

search, Eric Huls, overhea

ployees talking about a K

and was captivated by the

Allstate hosted a competit

plied three years of aut


Table of Contents

Previous Next

What data sources or challenges are driving, or would drive, your companys interest in using big data analysis?

Whats Spurring Interest In Big Data Analysis?

Finding correlations across multiple, disparate data sources (clickstreams, geospatial, transactions, etc.)

Predicting customer behavior

Predicting product or service sales

Predicting fraud or financial risk

Identifying computer security risks

High-scale machine data from sensors, Web logs, etc.

Social network comments for consumer sentiment

Web clickstreams

My company isnt interested in big data analytics

Data:InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 417 business technology pros at companies


52%

45%

34%

28%

23%

22%

18%

11%

15%


13/17

informationweek.com

asked competitors to beat Allstates baseline

model for predicting which covered autos

would be involved in bodily injury claims and

how much those claims would cost. The mod-

els were created and trained using two years

of data and then tested for accuracy against

the third year of data.All personally identifiable information was

stripped from the data, but still included were

plenty of details about the make, model, horse-

power, weight and length of the vehicles, along

with the cost of any bodily injury claims. In the

vast majority of cases, there were no bodily in-

jury claims, so the cost figure was zero.

Several Kaggle competitors handily beat All-

states baseline, and the winner beat Allstates

model by 270%, meaning it was that muchmore accurate in predicting which vehicles

would be involved in accidents and how

much related bodily injury claims would cost.

It was well worth the $10,000 in prize money

that Allstate put up. The insurer has since in-

corporated some of the techniques used in

that winning model, Huls says.

The appealing thing about Kaggle and crowd-

sourcing, Huls says, is that the competitors ap-

proach problems from many different angles.

Kaggles top competitors include astronomers,

hedge fund quants, statisticians, physicists, econ-

omists and mathematicians, and they dont have

preconceived we do things this way notions

about how to solve a problem.

By putting some of these problems out there

for others to work on, it prevents us from being

more satisfied with the job that were doing

than we should be, Huls says. Were doing a

good job, but this will push us to do better.

Allstate started another Kaggle competition

in September to study cu

this time its using an invi

whereby a select group of

invited to join the commer

ect. To participate, contesta

disclosure agreements so t

have to worry about givin

secrets and it can share mo

no personally identifiable in


Table of Contents

Previous Next

Big data expertise is scarce and expensive

Data warehouse appliance platforms are expensive

We arent sure how big data analytics will create business opportunities

Analytical tools are lacking for big data platforms like Hadoop and NoSQL databases

Our datas not accurate

Hadoop and NoSQL technologies are hard to learn

We dont have enough data

Hadoop and other NoSQL technologies lack management features

I have no concerns about big data analytics

Data:InformationWeek2013 Analytics, Business Intelligence and Information Management Survey of 417 business t


3

31%

22%

21%

17%

13%

12%

16%

What Are Your Primary Concerns About Using Big Data Software?


14/17

informationweek.com

but Allstate has included details on prices paid

for policies, coverage plans and other products

purchased, and whether those customers stuck

with Allstate or switched to another insurer. All-

state also offered contestants more context,

more detail on the methods it uses internally,

and insight into what has and hasnt worked forthe company in the past, Huls says.

From Standardization To Experimentation

Veteran analytics users like Allstate that are

trying out crowdsourcing and other advanced

techniques have come a long way from 2009,

when 47% of respondents to our survey said

they had standardized on one or a few BI

tools deployed throughout the company.

That percentage has since declined to 30% ofthis years 417 survey respondents using or

planning to use data analytics, BI or statistical

analysis software (see chart, p. 11). Whats

more, fewer respondents now report their

companies have a standard BI platform.

Why the reversal? For starters, the standard-

ization movement started around the time of

a significant consolidation of BI vendors that

culminated in 2007 when SAP acquired Busi-

nessObjects, Oracle acquired Hyperion and

IBM acquired Cognos. Microsoft was moving

aggressively into BI at that time, and all four of

those companies were pushing customers to

consolidate BI investments around their big,

broadly capable BI suites. Market-share gains

followed for the four vendors.

Before they were acquired, BusinessObjects,

Hyperion and Cognos had snapped up many

smaller, innovative companies. A new wave ofnimble and innovative BI companies in-

cluding QlikTech, Tableau Software and TibcoSpotfire has since emerged, and they are

now the fastest-growing BI vendors.

IBM, Microsoft, Oracle and SAP hold more

than half (53.2%) of the total BI software tools

market, in terms of 2011 revenue, according

to IDC. Their BI sales are also growing at dou-

ble-digit rates, but theyre not growing as

quickly as smaller, innovative rivals focusing

on advanced data visualization and self-ser-

vice BI (requiring minimal IT support).

SAS and IBMs SPSS unit have long domi-

nated the advanced analytics software cate-

gory, with 35.2% and 16.8

ket, respectively, according

third with just a 2.5% share

competitors each have les

Open source offerings suc

statistical programming ar

gaining commercial adopthasnt escaped the notice

have since introduced R-ba

ules tied to their BI suites. I

was among the first to mak

with the release of its RStat

fire followed, along with Ora

Big Data Ambitions

Where Allstates Kaggle co

volved highly structured dquantities, many Kaggle co

big data tens of hundr

highly variable data with

tures, complex data such a

log files, and minimally stru

text and social network da

growing world of big data

and BI practitioners are eag

What are the top reaso

spondents are interested

correlations across dispa

predict customer behavio


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Our data scientists are more

productive and with more

training data, our models are

more discriminating.

Mark Pitts, UnitedHealthcare


15/17

informationweek.com

uct or service sales (see chart, p. 12). Their biggest con-

cerns? Scarcity of expertise, expense of platforms and

lack of a clear business case (see chart, p. 13).

Hand in hand with the big data trend is a debate

about the future of data warehousing, analytics and BI,

sectors where relational databases and the SQL query

language have held sway for more than 30 years. Com-panies are now experimenting with Hadoop and

NoSQL databases, which let them work with unstruc-

tured, variable and complex data without all of the

data modeling steps that relational databases require.

Many of those adopting these new platforms are try-

ing out new tools as well as new vendors, such as Data-

meer and K armasphere. Theyre also raising concerns

about finding people with the skills to use these tools.

As companies embrace new platforms such as Ha-

doop and NoSQL databases, todays relational plat-forms will likely be focused even more on specialized

analytical tasks and applications. Relational databases

dont adapt quickly or well in places where new data

types, such as complex data and varied data, are con-

stantly showing up. And where data volumes are ex-

treme, Hadoops lower costs will make it a winner. That

means there will be a big opportunity for new analyt-

ics and BI tools built on Hadoop. These changes are

likely to take another five years, but theyre coming.

Read more stories by Doug Henschen at informationweek.com/

doughenschen. Write to him [email protected].

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