bi_final

Business Information INSIGHT ON MANAGING AND USING DATA

The Bonds That Bind the Internet of Things

First the World, Then the Cloud Cognitive Computing for All? Think About It

A Question of Ethics

APRIL 2014, VOLUME 2, NUMBER 2

PLUS:Joshua Greenbaum:

NoSQL Alone Doesn’t Equal Success

NoSQL MAKES

ITS MARK A feisty pack of new

databases is nipping at the heels of mainstream relational software. But is their bite big enough

to bring SQL down?

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MEETING ROOM: HEALTH CARE EXEC’S RX: A BIGGER ROLE FOR ANALYTICS

WHAT’S THE BUZZ? THE BONDS THAT BIND THE INTERNET OF THINGS

ON THE BEAT: FIRST THE WORLD, THEN THE CLOUD

NOSQL MAKES A DENT IN RELATIONAL DATABASE DOMINANCE

COGNITIVE COMPUTING FOR ALL? THINK ABOUT IT

JOSHUA GREENBAUM: LATEST ISN’T ALWAYS GREATEST

A QUESTION OF ETHICS

2 BUSINESS INFORMATION • APRIL 2014

ONE SNOWY MORNING recently a group of chief technology officers from the Boston area debated the merits of Plat-form as a Service over a breakfast of scrambled eggs and waffles.

Someone asked to clarify, “What are we really talking about when we are talking about Platform as a Service?” Before anyone could answer, one CTO interrupted with another question: “What’s the business problem?”

Nods all around. As if this group needed a reminder, technology is never deployed for its own sake.

It’s never been more important to ask that key ques-tion when it comes to spending IT dollars. New technol-ogy abounds: cloud, mobile, social media, analytics. But what is the value to your business?

In this issue of Business Information, we take a look at a new database technology, the realm of NoSQL—a.k.a. “not only SQL”—as well as its younger cousin, NewSQL, a kind of synthesis of the good, old relational database thesis and its NoSQL antithesis.

The good news is, NoSQL databases arose from a spe-cific business problem, writes SearchDataManagement Site Editor Jack Vaughan in this issue’s feature story on the dawn of the age of NoSQL: Google, Yahoo and Face-book ushered in the first of a new kind of database tech-nology because they needed a platform that “eschewed

rigid SQL development principles in favor of more flexi-ble and scalable data designs.”

But not every organization has the data needs of Goo-gle, Yahoo and Facebook. Does that mean that NoSQL is only for the Internet giants? Assuredly not, Vaughan writes. “NoSQL databases have become must-have items for companies with fast-growing vaults of Web, social media, demographic and machine data.”

So far, so good: We have established that there is a business purpose for the dozens of new NoSQL databases popping up. But don’t get too excited, warns IT consul-tant Joshua Greenbaum.

Before going all-in on NoSQL, he implores readers to do some soul searching, pinpointing the business purpose of the project. For example, are you developing new applications to support new business processes? Or bringing in new types of data for analysis? “Only then,” Greenbaum writes, “should you look around to see whether a new database is better for the job than some-thing you already have.”

But SQL or no, the decisions that IT managers and execs need to make are still the old ones, tried and true. n

What do you think the future of relational database management systems holds? Write me at [email protected].

EDITOR’S NOTE | SCOT PETERSEN

NoSQL, No Problem?

mailto:[email protected]

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TREND SPOTTER | EXECUTIVE DASHBOARD

SOURCE: THE 2013 TECHTARGET IT SALARY AND CAREERS SURVEY; BASED ON RESULTS FROM 1,711 IT PROFESSIONALS

SOURCE: A JOINT STUDY BY LINKEDIN AND MARKET RESEARCH OUTFIT TNS; BASED ON RESULTS FROM 998 SMALL AND MIDSIZE BUSINESSES IN THE U.S. AND CANADA

SOURCE: TECHTARGET’S 2013 CONTENT MANAGEMENT AND COLLABORATION SURVEY; BASED ON RESPONSES BY 157 IT PROFESSIONALS. RESPONDENTS WERE ASKED TO CHOOSE ALL ITEMS THAT APPLY

Clouds Roll InNearly one-quarter of organizations are running enterprise content management systems in private clouds and in hybrid ECM setups combining on-premises and cloud deployments. The use of public clouds for ECM is still in the single digits, though.

Money Matters Social Calling

The percentage of IT professionals who received a raise in salary in 2013

The average raise in IT salaries

The percentage of small and midsize businesses on social media

The percentage of those companies that use social networking sites for marketing purposes

Traditional on-premises

Hosted Software as a Service

Private cloud (hosted)

Public cloud Hybrid

62%

14%

55% 81%

94%5.5%

17%23%

6%

22%

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TREND SPOTTER | VERBATIM

“ As much as it’s hyped, big data does open doors to things we couldn’t do five years ago, or even two years ago.”CLAUDIA IMHOFF, president of consultancy Intelligent Solutions, on the business benefits that organizations can get from big data analytics applications

“ Enough. We have got to stop this set of siloed applications.”TODD MORAN, director of social enterprise at Schneider Electric, on the confusion created by the use of multiple platforms for collaboration. It later drove a decision to collaborate on a single software system

“ It’s not just data—there are actually people, patients attached to that data.”JASON GRADY, a nurse and paramedic at Northeast Georgia Medical Center, on the problems with relying on spreadsheets to log patient information

“ It allows you to compete with the big boys if you do it right, and it’s not beyond your reach.”MIKE ROWELL, vice president of business develop-ment at Alfa Insurance, advocating for midsize organizations to implement a data warehouse and analytics architecture

“ Data is a proxy for reality. It has little relevance unless a tool or person does something with it.”PETER MUELLER, head of the global business analytics program at Lonza Pharma & Biotech, discussing the problems that out-of-control analytics applications can cause when viewing data as a corporate asset

“ It can be very productive for you, but it can be equally destructive if it’s not communicated or messaged in the right way.” BRYAN COLANGELO, the former president and general manager of the Toronto Raptors, on the difficulty of getting decision makers to trust the recommendations of data analytics

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THE HEALTH CARE industry is often seen as behind the times when it comes to technology, and nowhere is that more apparent than in analytics. National stakeholders are still having a debate about the best way to digitize pa-tient records, and many organizations have yet to switch from paper to electronic health records. As a result, data scientists and other analysts have a hard time getting the

data they need to run basic business intelligence applica-tions, never mind more in-depth data analytics projects. But some providers are forging ahead with cutting-edge analytics initiatives. For example, Kaiser Permanente, a large health system serving mainly the western U.S., was one of the first medical organizations to implement an EHR system and is now using the data created and stored there to change the way it delivers patient care.

The EHR system has enabled the company’s data an-alysts to focus on deeper questions about clinical care, according to Terhilda Garrido, vice president of health IT transformation and analytics. She and her team have worked on a variety of projects, including one to alert doctors when patients with infections are likely to go into sepsis and develop potentially life-threatening complications.

A Waiting GameBut it took Garrido a while to put her background in engineering and biostatistics to work in a clinical set-ting. When she earned her graduate degree in biostatis-tics in 1983, the only statistical analysis being done in health care was in medical research. “You had to come in through the research arm,” Garrido said. “I think it’s wonderful now that people are much more aware of the

Health Care Exec’s Rx: A Bigger Role for Analytics

TREND SPOTTER | MEETING ROOM

NAME: Terhilda Garrido

TITLE: Vice President of Health IT Transformation and Analytics

ORGANIZATION: Kaiser Permanente

HEADQUARTERS: Oakland, Calif.


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importance of [clinical] data, but I would observe that that hasn’t always been the case.”

She had to look for work elsewhere. Her first job, in 1986, was doing economic modeling for the European Economic Community, a predecessor of the European Union. It wasn’t until 1995 that she found work at Kai-ser in a new division called the Medical Economics and Statistics Department, which analyzed clinical data for meaningful correlations. Garrido said the team’s work was hampered by a lack of useful data, but it did help

get the organization thinking about ways to use data analysis in care delivery.

Kaiser has adopted some technol-ogies more quickly than other health care providers, but it still has room

to grow on analytics, Garrido said. Outside factors will compel it to make analytics projects even more central to its clinical operations. Private insurers and public payers, like Medicare and Medicaid, are pushing providers to adopt accountable care practices, which will result in or-ganizations being paid for the quality of care they deliver rather than the quantity of services provided. Plus, a pro-vision of the federal Affordable Care Act penalizes hospi-tals with high readmission rates. Garrido said those two factors make it imperative for providers such as Kaiser to understand their operations at a deeper level and predict which patients will need extra attention.

“There’s enormous pressure on the industry to do more with less,” she said. “It’s incumbent on those of

us in the industry to really try to identify where we can leverage those resources we have to provide the best care we can for patients.”

Mind the GapGarrido is working on an initiative to minimize readmis-sion through the use of analytics. Called KP Outpatient Safety Net, the program seeks to identify patients who haven’t received appropriate follow-up attention after be-ing discharged from the hospital so that proper care can be arranged. For example, patients who undergo sple-nectomies have an increased risk of contracting the flu. Garrido’s team will analyze the EHR data to make sure that those patients have received vaccinations and alert clinicians if they haven’t.

It remains unclear how quickly the rest of the industry will adopt the same kind of analytical approach to patient care. Garrido said that about 50% of physicians work in one- or two-doctor practices. Their time tends to be con-sumed by day-to-day patient care activities, meaning they don’t have the time—or, often, the expertise—to think about things like database architecture and other techni-cal concerns that come with analytics projects.

Nonetheless, Garrido is hopeful that data analysis technology and know-how can be diffused more broadly throughout the industry so a larger number of organiza-tions can take advantage of the benefits of data analytics. “There are many forces for the good that are improving opportunities for analysts and statisticians [in health care],” she said. —ED BURNS

Read more TechTarget profiles of business and IT professionals.

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THE INTERNET OF THINGS is the term popularly used to de-scribe the ability of machinery and other devices to “talk” to one another and send data to IT systems over the Web. An example of machine-to-machine communication, it’s made possible by inexpensive sensor technology, such as radio frequency identification chips, and widespread Wi-Fi and cellular networks. The connections can be used to gather real-time information on the performance and condition of wind turbines, generators, vehicles, streetlights or even household appliances.

THE BUZZConnecting “things” to the Web can make available data that was hard to get. It can save organizations cash by cutting down on travel to service devices in the field—and even enable preventive maintenance on equipment. Utilities can use the Internet to monitor data from con-nected meters, eliminating the need for in-person check-ups. And manufacturers can remotely track and perform maintenance on machinery in multiple plants.

THE REALITYNetwork disruptions could have devastating results: For example, incomplete data could spoil the stock of trucks carrying refrigerated medicines. Also, collecting and

managing all that data can be a challenge. Then there are security risks. Strong data security and disaster recovery procedures are imperative. Other, more direct human losses are possible as field technicians who service ma-chines find their hours reduced or eliminated. After populating the Internet with things, people might find themselves outnumbered. —BRENDA COLE

TREND SPOTTER | WHAT’S THE BUZZ?

The Bonds That Bind the Internet of Things

BEVERAGES

COMING RIGHT UP!: A vending machine is one “thing” being connected to the Internet. When stock is low, a soda machine, say, can send an alert to the supplier.

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JUST OVER A decade ago, Amazon Web Services wasn’t much more than some ideas scribbled on a whiteboard at Amazon.com. In today’s Infrastructure as a Service uni-verse, AWS is, as Frank Sinatra might have put it, “king of the hill / top of the heap.”

Amazon rolled out its first Web service, a message- queuing product called Simple Queue Service, in No-vember 2004, but the Seattle-based company didn’t launch AWS as a business until 2006. Today, Amazon’s cloud offerings include computing, database, network-ing, payment, storage, application and other services. AWS operates in more than 190 countries, serving hun-dreds of thousands of customers in 10 regions worldwide. In April 2012, Amazon launched the AWS Marketplace, an online store that now offers more than 1,100 AWS- related software products from other vendors.

Customers range from NASA to Nokia. President Barack Obama’s 2012 re-election campaign relied on AWS, and the company recently inked a deal with the CIA. Netflix runs virtually its entire business on AWS—although its streaming-video service competes with Amazon’s own video offerings.

In November 2013, the Amazon re:Invent conference sold out, attracting 9,000 attendees to a cavernous con-vention center in Las Vegas while another 9,000 from 57

countries watched a streaming version of the event. Amazon senior vice president Andy Jassy, who over-

sees AWS, told the crowd that despite its stunning ascent, AWS still sees itself as a young business. “That’s because we have so much more coming for you guys, our custom-ers, in the next few years.” After the conference, The Wall Street Journal ran an interview with Jassy under the head-line “Meet the Man Who Really Runs the Internet.”

The Attraction AWS’s popularity is often credited to a few factors: Am-azon’s brand power, AWS’s size and reach and the flexi-bility and simplicity of its pay-as-you-go business model. But for most customers, the biggest attraction is the highly competitive prices.

They can translate to big savings. In 2012, market research outfit IDC studied 11 AWS customers to deter-mine the long-term financial impact of using Amazon cloud products. On average, the companies reported a five-year return on investment of 626%.

For other customers, AWS’s real value is its scalability. One example: Airbnb Inc., an online property-rental marketplace that launched on AWS in 2009 and has since grown to accommodate 11 million users and an av-erage of 150,000 bookings a day.

TREND SPOTTER | ON THE BEAT

First the World, Then the Cloud

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Behind the CurtainJust how much money does AWS make? Amazon isn’t saying; the company doesn’t specifically break out AWS revenues in its financial reporting. That could change if, as some analysts have suggested, Amazon spins off AWS into a separate company. Meanwhile, Morgan Stanley an-alyst Scott Devitt recently estimated AWS’s current value at $25 billion—that’s $2 billion higher than his estimate for Amazon’s Kindle e-reader business. Other analysts project AWS’s value to reach anywhere from $50 billion to $100 billion over the next few years.

That’s not to say that AWS has had an easy ride to the top. The company has suffered several major crashes, in-cluding the Christmas Eve outage that knocked out Net-flix’s streaming-video service in 2012.

Then there are AWS’s price reductions—at least 40 since 2006. While they sound like great news, some analysts have suggested that prices are already so low that constant reductions don’t make a real difference anymore.

And there’s no question that AWS must keep looking over its shoulder. When VMware launched the U.K. ver-sion of its vCloud Hybrid Service in February, a company official told reporters that the EMC subsidiary hoped to become a major cloud-services provider—but didn’t ex-pect to challenge AWS. Less than a week later, VMware announced the hiring of AWS senior technology evange-list Simone Brunozzi as its new vice president and chief technology for hybrid cloud.

In a recent report, analysts at market research

company Gartner included several cautionary notes about relying on AWS. For instance, it charges separately for some optional items that are included in its compet-itors’ packages. “This increases the complexity of under-standing and auditing bills,” the report said. Researchers also took issue with AWS’s tiered support system, noting “the quality of support differs materially between tiers.”

Still, AWS remains the market leader. Gartner analyst Lydia Leong wrote in a blog post, “While it’s still far from everything it could be, and it has some specific and sig-nificant weaknesses, that steady improvement over the last couple of years has brought it to the ‘good-enough’ point.” —ANNE STUART

New Site: SearchAWS THIS MONTH WE are launching a website focused on

Amazon Web Services. Go to SearchAWS.com to

find out how your organization can get the most

out of its cloud strategy. Whether you’re strug-

gling to expand computing power in the cloud;

want to develop, deploy and even sell applica-

tions on the AWS platform; or are making AWS

your primary IT infrastructure partner, you’ll find

insight in news, features and tips compiled by my

team of award-winning writers and editors.

—SCOT PETERSEN

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ARCHITECTURE | JACK VAUGHAN

NoSQL MAKES A DENT IN RELATIONAL DATABASE DOMINANCESQL used to have a lock on how data processing was done. NoSQL databases have opened things up to alternative approaches—but they aren’t likely to completely replace the old way.

In 1922, automaker Henry Fordfamously wrote that his customers could have a car painted any color they wanted—as long as it was black.

Until recently, IT managers, application developers and business executives faced similarly limited choices in selecting database technologies. Relational databases built on top of the SQL programming language were the dominant engines powering corporate IT and business systems, with no real challengers in sight.

But things have changed. Starting in the mid-2000s, SQL’s absolute supremacy was undone by the likes of Yahoo, Google, Facebook, Amazon.com and eBay. Their need to run colossally scalable Web applications with varied and fast-changing data requirements prompted efforts to find alternatives to mainstream relational da-tabases. That ushered in first a stream, and over the past few years a torrent, of new technologies that eschewed rigid SQL development principles. Those databases are spread across several distinct product categories based on different data models. But they share a pithy name with a stake-in-the-ground sound: NoSQL.

The truth is, though, that the NoSQL movement



isn’t really an up-against-the-wall revolution seeking to eradicate relational databases. Yes, some NoSQL ven-dors do talk like that’s their ultimate goal. But the term NoSQL has been softened to also mean “not only SQL,”

in recognition of the fact that many of the databases do incorporate some elements of SQL. More substantively, NoSQL technologies aren’t positioned as wholesale re-placements for relational software—they tend to be built

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All in the NoSQL FamilyNoSQL databases are geared toward managing large sets of varied and frequently updated data, often in distrib-

uted systems or the cloud. They avoid the rigid schemas associated with relational databases. But the architec-

tures themselves vary and are separated into four primary classifications.

Document databases

Store data elements in document-like structures that encode information in formats such as JSON.

Common uses include content management and monitoring Web and mobile applications.

EXAMPLES: Couchbase Server, CouchDB, MarkLogic, MongoDB

Graph databases

Emphasize connections between data elements, storing related “nodes” in graphs to accelerate

querying. Common uses include recommendation engines and geospatial applications.

EXAMPLES: InfiniteGraph, Neo4j

Key-value databases

Use a simple data model that pairs a unique key and its associated value in storing data elements.

Common uses include storing clickstream data and application logs.

EXAMPLES: Aerospike, DynamoDB, Redis, Riak

Wide column stores

Also called table-style databases—store data across tables that can have very large numbers

of columns. Common uses include Internet search and other large-scale Web applications.

EXAMPLES: Accumulo, Cassandra, HBase, Hypertable, SimpleDB

http://searchdatamanagement.techtarget.com/opinion/Slew-of-disparate-NoSQL-databases-vie-to-displace-RDBMSs-fit-by-fit



for specific uses, usually involving large data sets that need to be accessed and updated frequently. And that’s how things are playing out on the ground: NoSQL data-bases have become must-have items for companies with fast-growing vaults of Web, social media, demographic and machine data, but often they’re sharing data process-ing and analysis workloads with SQL-based software.

For example, Crittercism Inc. is a startup that helps organizations monitor the performance of their mobile applications, based on real-time data collected from more than 800 million mobile devices. In application perfor-mance management parlance, a user interaction with an app is called a request; Crittercism pulls in information about more than 30,000 requests a second, a rate that adds up to nearly 3 billion a day. That has created a pool of more than 20 terabytes of data—and the total only keeps growing, said Lars Kamp, vice president of business development at the San Francisco company.

Included in the mix is data on application errors, crash diagnostics and what Crittercism calls “network breadcrumbs” documenting the trail of processing events leading up to app problems. That data “is very unstruc-tured and non-uniform and varies widely from customer to customer and application to application,” said Mike Chesnut, director of operations engineering.

Meeting the Old Way Halfway The sheer amount of information involved, and its vari-able nature, mandated a fresh approach to formatting the data. Using relational software would have required

substantial processing overhead to maintain a database schema that could accommodate all of the information, plus frequent downtime for making changes to the schema, Chesnut said; he added that the company had to be able to modify how it collects and stores data “on the fly, often several times a day.” Kamp was even blunter: “Crittercism as a company would not have been possible 10 years ago,” when SQL was the only choice, he said.

Enter MongoDB, a NoSQL database running on the Amazon Web Services cloud. Like other NoSQL technol-ogies, it offered schema design flexibility. That made it possible for Crittercism to store the error and crash data in a single “collection”—the MongoDB equivalent of a relational table—without imposing a strict schema on the information. In turn, the lack of a fixed data struc-ture with uniform fields has enabled the company’s per-formance management service to “evolve organically” to meet the needs of different customers, Chesnut said.

Crittercism also uses Amazon.com’s DynamoDB

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LARS KAMP, vice president

of business development at

Crittercism Inc., needed a tech-

nology that could capture a

fast-flowing stream of mobile

application performance data.

He and his colleagues found

one in MongoDB, a NoSQL da-

tabase running on the Amazon

Web Services cloud.

(Continued on page 14)

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NOSQL DATABASE IMPLEMENTATIONS have become increas-

ingly plentiful among fast-moving Web and cloud

companies looking to buck the confines of relational

software. But NoSQL vendors are facing heightened

competition of their own from another group of up-

starts: Developers of NewSQL databases that com-

bine SQL and NoSQL traits.

DropShip Commerce, a two-year-old company that

operates a logistics and business-to-business trading

platform for retailers, suppliers and distributors, took

a stab at using NoSQL software to help run its opera-

tions. The American Fork, Utah, company initially mi-

grated from relational MySQL to the MongoDB NoSQL

database before moving again last year to NuoDB’s

namesake NewSQL technology to power virtual prod-

uct catalog delivery, inventory management and or-

der tracking for its clients.

The MongoDB system ran into limitations in scaling

and its ability to handle the reporting requirements of

customers, said Scott Lemon, DropShip’s chief tech-

nology officer. The company’s processing platform

has to be flexible because it’s used by a variety of

suppliers and retailers with data requirements that

change frequently. But Lemon said the MongoDB de-

ployment consisted of separate implementations built

around individual customer partnerships—creating, in

effect, islands of NoSQL data.

Managing all of the individual data models was

a major source of pain for Lemon’s team. “The data

models were crippling,” he said. “We wanted to put

this all into a single large database that we were able

to query.”

NuoDB is based on a three-tier, distributed da-

tabase architecture and built for use in the cloud,

like most NoSQL technologies; its features include

multi-tenancy support that allows users to run multi-

ple databases off of a single installation. But it sports

a transaction engine that executes SQL code, said

NuoDB CEO Barry Morris.

Lemon gave good marks to NuoDB for its distrib-

uted computing chops. He said elastic scalability

is vital to his applications because e-commerce in-

terchanges are subject to massive seasonal—and

sometimes daily—swings in traffic. Blaine Nielsen,

DropShip’s president, added that the company can

now scale its databases “in lockstep as the business

scales.”

A MongoDB representative said that there are

“plenty of people running highly scalable MongoDB

systems,” while also acknowledging that there’s still

“room for improvement in the experience of develop-

ing” such systems. n

From NoSQL to NewSQL

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NoSQL database to store data on a specific request path that requires particularly fast performance. But there’s SQL in its database architecture, too. A PostgreSQL open source database holds highly relational operations data, and all of the information is summarized in a SQL-based Amazon Redshift data warehouse for analysis and report-ing. Chesnut and his colleagues aren’t NoSQL purists: “We’re very engaged with exploring any and all technol-ogy offerings that can help us solve our problems and better serve our customers,” he said.

Recent surveys show that NoSQL databases are

making inroads with big data users—but overall, adop-tion is still relatively low. For example, TechTarget’s 2013 Analytics & Data Warehousing Reader Survey found that 21% of 222 respondents with active or in-the-works big data programs were using or planning to deploy NoSQL systems as part of the efforts. Another survey conducted last year by Enterprise Management Associates and 9sight Consulting produced an almost identical result: In that case, 22% of the 259 respondents said they had NoSQL platforms in place. In a third survey, done by The Data Warehousing Institute, 32% of 189 respondents said their organizations were using NoSQL software. Even

Relational databases on massively parallel processing systems

Relational databases on symmetric multiprocessing systems

Data appliances

Hadoop Distributed File System

Columnar databases

NoSQL databases

What’s in Your Big Data Environment? Percentages of organizations using various data platforms for big data management, in order of prevalence:

SOURCE: THE DATA WAREHOUSING INSTITUTE’S MANAGING BIG DATA; BASED ON RESPONSES FROM 189 IT PROFESSIONALS, CONSULTANTS AND BUSINESS USERS WITH BIG DATA MANAGEMENT EXPERIENCE, FROM AN ONLINE SURVEY CONDUCTED IN 2013

63%

60%

51%

48%

48%

32%

(Continued from page 12)

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there, though, NoSQL technology was last on the adop-tion list, trailing behind relational databases, data appli-ances, columnar software and big data fellow traveler Hadoop.

Greater penetration of data centers is expected: Ana-lyst group Wikibon forecast last year that worldwide rev-enue for NoSQL software and services would grow from $286 million in 2012 to $1.825 billion in 2017. Venture capitalists are betting on such growth. MongoDB Inc., which leads the development of its namesake database, raised $150 million in new funding last fall. That came shortly after $45 million and $25 million funding rounds by DataStax and Couchbase, two other NoSQL vendors.

Hitting From Both SidesEven the big relational database vendors have gotten into the NoSQL game. Oracle introduced a NoSQL database in late 2011 and was one of the lead sponsors of last year’s NoSQL Now! conference. Last June, IBM added support for MongoDB’s application programming interface to its DB2 relational database, enabling users to store data

there in the JavaScript Object Notation (JSON) format. DB2 can also handle graph and XML data, and IBM in March acquired Cloudant, a NoSQL vendor that runs a hosted version of the JSON-based CouchDB database. Microsoft offers a NoSQL data store as part of its Win-dows Azure cloud platform.

Application-driven data needs and the growing move toward cloud computing are creating a wider opening for NoSQL methods, said Carl Olofson, a database analyst at market research company IDC. For IT managers and business executives, though, he compared buying into

NoSQL with investing in a new stock that doesn’t have a lot of market history.

“Most of the NoSQL databases are new. They still need to be bat-tle tested,” Olofson said. “If you’re constantly changing data defini-tions and you can’t change your re-lational database fast enough, you

CARL OLOFSON, an IDC analyst,

sees the surge in NoSQL tech-

nologies pointing to a greater

issue: the growing move toward

cloud computing—and the re-

sulting need to quickly mine

application-driven data.

Percentage of developers who cited 1 petabyte or more as the point at which to switch from traditional database technologiesSOURCE: EVANS DATA CORP.’S DATA AND ADVANCED ANALYTICS SURVEY 2013; BASED ON RESPONSES FROM MORE THAN 440 DEVELOPERS67

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might look at NoSQL. But there is risk.”For one thing, NoSQL technologies typically don’t

provide full ACID capabilities—atomicity, consistency, isolation and durability—for guaranteeing transaction

integrity, as relational databases do. In addition, they of-ten lack enterprise-class services in areas such as disaster recovery, security and data quality, according to Olofson. He also expects a whittling of the well-populated ranks of

WHEN THE PROCESSING job at hand is updating gener-

al-ledger data or running queries for analytics and

reporting, SQL relational databases are still the likely

technology choice for most organizations. But it’s a

different story with Web applications, particularly

ones running in NoSQL-friendly cloud computing

environments.

“NoSQL arose from a desire to quickly spread

cloud-based applications and Web applications,” said

IDC analyst Carl Olofson. Relational software became

harder to maintain as databases increasingly ran

across farms of servers scattered in multiple loca-

tions or deployed in the cloud, he added. That created

an opportunity for NoSQL vendors—and they’re cash-

ing in on it.

Guy Harrison, executive director of research and

development for Dell’s database management tools,

said NoSQL databases aren’t dependent on cloud

environments. But, he added, they “are built with the

cloud in mind. They all easily scale elastically, for

instance.” Harrison, who also has written four books

on managing databases, said he expects NoSQL cloud

deployments to increase naturally as more organiza-

tions move applications to public clouds.

The strictures of SQL also make it difficult with

relational software to modify the data architecture

of Web applications and set up different fields and

structures for individual data sets—both common re-

quirements for Web developers. The schema-less or

schema-after-the-fact NoSQL database is a means to

those ends, and that flexibility is often the chief driver

when organizations decide to dip their toes in the

NoSQL waters.

“Doing the entire schema in advance is incon-

venient for Web applications,” said Curt Monash,

president of analyst company Monash Research. In

addition to complicating modifications down the

road, it adds time to the development process—and

on the Web, Monash pointed out, deployment speed is

often king. n

NoSQL Tools Enjoy Rarefied Cloud Air

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NoSQL vendors as the market matures.“NoSQL databases are really good for handling XML

and JSON data, which includes a lot of things Java devel-opers are working on these days,” said Wayne Eckerson, a TechTarget industry analyst and president of consultancy Eckerson Group. In particular, they’re well suited to high-performance Web applications “with a high volume of reads and writes,” Eckerson said. But, he added, they aren’t such a good fit for complex analytics jobs.

NoSQL Speed BoosterThat maps to the database architecture at Exelate, a mar-keting data services and technology provider that uses a range of tools to supply information on household demo-graphics and purchases to online advertisers and publish-ers. “Data is what we do,” said Elad Efraim, co-founder and chief technology officer at the New York company. While Exelate didn’t start out with NoSQL technology when it was founded seven years ago, the need for speed eventually led Efraim and his team to deploy Aerospike, an in-memory NoSQL database that has helped scale the company’s infrastructure to handle as many as one tril-lion real-time data transactions a month.

Aerospike provides a high-performance repository for data on the user session activity of website visitors that is constantly being updated, Efraim said. “We’re talking about a large-scale system with a very high capacity of reads and writes that have to complete in some millisec-onds. It’s very important for us to make sure we can ac-cess the data in a way so that it can be made available

[to our customers] for decision making.”The database runs on servers at four fully replicated

data centers worldwide, indexing everything to memory and holding it in the server cluster for further process-ing. From there, the data can be mined and correlated to other information in analytics and back-office systems. To make that happen, though, Exelate’s applications don’t solely use NoSQL software. One layer above the Aero-spike repository is a “pretty standard” MySQL relational database that lets customers aggregate data, Efraim said. The company also uses an IBM Netezza appliance and relational database as a data warehouse for analytics uses.

To put things in Henry Ford’s terms, users like Ex-elate and Crittercism no longer have to limit themselves to basic-black relational databases—and they’re taking advantage of NoSQL’s new color choices to drive appli-cations that mainstream relational software isn’t suited for. But SQL black isn’t going completely out of style with IT shoppers. For now, the two technologies are likely to share space in database garages. n

ELAD EFRAIM, co-founder and

chief technology officer at

marketing data services and

technology provider Exelate,

deployed Aerospike, an in-mem-

ory NoSQL database that has

helped the company handle as

many as one trillion data trans-

actions a month.

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COGNITIVE COMPUTING FOR ALL? THINK ABOUT ITThanks to advances in artificial intelligence and natural language processing, machines like IBM’s Watson are becoming as smart as we are. And soon the technology may become elementary to many more than just Big Blue.

TRENDS | STEPHANIE NEIL

The health care industry suffers from an ailment known as information overload. It’s a condition that makes it difficult for doctors and insurance providers to quickly determine the procedures that will be required and covered for each patient.

The individual’s history, years of case files and clinical evidence must be considered—no small feat for a hu-man. But it’s as easy as saying “aah” for Watson—IBM’s artificial intelligence computer system that processes natural language questions against a deep well of data to compute evidence-based answers in a matter of seconds.

Watson can sift through the data equivalent of about 1 million books, analyze the information and provide precise responses to complicated questions in less than three seconds. Insurance provider WellPoint Inc. doesn’t have to imagine what that could mean to the health care industry. Through a partnership with IBM that be-gan two years ago, the Indianapolis-based company is working with more than 3,000 physician offices in its

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network to provide patient coverage and treatment op-tions almost instantaneously.

“We trained Watson to think like a nurse or physician on staff,” said Elizabeth Bigham, WellPoint’s vice pres-ident of health IT strategy. “It receives requests from providers, finds medical policies, compares what the pro-vider said in the request, determines who the patient is, what they want to do and why, and renders a recommen-dation to our staff.”

Watson, Bigham said, is a game changer in the med-ical field. Other companies are also developing Wat-son-powered applications for health care providers and consumers—for example, software startup Welltok Inc.’s CafeWell Concierge, which will offer personalized, lo-cation-based guidance on diet, exercise and preventive services.

And it’s not just health care. In January, IBM launched a Watson Group business unit to ignite new commer-cialization efforts in a range of industries, including financial services, travel, telecom and retail. To help fuel its efforts, the new unit was given $100 million to invest in third-party software developers; it made an initial investment in Welltok in February. IBM also is making Watson services available in the cloud and providing software developers with access to its Watson application programming interface to build new kinds of cognitive apps. Welltok’s project is one of those efforts; another is a Watson-powered “smart adviser” self-service application, which can understand natural language, read and inter-pret text and learn from other types of smart technology,

such as virtual personal assistants. Also, Fluid Inc., which makes software designed to improve online shopping, is developing an app that makes product recommendations based on information provided through natural dialogue. So talking to a smart device, like an iPad, could deliver the same kind of experience a shopper would have with an in-store sales associate.

A Bountiful MindIt is this cognitive capacity—the ability to mimic the human brain, to learn and to understand in context and to be more assistant than tool—that could revolutionize computing as we know it.

That’s according to IBM, of course. But Dan Miller, founder and senior analyst at Opus Research, doesn’t dis-agree, calling the technology “transformative.”

“IBM was out to demonstrate deep computing’s ability to do things like understand a topic quickly, discern irony and satire,” he said. “It also helped uncover some of the

ELIZABETH BIGHAM, vice pres-

ident of health IT strategy

at health insurance provider

WellPoint Inc., oversees a pro-

gram that uses the cognitive

power of IBM’s Watson to work

with thousands of physician

offices in its network, offer-

ing nearly instantaneous pa-

tient coverage and treatment

recommendations.

(Continued on page 21)

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IT Brain EvolutionThe history of cognitive technologies is studded with starts and stops, and new developments indicate the trend is again picking up steam.

MIT receives a $2.2 million grant from the U.S. government for research in AI.

Scottish robot Freddy is built by The Assembly Robotics group at Edinburgh University with the

ability to use vision in problem solving. Later, such open-ended research in AI would no longer

receive government-allocated funds.The American Association of Artificial Intelligence holds its first national conference, at Stanford University.

Thinking Machines Corp. is founded in Waltham, Mass.; it later launches a commercial product called Connection Machine.

Sales of AI-related software reach $425 million in the U.S., fueled by vendors of Lisp-based “expert systems” such as Symbolics and Lisp Machines.

Dean Pomerleau creates ALVINN (Autonomous Land Vehicle in a Neural Network),

the groundwork for a system that drove a car coast to coast under computer control. Thinking Machines files for bankruptcy.

Chess-playing system Deep Blue, developed by IBM, beats then world champion Garry Kasparov.

Jointly created by IBM and the Los Alamos National Lab, the Roadrunner supercomputer is the first of its kind to achieve one quadrillion calculations a second.

IBM’s Watson competes on the game show Jeopardy! against former champions Brad Rutter and

Ken Jennings and beats them both. IBM Watson Group is formed as part of an effort to foster increasing demand for cognitive technologies.

John McCarthy coins the term artificial intelligence at a Dartmouth conference.

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constraints of the brute force approach. Scientists have long known that the answers get better and quicker if the systems address a specific topic or domain. And that’s where IBM Watson is taking it.”

In health care, for example, where the volume of data is doubling every five years, it could take a nurse 20 minutes to collect the data needed to make a treatment assessment. Cognitive technology, coupled with big data, is delivering that same evidence-based information in a matter of seconds.

“We know Watson makes us more efficient and is help-ing us turn around requests faster,” Bigham said. “It also ensures we are consistent in our application of medical policies and guidelines.”

IBM is also working with pharmaceutical companies to understand drug interactions. Using IBM’s new Wat-son Discovery Advisor service, which makes connections across millions of articles, journals and studies, drug re-searchers can formulate conclusions that previously took months in just hours.

Of course, cognitive technology is not a new con-cept. Artificial intelligence emerged as a hot topic in the 1960s, when computer scientists set out to build systems that were as intelligent as humans. The 1980s saw a flowering of “expert systems” from companies like

Symbolics and Lisp Machines as well as the highly pub-licized development of a massively parallel processing supercomputer aimed at AI applications by Thinking Machines—but those efforts quickly ran out of steam. Other supercomputer makers—even IBM—also dangled the idea of intelligent machines in front of government agencies and research laboratories, but their focus was on solving grand scientific problems, such as modeling

the global climate and mapping the hu-man genome. Those things didn’t require cognitive capabilities, just colossal com-puting power.

Today, processing power and storage are not the big issues they once were. When Watson was introduced in 2011, it ran on 90 servers and 20 terabytes of disk. The current system is 90% smaller


IBM’s investment in a new business group dedicated to commercializing its Watson supercomputerSOURCE: IBM CHAIRMAN, PRESIDENT AND CEO VIRGINIA ROMETTY $1b

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(Continued from page 19)

COMPUTER SCIENTISTS SET OUT TO BUILD SYSTEMS THAT WERE AS INTELLIGENT AS HUMANS IN THE 1960s.

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and 20 times faster, said Steve Gold, IBM’s vice president of marketing and sales operations for Watson systems.

And the cognitive technology has evolved as well. Watson is an amalgamation of artificial intelligence,

machine learning and natural language technologies. But it does not follow a logic-based set of rules as the supercomputers of the past did. Instead, it decomposes

questions from natural language to understand the con-text of what is being asked. Then it analyzes the corpus of available information in research and articles and comes up with candidate answers. It is not deterministic; it is probabilistic—producing a set of best answers with ranking and supporting evidence. For example, a sim-ple question about the color of the sky depends on the

IBM’S MEGACOMPUTER WATSON is not the only game in

town when it comes to complex, deep learning tech-

nology for the enterprise.

“There are more cost-effective ways to enter the

market and start applying those same disciplines

to provide a better customer experience,” said Dan

Miller, founder and senior analyst at Opus Research.

Specifically, the use of virtual agents that harness

artificial intelligence, machine learning and natural

language to ascertain the purpose of a question and

get the answers quickly. The difference is, virtual

agents tap into a finite data set rather than the un-

limited sea of information available to Watson. While

IBM’s technology has its place in sophisticated ap-

plications in the health care and pharmaceutical in-

dustries, it can be overkill when it comes to customer

self-service uses.

“Watson is great in a highly complex environment,

but it was never designed to be low-labor-intensive,”

said David Lloyd, CEO of virtual agent provider Intel-

liResponse. “If all you are trying to do is help a cus-

tomer get to a finite set of the right answers, it is a

much different process.” The trick is figuring out the

many ways a customer might ask the question in or-

der to serve up the right information, he said.

Right now, more than 50 vendors offer virtual as-

sistant technology, including Anboto, Expertmaker,

Next IT and Nuance. And the market could be about

to explode. According to Opus, in 2013, enterprise vir-

tual assistant spending lingered around $100 million.

But as these virtual agents get better at learning new

tasks and responding to customers in personalized

ways, Opus predicts spending will approach $700 mil-

lion by 2016. n

Cost-Effective Cognitive Tools Not an Idle Thought

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circumstances: It could be blue, gray or white. Watson tries to comprehend the question and then uses thou-sands of algorithms to score the answer.

Business users and consumers, too, are more ready for intelligent systems than in the past. Conditioned by the prevalent use of intelligent personal assistant applica-tion, like Siri on Apple’s iPhone, many people now expect computers to recognize natural language and be able to respond to complex questions.

“What IBM is doing at the high end, as well as com-panies like Google and Apple that are working natural language understanding into machine learning, we, as humans, are being conditioned to feel more comfortable talking to some form of artificial intelligence,” Miller said.

Miller expects there will be a ripple effect that will eventually bring high-end Watson-powered apps to the masses. “But it doesn’t take a Watson-like investment to get an interface that is conversational and human-like in nature,” he said.

The Road to ReasonWatson isn’t simple or inexpensive. While Bigham wouldn’t disclose WellPoint’s financial arrangement with IBM, the process of training Watson for use by the in-surer includes reviewing the wording on every medical policy with IBM engineers, who define keywords to help Watson draw relationships between data. The nursing staff together with IBM engineers must keep feeding cases to Watson until it gets it. Teaching Watson about

nasal surgery, for example, means going through policies and inputting definitions specific to the nose and con-ditions that affect it. Test cases then need to be created with all of the variations of what could happen and fed to Watson.

And things change, so it is an ongoing process. Bigham said the company can now teach Watson new things over a period of several weeks. It is a significant time and money investment, but WellPoint is bearing the brunt of the work to develop an affordable commercial app that it can license to other health insurance companies.

This painstaking process of training Watson is most likely the reason cognitive technology is not catching on like wildfire, Bigham said. According to Miller, there are subtler things at work.

“Generally, solutions like Watson are put in the ‘emerging technologies’ category, and the processes involved with building a business plan to make an in-vestment in Watson are just now being defined,” Miller said. So though cutting-edge technologies like cognitive

DR. S.S. IYENGAR, a professor at

Florida International University

and chief scientist at NuLogix

Labs, helped invent a complex

event processing technology

called the Cognitive Information

Management Shell, which is be-

ing used to develop an agro-in-

telligence platform designed to

help increase food supplies.

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computing aren’t typically associated with objectives like return on investment, that is starting to become an issue in this case.

Similarly, other companies are working on their own strategies with different forms of cognitive technology. Enterra Solutions, based in Newtown, Pa., has devel-oped a cognitive reasoning platform that combines big data and artificial intelligence to find insights that can improve performance in areas like the supply chain and consumer marketing. Google, Facebook and Yahoo have all recently hired AI researchers or acquired startup ven-dors to lead machine learning development efforts. And NuLogix Labs, recently relocated from Princeton, N.J., to San Jose, Calif., is using a complex event processing tech-nology, called the Cognitive Information Management Shell, to develop an agro-intelligence platform designed to help increase food supplies—starting in India.

Total RecognitionThe CIM Shell, developed at Louisiana State Univer-sity, can drill down into complex events and activities to adapt rapidly to evolving situations. The work being done with a university in India is focused on increasing specific crop productivity by using sensors to collect data on ground activity and a synthesis program to dynami-cally reconfigure in real time to adjust to environmental changes. The agriculture app provides actionable intelli-gence to scientists who use it to direct experiments and inform decision making.

The technology can be put to use in other industries as

well. Oil and gas company BP gave LSU a $250,000 grant to develop a prototype of the technology that could be used to help prevent future oil spills.

The CIM Shell’s distributed intelligent agents fuse disparate streaming data, like text and video, to create an interactive sensing, inspection and visualization system that provides real-time monitoring and analysis. If there are any changes in data patterns—temperature or pres-sure of equipment, for example—it sends an alert, noted Dr. S.S. Iyengar, a computer science professor at Florida International University and chief scientist at NuLogix, and one of the technology’s inventors. It’s not the first technology that detects changes in conditions—abnor-mal situation management applications can, but they only flag things out of the ordinary. The CIM Shell not only sends an alert but reconfigures on the fly in order to isolate a critical event and fix the failure.

“The goal of CIM is that nobody should have to write the program,” said co-inventor Supratik Mukhopadhyay, an assistant professor in the department of computer sci-ence at LSU and NuLogix’s chief technology officer. “You tell the computer what it needs to do and it writes a pro-gram itself that will solve the problem in real time.”

The Human FactorThe CIM Shell and Watson take different approaches to understanding complex events, but they both are built to respond, learn and continue processing, just like the human brain.

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What these cognitive systems can’t do is analyze the risk that might not be represented anywhere in the un-structured data. That includes factoring in cultures, envi-ronments, people and accountability.

“You have to be able to analyze risk,” said Jose Bravo, a chief scientist at oil company Shell Global. Shell is looking at big data systems and is considering a variety of artificial intelligence products, but, according to Bravo, there are still limitations to what a deep learning ma-chine can do. For example, if a predictive model says buy oil in the Middle East, but a leader in the region is at risk of being deposed by a revolution, it must be factored into

the decision. “If you could predict how the future will develop, that would be great, but you can’t,” Bravo said. “And you can’t hold a machine accountable if it makes a disastrous decision.”

But that’s why there will always be a human element in the cognitive machine mix. At WellPoint, the staff ultimately chooses whether to accept Watson’s recom-mendations. The value of Watson is the speed, efficiency and consistency for responding to a doctor’s request and for complying with medical policies and guidelines, Bigham said.

For the doctor’s office, users are typing a natural lan-guage question into a browser on demand. There is no calling and waiting to submit a request. And every day Watson gets smarter, drawing connections between con-cepts based on things it’s already learned.

As time goes on, Opus Research’s Miller predicted, cognitive technology will evolve for the masses, getting less expensive and easier to use. Watson and other artifi-cial intelligence systems won’t fade away like the cogni-tive fads of the past, Bigham said. “In my opinion, this is one of the next big things.” n

WHAT THESE COGNITIVE SYSTEMS CAN’T DO IS ANALYZE THE RISK THAT MIGHT NOT BE REPRESENTED ANYWHERE IN THE UN STRUCTURED DATA.

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CONNECT IT JOSHUA GREENBAUM

Latest Isn’t Always GreatestToday’s post-relational database models have sleek designs and promise fast performance—but if they don’t suit your business needs, you’ll be off on a road to ruin.

NEW DATABASE TECHNOLOGIES are coming to market with increasing regularity, and if these products live up to the hype—superfast and crazy cheap—hundreds of thou-sands of workhorse relational databases in use today will be put out to pasture. Who needs a 20th-century relational database when you can have a decidedly more modern NoSQL, columnar or in-memory database—or even the Hadoop Distributed File System?

Most organizations, it turns out. At least for now. While the seductive powers of the new technologies

are not to be denied, you should be careful about listen-ing to the siren song of the new, post-relational database vendors. Not because the new database options lack merit, but because making your company’s next database move a technology decision is the wrong way to go about

it. The choice of database should be secondary. Your business goal—that comes first.

A Very Good Place to StartConsider a battery of practical questions about your proj-ect: Are you creating net new applications in support of net new business processes or merely upgrading the ones you already have? Engaging new types of users, data or analysis? Supporting a new line of business or reinvigo-rating an existing one? Answers to these questions will provide essential criteria for understanding which new database technology, if any, to deploy.

Only then should you look around to see whether a new database is better for the job than something you already have.

Implementing a database of any kind isn’t cheap. While many of the new varieties are open source, they aren’t free—and even more costs enter the equation when a project involves migrating an existing relational database to one of the newbies. Myriad complexity issues also stand in the way.

New database technologies, particularly in-memory ones, often need new hardware. Many of the available options promise to lower total cost of ownership over time—but new hardware will have to be obtained, and that up-front cost must be taken into consideration.

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The Fine PrintFinding people with the right skills is an even bigger issue. The new models may require fewer administra-tors—most proponents insist that their databases are less expensive to implement and manage than old-school relational databases are. And in many cases that’s an easy argument to make: Top-tier database administrators are some of the highest-paid people in the IT department, and their numbers—most relational databases are noto-rious for the number of admins required to keep them finely tuned—clearly add significant costs.

But the likelihood of finding a Hadoop or columnar database expert in a traditional relational database shop is slim, which means you’ll have to go out and hire these in-demand people or get the required skills from a con-sulting company.

And as anyone who has worked to bring a major application project to fruition can attest, the bulk of the complexity is centered on everything but the cost of the software license. Creating new algorithms, ana-lytical models, transactional components and business

processes that need to be engineered and implemented is where the real expense is. Until they’re well understood and the necessary stakeholder input and approvals have been obtained, the choice of database technology is at best a distraction. At worst, it’s a great way to knock a project off its axis and send it spinning out of control.

Think BigThis is particularly true in the era of big data, which is driving a considerable percentage of the new application projects in organizations. For many, big data projects involve data types that are new, unfamiliar and often unstructured—time-series data, Web server logs, text. While some new database technology might eventually need to be deployed, figuring out what the new data sources are and what the new algorithms should look like must be the first order of business, right after you’ve reached agreement on what the new business processes are all about. To do otherwise is to march your company down the path of cost overruns, scope creep and even-tual—if not inevitable—failure. n

CONNECT IT | JOSHUA GREENBAUM

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BIG DATA BECAME something of a household term last year, but it did so in a swirl of controversy.

Snooping by the National Security Agency and data breaches at Target stores were just two of the prominent news events that took some of the wind out of big data’s burgeoning sails. That wasn’t necessarily bad, though, be-cause the storm of hype around big data technologies was threatening to burst those sails at the seams.

Data processing has a long history, but the recent con-troversies suggest that it may be entering a new era—one in which information ethics will become top of mind. While most of the coverage in Business Information and on SearchDataManagement is about the nuts and bolts of data management, we’re also hearing from people on the IT and application front lines who are thinking about

the ethics of data, perhaps more deeply than in years gone by.

Big data applications have something to do with that; so do the open data initiatives being launched by govern-ment entities. But like almost any political, philosophical or ethical issue, the challenge doesn’t present itself in stark black and white. IT, analytics and business man-agers have to find a balance between making data open, protecting individuals’ privacy and—in businesses, at least—using data to make money.

Longhorn Data Gets Freer RangePrivacy and access to data are dual concerns for Steph-anie Bond Huie, vice chancellor of strategic initiatives at the University of Texas. She helped lead an effort to open up data for measuring the UT system’s performance across its various academic and health services institu-tions, but at the same time she had to be mindful of pro-tecting sensitive data.

Huie and her colleagues have transformed their ap-proach to delivering data analytics. Where once-a-year “report books” once held sway, highly interactive and up-to-date data analysis dashboards became the rule. To meet the challenge, her group implemented a data ware-house fronted by SAS Visual Analytics software, which enables citizens to see info such as out-of-state versus

A Question of EthicsNot long ago, the challenges presented by big data were capturing, storing and harnessing all that information. But there are other, knottier issues, and they go much, much deeper.

HINDSIGHT JACK VAUGHAN

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HINDSIGHT | JACK VAUGHAN

in-state enrollment for different academic departments.In January, the university also announced the launch

of a website that provides salary and student-loan debt statistics on students after graduation.

“It was important that we had secure procedures in place, to make sure people can’t hack into the site,” Huie said. For example, the production servers that push data to the website don’t hold any student data.

The drive to open up the data on the university’s per-formance wasn’t without controversy. The effort came in part at the behest of political forces backed by Texas Gov. Rick Perry. The proponents pushed hard to bring the data to light, including politically sensitive metrics like the workloads of professors. In response, UT’s chan-cellor, Francisco Cigarroa, embraced measured openness initiatives.

Ethics on the Back BurnerHuie’s efforts are clearly conscious of the real people be-hind the data points. Will that type of thinking become pervasive?

That’s what I was wondering when I met up with Da-vid Wells, an independent consultant at Seattle-based Infocentric and an instructor for The Data Warehousing Institute, at a TDWI conference late last year.

The NSA and its digital “hoovering” was all over the news. Funny—the surveillance agency is being scruti-nized for activities that sometimes aren’t very different than the practices of top Web companies that collect massive amounts of information as part of their big data

business strategies. That was the backdrop as I asked Wells if there really is such a thing as a data profession, with accepted norms of data ethics.

“It’s not a profession yet,” he said, adding that he doesn’t think there’s enough interest in ethical consider-ations to qualify it as one. “There are a handful of articles about the ethics of data, but there should be many. There should be books, and classes. What we have to do is to get people to understand that it matters.”

Unfortunately, as Wells points out, crises most often drive people’s attention. The “NSA fiasco,” he remarked, may be what’s needed to bring the issue of balancing data access and privacy to a head.

Coming months will tell how much attention stays focused on information ethics issues. Data and analytics are playing an increasingly pivotal role in the rush to in-novation, as well as in the drive to succeed in commerce.

But sometimes it’s necessary to pause and consider the social implications of data collection, access and usage—if the term “data professional” is to ring true. n

STEPHANIE BOND HUIE, vice

chancellor of strategic ini-

tiatives at the University of

Texas, had to deal with a host

of issues—among them data

security and student identity

protection—while leading the

effort to open up data on the UT

system’s performance.

Read more columns by TechTarget editors.

http://searchdatamanagement.techtarget.com/ezine/Business-Information/Big-data-technology-Beyond-the-trendy-tools

http://searchdatamanagement.techtarget.com/ezine/Business-Information/Big-data-technology-Beyond-the-trendy-tools

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