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Understanding in-databaseanalytics technology:Benefits, use cases and ROILearn all about in-database technology in this E-book, designed to give you acomplete overview of the technology and how it can be leveraged successful-ly. First, get the basics. Learn how in-database analytics differs from pastapproaches and find out why it’s getting so much attention these days as agame-changing trend. Next, find out more about the trend toward in data-base analytics. Read why more companies are choosing to leverage this newtechnology – and learn more about their ROI and business results.

In this E-book, readers will:

• Get the basics about in-database analytics, including how it differsfrom past approaches and why companies are using this technology

• Find out why in-database analytics is an important trend in businessintelligence – and learn more about how organizations are deployingthe technology, including the benefits and challenges to expect

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Understanding in-database analytics technology: Benefits, use cases and ROI

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Understanding in-databaseanalytics technology:Benefits, use cases and ROI

E-Book

Table of Contents:

Understanding in-database analytics technology: Benefits, uses and ROI

Is in-database analytics an emerging business intelligence (BI) trend?

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Understanding in-database analytics technology: Benefits, usesand ROI

By Elisabeth Horwitt, SearchDataManagement.com Contributor

In-database analytics is an emerging practice that experts say can significantly cut the cost and time it takes to do

complex and data-intensive analytic processes.

The first chapter in this E-book on in-database analytics will cover how the practice works and how it differs from

more traditional analytical methodologies. We will also look at recent and developing market trends, such as vendor

support and the emergence of open standards, as well as in-database analytics’ potential for revolutionizing

advanced analytics and business intelligence. The second chapter will discuss the paybacks of in-database analytics

and how to realize them, as well as potential deployment challenges.

Breaking down in-database analytics

According to a September 2009 Forrest Research Inc. report, “In-Database

Analytics: Heart of the Predictive Enterprise,” the practice is far from bleeding-

edge. In fact, in-database analytics is the latest instance of a longstanding

approach in which developers embed application logic into data warehouse

and database systems.

In a traditional set up, predictive analysis, data mining and other compute-

intensive analytic functions are part of separate applications or data marts, each with its own system, set of data,

analytic tools and programmers. As a result, “a lot of people spend a lot of time shepherding data out of a data-

base, profiling it, transforming into a format a particular analytic tool can digest, and moving it to where analysts

can use it,” says Neil Raden, president of the Santa Barbara, Calif.-based consultancy Hired Brains Inc.

In contrast, with in-database analytics, predictive analysis, data mining and other analytic functions reside on the

same centralized enterprise data warehouse. This eliminates I/O-intensive extract, transform and load (ETL) opera-

tions that can consume as much as 75% of cycle time in predictive analysis. It also enables developers to exploit

powerful data warehouse platform technologies like parallel processing.

Empowering the enterprise data warehouse

In-database analytics is one of several recent developments that have made advanced analytics an increasingly

important, and affordable, element of corporate business intelligence (BI) initiatives.

First, a precipitous drop in storage, computing and memory prices has helped fuel the emergence of scaled down,

low-cost database engines, data warehousing platforms and appliances.

Second, many of these platforms support leading-edge computing technologies that enable computing-intensive

applications like advanced analytics to run more efficiently. For example, 64-bit memory enables large volumes of


Understanding in-database analytics technology: Benefits, uses, and ROI

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In-databaseanalytics enables

developers to exploitpowerful data

warehouse platformtechnologies like

parallel processing.

data needed for predictive analytics to reside in main memory instead of on disk, which eliminates time-consuming

I/O transfers. Parallel processing enables multiple analytic processes to run in tandem. Virtualization enables

companies to allocate computing resources to analytic and database querying functions on a prioritized and as-

needed basis.

Another key factor is the recent emergence of two industry standards for advanced analytics. MapReduce, a

vendor-neutral programmability framework for complex information types, is gaining traction among data ware-

house and advanced analytics solution vendors, according to Forrester.

The second standard, Hadoop, defines an open analytic processing pushdown workflow model and distributed

analytic object-file store. It has growing support from database, data warehouse and cloud computing platform

vendors, according to Forrester.

Once these standards gain broad support from leading players, businesses will have far more flexibility in choosing

(and migrating between) data warehousing platforms.

At least as important, MapReduce and Hadoop can work with unstructured as well as structured data residing in a

database. This will be critical for the next generation of analytic applications, which will be mining the complex

patterns in diverse and distributed information generated by Web 2.0 applications, social networking, clickstream

analysis, and the like, says Forrester Research Director James Kobielus, lead author of the report.

Who can benefit from in-database analytics?

Advanced analytics is potentially useful to many types of organizations pursuing advanced analytics. It’s well-suited

for activities such as targeted response marketing, dynamic pricing analysis and fraud detection and prevention. It

can also help executives who need to know how best to allocate R&D money or security upgrades; or create a

business plan that reacts to projected market changes over the next five years.

Still, it isn’t for everybody, Raden warns. He recommends that companies considering whether to deploy in-data-

base analytics, either as an in-house development platform or to support commercial applications, should ask

themselves the following questions:

• Do you have any problems that lend themselves to predictive modeling?

• Is the necessary data readily available, consistent, accurate and supported?

• Above all, do you have the corporate culture and the will to make use of the results you obtain?

In other words: “Are you willing to let math algorithms in a computer lead you to do things you didn’t conceive of

yourself, and go against what you’re already doing?” Raden asks. “A lot of companies aren’t there yet.”

Elisabeth Horwitt is a freelance writer.


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Is in-database analytics an emerging business intelligence (BI)trend?

By Elisabeth Horwitt, SearchDataManagement.com Contributor

Gartner included advanced analytics as one of the Top Ten Strategic Technologies for 2010 at its recent

Symposium/IT Expo 2009. It’s no wonder, then, in-database analytics has become an important business intelli-

gence (BI) trend , according to many experts.

Once limited primarily to government organizations and large financial services and insurance companies, advanced

analytics is taking off in applications ranging from fraud detection and prevention to targeted marketing to financial

strategy and risk management. In-database analytics has significantly bolstered this BI trend by making such

applications affordable to organizations that can’t afford either supercomputers or on-staff quantitative analysts,

according to Merv Adrian, president of IT Market Strategy.

By integrating analytic processes with business applications on a common

enterprise data warehouse (EDW) platform, in-database analytics enables

companies to leverage increasingly affordable and powerful platforms in a

far more cost-effective way. Furthermore, development time for complex

analytic applications can go from weeks or days to hours -- or even min-

utes.

Neil Raden, president of Santa Barbara, Calif.-based Hired Brains, cites

one real-life client example. The advertising arm of a major media

company typically gathers tens of terabytes of market data over a

two-week period. Under the old ETL regimen, the firm’s research analytics

team spent three or four days exporting, transforming, moving and

distributing chunks of market data in order to gain business insights.

Once it had deployed an enterprise data warehouse platform with in-data-

base analytic capabilities, however, the team could run programs directly in the database, eliminating system

bottlenecks. Setting up two weeks’ worth of data takes about 20 minutes, the director of research analytics reports.

What makes in-database analytics a unique BI trend?

By making database functions transparent, in-database analytics enables BI application developers “who don’t know

how to do SQL in a complex way” to create new applications involving complex manipulation of databases, without

having to depend on the expertise of SQL programmers, Adrian noted.



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In-database analyticshas the potential tolevel the playing fieldin BI, enabling agile,smaller companies tocompete with the bigguys in terms of pre-dicting, identifying andresponding to markettrends in a timely and

effective manner.

Indeed, in-database analytics has the potential to level the playing field in BI, enabling agile, smaller companies to

compete with the big guys in terms of predicting, identifying and responding to market trends in a timely and

effective manner. Models based on in-database analytics are also more flexible than traditional predictive models,

whose parameters -- once set -- are difficult to change, Raden said. This is critical in today’s business world, where

executives and knowledge workers have to make rapid decisions based on a flood of information from disparate

sources.

In one case, modeling software based on in-database analytics has enabled a telephony service provider to do

online pricing and margin analyses and usage-based micro-segmentation of the subscriber base. Models are

regularly updated on the basis of rated call data records, which are continually collected from the carrier’s global

network.

Furthermore, experts report that consolidating on a single enterprise data warehouse infrastructure can pave the

way to top-down governance of all BI and analytic initiatives. Companies can enforce enterprise guidelines for

development templates, data cleansing and transformation rules, and they can leverage resources across analytic

initiatives enterprise-wide, according to James Kobielus, a research director at Cambridge, Mass.-based Forrester

Research.

However, getting to a place where all analytic initiatives use the same, fresh version of data and consistent models

may require some hefty housecleaning and consolidating of data, not to mention a major reorientation of end-user

and developer mindsets, industry experts cautioned.

In-database analytics a BI trend to approach thoughtfully

Today’s predictive analytic initiatives tend to consist of disparate, project- and application-driven data marts, each

with its own cadre of analysts and specialists and a different set of often-inconsistent data, warned Shaku Atre,

president of Atre Group, Inc. She recommends that companies take a prioritized approach, focusing on key

applications and data, rather than trying to do it all at once.

Be aware, too, that the in-database analytics market is young and volatile, with much of its potential still to be

realized. Leading database and data warehouse vendors are still in the process of incorporating it into their

platforms. Independent software vendors are just beginning to take advantage of the efficiencies of in-database

analytics to develop new BI applications, often in partnership with data warehouse platform vendors.

“This should mean that more packaged analytic applications will become available, which is good news for the many

companies that can’t afford their own in-house quantitative analysts,” Hired Brain’s Raden said. “For example, a

trucking company can buy an application that uses very ‘hairy’ analytics, and trust that it works, because other

people have bought and benefited from it.”

Furthermore, broad industry support of standards such as MapReduce and Hadoop holds the promise of bringing

more flexible deployment of advanced analytic applications, as well as the ability for models to work with

unstructured as well as structured data.



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All that said, companies need not wait months or years to start deploying, according to industry experts.

“Look around and see if you have a problem you want to solve and what kind of information you need to solve it,”

Raden said. If it’s an analytical problem involving large quantities of data, developing a model to solve it could be a

lot cheaper and quicker with in-database analytics.

Elisabeth Horwitt is a freelance writer.



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