Big Data

1 Copyright ©2014 Dell Inc. All rights reserved. Copyright ©2014 Dell Inc. All rights reserved.

Advanced analytics

techniques uncover

business insights from

all types of data, big and

small — on open platforms

that adapt easily to

different use cases.

By David Sweenor and Uday Tekumalla

5 ways to boost your business IQ

A few short years ago, business analytics were primarily designed to

answer questions like “Why did this happen?” to explain the past. These

analytics required making sense of structured data that often resided in

databases from a single vendor.

Today, the picture is more complicated. To compete effectively, line-

of-business (LOB) managers must answer complex questions that often

involve predictions: “What’s going to happen next?” “What if these trends

continue?” “How do we combine the different data sets to gain better

insights?” The answers can be extracted by analyzing the massive influx of

internal and external data sources available to many organizations today.

Organizations have an opportunity to move beyond classic business

intelligence (BI) systems to adopt advanced analytics techniques that

address diverse business use cases. They can extract new insights from all

data types — structured data from applications and traditional databases

as well as semi-structured or unstructured data from email, websites,

files, documents and video. But first, decision makers need to assess their

analytical maturity level to deliver on specific business goals.

Dell

Inc.

2Copyright ©2014 Dell Inc. All rights reserved. Copyright ©2014 Dell Inc. All rights reserved.

Laying the groundwork

Fundamental capabilities must

be in place before moving

on to advanced analytics. An

organization’s next step depends

on its current level in the analytical

maturity model (see figure).

To understand how this model

applies to a real-world example,

consider a big data initiative to

bolster financial services sales.

This initiative must be designed

to intelligently integrate data

across varied sources to measure

customer satisfaction, identify

trends in retention and loss, and

predict behavior. Data analysis

may reveal certain trends,

provide insights into why these

trends are happening and spark

ideas for ways to reverse or

reinforce them.

At the outset, some

organizations might use a

simple spreadsheet tool such as

Microsoft® Excel® software to

perform ad hoc data collection,

to keep a record of daily

transactions and to carry out basic

data analysis. This fundamental

level of analytical maturity helps

capture the progress of an

organization’s events and answer

straightforward questions: “What

transactions did my customers

perform today?”

At the next level of analytical

maturity, integrating and consolidating

data across silos lets organizations

derive relationships by storing

data logically in a relational, or

structured, database. This process

enables organizations to determine

qualitative measurements: “How

many accounts does each

customer have?” “What are their

account balances?”

Proactive reporting and

analysis help establish standard

measurements of organizational

Data analysis may reveal certain trends,

provide insights into why these trends are

happening and spark ideas for ways to

reverse or reinforce them.

Cognitiveanalytics

Predictiveanalytics

Proactivereporting

Informationconsolidation

Casual data accessand analysis

An

alyt

ical

mat

uri

ty

What do you want to achieve?

Analyze root cause of behaviors

Make reasonable predictions based on historical trends

Establish KPIs to measure and track organizational goals

Define logical relationships across the data

Capture progress through simple recording and tracking of data

From data to decisions: Levels of analytical maturity

Big Data

3 Copyright ©2014 Dell Inc. All rights reserved. Copyright ©2014 Dell Inc. All rights reserved.

success. These capabilities are

typical of the analytical maturity

level at which organizations

use a data warehouse and a

standardized BI platform to

measure and track organizational

goals and key performance

indicators (KPIs). With these

capabilities, an organization can

answer questions such as “What

is the portfolio balance for a

family across different divisions or

regions?” and “How are we serving

their various needs?”

The topmost analytical maturity

levels, predictive analytics and

cognitive analytics, fall under the

realm of advanced analytics.

Predictive analytics enable

organizations to anticipate future

behavior and make reasonable

forecasts based on historical

trends: “What upsell should I offer

a 25-year-old male with checking

and IRA accounts?”

Cognitive analytics are designed

to model human behavior and

generate advanced behavioral

insights: “Will the customer prefer

to receive a loan offer or a premier

savings account?” “Will they

recommend us to a friend?”

Determining the best fit

To meet specific business needs,

organizations can evaluate five

major categories of advanced

analytics techniques depending on

the nature of the questions they

need to answer.

Segmentation. Organizations

can design and implement highly

targeted business strategies

by segmenting data into

subsets based on geographic,

demographic, behavioral and other

factors. In marketing, organizations

use segmentation to understand

who is buying their products and

the best way to reach different

groups. Segmentation is also

used in the insurance industry for

fraud detection and in finance

for detecting anomalies or

identifying transactions that are

similar and dissimilar.

Decision trees. The decision

tree category of analysis is widely

used in finance and banking

to help with credit risk decisions —

determining whether credit

should be extended to a person

or organization. In healthcare,

decision tree techniques enable

providers to decide which

medications are most beneficial

in particular cases. Businesses

also use this method in customer

service, automated marketing

and call centers to help ensure

customers receive the right

information at the right time.

Statistica: Smart and flexibleDell has built a data-agnostic, platform-agnostic portfolio spanning

the analytical maturity model — from basic data collection to

data integration, business reporting and advanced analytics. With

the acquisition of StatSoft by Dell in March 2014, midmarket

organizations now have access to Dell Statistica software, a

predictive analytics solution that enables fast decision making.

Dell Statistica provides a comprehensive platform that supports

the analytics lifecycle, including model management, real-time

scoring and the ability to combine business rules and predictive

models to make decisions. As an open, standards-based platform,

it easily integrates with existing systems and adapts well to specific

requirements and a variety of business use cases.

The software is designed to make it easy to prepare, report on

and analyze data, empowering both line-of-business users and

sophisticated analysts with an intuitive solution that complements

existing IT investments. It also helps simplify data mining;

predictive analytics; machine learning; and analysis of structured,

semi-structured or unstructured data.

Dell Statistica is consistently ranked as one of the most

comprehensive and easiest-to-use advanced analytics platforms

on the market.*

* “Magic Quadrant for Advanced Analytics Platforms,” by Gareth Herschel, Alexander Linden and Lisa Kart, Gartner, Inc., February 19, 2014, gartner.com/doc/2667527; “Advanced Analytics: The Hurwitz Victory Index Report,” by Marcia Kaufman and Daniel Kirsch, Hurwize & Associates, 2014, software.dell.com/whitepaper/advanced-analytics-the-hurwitz-victory-index-report830046; “2013 Data Miner Survey,” Rexer Analytics, 2013, rexeranalytics.com/Data-Miner-Survey-Results-2013.html, statsoft.com/Company/About-Us/Reviews/2013-Published-Reviews#rexerhighlights2013

4Copyright ©2014 Dell Inc. All rights reserved. Copyright ©2014 Dell Inc. All rights reserved.

Predictive models. Extremely

useful in a variety of industries,

predictive modeling can help

forecast revenue and other

business results. Retailers

frequently use it to predict

demand and avoid out-of-stock

or overstock problems. Insurers

employ modeling to forecast

policyholder risk and adjust

premiums accordingly. And

healthcare organizations can

assess the likelihood of patient

readmissions or predict the path of

a disease outbreak.

Text analytics. Techniques

of text analytics enable an

organization to use unstructured

data such as handwritten notes,

typed comments or text from the

web to gain fresh insights — such

as analyzing trends in social media

to determine whether a product is

viewed favorably. Text analytics

is also used for fraud detection,

combining unstructured and

structured information to obtain a

highly accurate model.

Optimization and simulation.

Lastly, optimization and simulation

capabilities are essential in

manufacturing to fine-tune factory

settings, boost production or

ensure parts are delivered when

they are needed. This analytics

technique is also used in the

utilities industry to maximize the

efficiency of power generation.

Delivering measurable results

To help organizations move up

in analytical maturity, Dell offers

a portfolio that provides the

necessary foundation to gain

high-level, actionable insights.

For example, Dell Statistica

provides access to the five types

of advanced analytics techniques,

from segmentation and decision

trees to text analysis and predictive

models. (See the sidebar, “Statistica:

Smart and flexible.”)

The predictive analytics

capabilities of Dell Statistica

accelerate tangible business results.

In healthcare, for example, a major

hospital is using Statistica to help

reduce the occurrence of surgical

site infections and readmissions.

Using predictive models during

surgery, doctors have access to

real-time prescriptive and predictive

decision recommendations and can

take preventative measures before

problems occur — enhancing the

quality of care for thousands of

surgical patients each year.

In another example, a U.S.–

based integrated steel producer

with major production operations

in North America and Central

Europe is using Dell Statistica to

harness big data for optimizing the

plant’s monitoring and reporting.

Enhanced monitoring enables

the plant to better manage costs.

In addition, advanced analysis

of big data helps the company

reduce emissions of particulates

into the atmosphere during

coking operations.

Dell Statistica lets IT teams

provide LOB users across all

industries with self-service access

to sophisticated capabilities like

these, so they can garner the

insights they need to advance

organizational success.

Authors

David Sweenor is the global product

marketing manager for advanced analytics

at Dell. He is responsible for go-to-market

strategies for Dell Statistica. Follow David on

Twitter @DavidSweenor.

Uday Tekumalla is the marketing evangelist

for big data solutions at Dell Software.

He is responsible for raising awareness

of Dell big data offerings. Follow Uday on

Twitter @udayte.

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