Anatomy of the new decision

Anatomy of the New Decision
How Five Hot Trends Are Shaping the Future of
Business Analytics
A White Paper
WebFOCUS iWay SoftwareWebFOCUS iWay Software

1 Introduction
3 Predictive Analytics: Back to the Future
3 Predictive Modeling and Analytics
5 Outsourced Data for Better Insights
6 (I Love Analytics) for Sentimental Reasons
8 Big Decisions Require Big Data
9 Mobile BI: BYOD FTW
11 Anatomy of the New Decision
11 Law Enforcement
12 Retail
13 Solutions From Information Builders
13 Intelligence
13 Integration
14 Integrity
15 Conclusion
Table of Contents

Information Builders1
Introduction
For decades, companies have made decisions based on instinct, hunches, and intuition. Then
came business intelligence (BI), which made decision-making more scientific. Before it was known
as BI, it was referred to as “decision support.” Companies would review the data they had collected
and make decisions about the future based on what had happened in the past. For example:
■■ A two-year, increasing trend of sales in Florida dictates the need to increase inventory levels over
last year in that region to meet demand
■■ A law enforcement agency deploys more of its officers to District A, one its highest crime areas
■■ Students with higher SAT scores have higher graduation rates than students with lower scores,
so to improve graduation rates, a university only accepts students with higher SAT scores
That kind of thinking was good – once. Nowadays, it’s like fighting with one hand tied behind
your back.
Each of the decisions above was made using a small number of data points to conjure up an
overly simplistic conclusion. Smarter decisions would incorporate more relevant data that answers
more sophisticated questions, such as:
■■ What products or services have Florida customers been most positive about on social
networking, blogs, and free-form feedback sites?
■■ What factors – such as weather, special events, and economics – influence various types of
crimes and where they are likely to occur today?
■■ What are the likely consequences of increasing the average SAT score in the acceptance process?
The good news is that exciting technologies are now emerging, and in some cases, converging,
to help organizations drive innovation, which empowers people to make smarter decisions with
insight based on more than just structured, static data. Behind the tech industry buzzwords,
there are practical and proven methods for applying these new technologies to your current
information management strategy and unlocking measurable returns:
■■ Cloud-based information services enable us to get information that a company might not have
previously tracked on its own, such as demographic and market changes in Florida
■■ Predictive analytics combines many factors to present a clear picture of what’s most likely to
happen, what is the best-case scenario, and what action should be taken
■■ Social media analytics help us to understand how other people are reacting to our actions – far
more quickly and reliably than focus groups would
■■ Big data technologies help us to manage the increasing velocity, variety, and volume (the three
Vs) of all of the data that makes this possible
■■ Mobile computing deploys analytics to more people, so they can make smarter decisions
whenever and wherever they are

Anatomy of the New Decision2
Moreover, the technology advancements are more effective when used in concert than when
taken separately.
In this paper, we will discuss how cloud-based services, predictive analytics, social media analytics,
big data, and mobile computing are combining to take business intelligence beyond traditional
boundaries and transform the way critical decisions are made. We will also highlight Information
Builders’ solutions and share two use cases, which demonstrate how two organizations – a law
enforcement agency and a retailer – might use available tools and technologies to tap into the
wealth of information available.

Queries, reports, dashboards, and other forms of business intelligence are often used to answer
simplistic questions: What product mix was sold this month compared to last month? What are
the year-over-year overhead expense trends?
OLAP and query tools have allowed users to refine and enrich those comparisons, but while the
tools have gotten better, faster, and easier to use, the basis for how they facilitate decision-making
based on historical data hasn’t changed much.
Predictive analytics changes the game by going beyond an analyst’s current viewpoint and
providing new, sophisticated insight about how the past can help predict the future.
Predictive Modeling and Analytics
Predictive analytics is often misunderstood. It doesn’t conjure up miraculous forecasts from thin
air. Instead, it correlates the relationships between many factors (most commonly descriptive
dimensional and numeric data) and provides insight into which of those factors may influence
an outcome. That influencing effect can then be given a score or a probability, which becomes a
“predictive analytic.”
The choice of factors affects decision-making the most. For instance, scouting in baseball has
always been based on metrics such as stolen bases, RBI, and batting average, but it took the
statistical analysis headed up by Billy Beane, general manager of the Oakland A’s, to recognize that
on-base percentage and slugging percentage were more predictive markers of offensive success.
Predictive Analytics: Back to the Future
Predictive data mining supports retail initiatives such as target marketing,
cross- and up-selling, and customer acquisition and retention.

Beane could find those qualities more cheaply on the open market, and was thereby able to
assemble a team competitive with the best in the league – for about a third of the payroll cost.
Convenience store transactions might be affected by weather, with in-store ATMs generating
walk-in traffic when outdoor ATMs are less pleasant to use. Used car sales might be affected by
the local real estate rental market. Generic medication prescriptions might see an uptick after a
marketing campaign by a brand-name pharmaceutical company.
The point isn’t that any one of these things is true or false; it’s that you wouldn’t know whether
they were true or false unless you assembled the data and found out where the statistical
correlations are. That modeling, and the predictions that come from it, lead to business value.

With predictive analytics, it can be a challenge to collect enough data while ensuring its relevance
to outcomes. For example, many organizations have failed to collect certain historical data outside
their own sphere of influence – but that external or third-party data may have a significant
influence on their outcomes. After all, who would expect weather to impact retail sales or crime
rates? Why would a company collect stock market trends or changes in tax rates to see if they
affect automobile sales?
But these factors really can affect business results. When the marketing arm of a national retailer
analyzes how sales were affected by a Presidents’ Day promotional campaign – but doesn’t take
into account a serious storm system in the southeastern U.S. – it might reduce its efforts in Florida
when it should be increasing them.
This problem isn’t limited to predictive analytics, either. Any form of analytics will be limited by the
available data.
Fortunately, there are information providers for almost any kind of information you can think of,
and cloud-based web services allow users to unify historical data with information about the
weather, crime, the stock market, travel trends, taxes, and virtually anything else. Integrating cloud-
based external data resources with unstructured input from social networks enables companies to
dramatically improve the sophistication of their decision-making without having to collect every
possible external factor that might affect their businesses.
Outsourced Data for Better Insights

Sellers used to have a chance to talk to potential customers before they made a decision about
what product to buy. Those days are gone. In business-to-business sales, more than 60 percent of
a typical purchasing decision now happens before the buyer ever contacts a supplier.1
Buyers aren’t talking to sellers because they’re talking to each other. Social networks and blogs
have made it very easy for them to get information about products and services that interest
them. It’s incredibly important for businesses to discover what they’re thinking about their
products, services, marketing campaigns, salespeople, return policies, customer support, and
anything else that reflects on them. In other words, they need the ability to do sentiment analysis
about their company, brands, executives, and campaigns.
One influential person tweeting “[brand] is horrible!” can have a devastating effect. A few dozen
Facebook posts complaining about on-hold times or product limitations can deter future buyers.
On the other hand, a vibrant customer community that is nurtured to provide positive statements
will move potential customers into the buyers’ camp. A brand that listens and responds to
customer issues with honesty and transparency can establish even closer relationships based
on trust.
PepsiCo is a great example of a company that understands the power of social insight. It has used
social networks to gather customer insight about its DEWmocracy promotions, which have led to
the creation of new varieties of its Mountain Dew brand. Since 2008, the company has sold more
than 36 million cases of them2
.
The best news of all is that companies can now analyze sentiment continuously, at a fraction of
the cost of other methods, while incorporating sentiment information into other forms of analysis.
Social media sentiment analysis can replace or augment $15,000 phone surveys, $7,000 mail
surveys, and $6,000 focus groups – while catching problems before they get out of control.
(I Love Analytics) for Sentimental Reasons
Accurately understand customer sentiment and visualize the context of the
words used to describe your company, products, and services.

As Forrester’s Zach Hofer-Schall says, “Social media’s prevalence across the web gives consumers
and brands a new way to connect online. But while most businesses know the importance of
social media, most are missing opportunities by not capturing and analyzing the data generated
in social channels.”3
Imagine how different the analysis of a Presidents’ Day promotion might look if, instead of just
looking at the timing of the promotion, it also took into account both the weather and the
sentiment of tweets that contain a related hashtag. Understanding social engagement through an
integrated use of data, tools, and technologies is a clear priority for all organizations for amplifying
customer loyalty, competitive differentiation, and growth.
1 Adamson, Brent; Dixon, Matthew; Toman, Nicholas. “The End of Solution Sales,” Harvard Business Review,
August 2012.
2 Dival, Roxane; Edelman, David; Sarrazin, Hugo. “Demystifying Social Media,” McKinsey Quarterly, McKinsey
Company, April 2012
3 Hofer-Shall, Zach. “Leverage Social Data To Elevate Customer Intelligence,” Forrester, May 2012.

Data volumes are growing rapidly, for many reasons. Predictive analytics are most reliable on
very large data sources. Blog posts and social media can encompass a huge amount of language.
Sensor data (everything from smart utility meters in your house to RFID chips in warehouses) has
made certain things possible, while increasing data volumes dramatically. The mobile channel has
spawned a whole new category of data to track, from in-app clicks to mobile transactions.
And that just accounts for one of the “three Vs” of big data.
A recent study by the Economist Intelligence Unit, commissioned by Capgemini, found that
two-thirds of respondents – 607 global executives (43 percent of them C-level and board
executives) from 20 different industries – say that the collection and analysis of data underpins
their firm’s business strategy and day-to-day decision-making. In fact, just more than half say that
management decisions based purely on intuition or experience are regarded as suspect.4
Because there seems to be value in big data, many companies start collecting massive volumes of
diverse, real-time data before they know what to do with it. Unfortunately, they don’t necessarily
make sure that it’s clean at collection time. Ideally, data is clean as transactions flow into your
systems, such as when the user clicks “OK” on your website or as an RSS feed tells you that a new
blog post is live.
Moreover, having data quality tools helps to correlate information from multiple systems. For
instance, companies may improve their one-to-one marketing dramatically if they can determine
that “jdoe1968” on their website is “Jonathan Doe,” who used a credit card on the phone last
month and also identified himself as “Jon Doe” just now when he entered a store in Manhattan.
Finally, if a company’s data is truly big, most people will need help in finding the information or
analytics that derive from it. They’ll need a search engine that’s fully populated with structured
data, unstructured data, and links to existing reports and analysis.
Big Decisions Require Big Data
4 Olavsrud, Thor. “Big Data Analytics Today Lets Businesses Play Moneyball,” CIO, August 2012.

The new decision isn’t chained to a desk.
Information is our constant companion: at meetings, in coffee shops, or first thing in the morning
if that’s when we need it. How we interact with information can be very personal – so much
so that we now expect to interact with our personal laptops, smartphones, and tablets to get
answers to business questions. As a result, it’s more important than ever that business intelligence
be available on any device, whether iOS, Android, or BlackBerry, and in virtually any form factor.
There are a couple of ways that the mobile channel impacts decision-making and, when
combined with a few of the other tech trends we’ve discussed, can be a total game changer.
First, mobile apps can be designed to empower your employees with real-time information –
and the ability to analyze that information on the fly and on the go. By optimizing data analytics
for mobile platforms, and incorporating the native capabilities of the mobile devices, you
are empowering users, creating a personal connection to your brand for all key stakeholders,
improving efficiency, streamlining communications, and differentiating your services.
But there is also a “back-office” side to mobile that can improve our understanding of our
business so that we make better strategic decisions about its operations, marketing, and finances.
Smartphones are essentially sensors that enable you to get location- and context-aware feedback
from a variety of touchpoints in real time. When you combine this mobile data with other
data sources (i.e., big data) and apply analytics, the result is a whole new understanding of the
workforce, the customer, and the market.
Mobile BI: BYOD FTW
Mobile BI enables you to check critical data at any time, from any location.

Interestingly, this increased emphasis on mobility also increases the emphasis on data quality.
When someone shares information with a lot of people, it had better be right. And since mobile
data also needs to come from every kind of system so people don’t have to wait to get to their
desks to get the real answer, mobile applications also increase the need for data integration.

The new decision – one that leverages predictive analytics, as well as data from cloud and
social media sources – applies to many real-world scenarios. From financial institutions trying
to put together the most successful portfolio of products and services to telecommunications
companies seeking new and effective ways to increase loyalty, the new decision provides insight
that can drive competitive advantage.
Lets look at two potential use cases – one in law enforcement and another in retail.
Law Enforcement
The image above represents a city divided into six sectors. Each sector is color-coded based on
historical crime data: Sector B has the highest crime rates, followed by sectors C, F, D, E, and A.
Standard historical crime analysis would indicate that the police dispatcher would need to put the
greatest police presence in sector B to help deter crime. However, today’s event calendar shows
that there is a free concert in sector A. The concert starts at noon in the city park, with 5,000
attendees expected, but the weather forecast shows a 60 percent chance of rain. This typically
means that there will be at least a 50 percent drop-off in concert attendance.
In the past, when the forecast has called for rain, the crimes that commonly occur in sector B tend
to decrease, while occurrences of petty theft in the shopping mall parking lots in sector F increase.
Anatomy of the New Decision
A
B
C
D
E
F

A look at public sentiment on the city’s Facebook page, as well as on various local blogs, shows
that many people are concerned with the high number of traffic accidents taking place on the
main highway leading into the city in sector E. An astute analyst also notices that activity on the
band’s Facebook page (the band playing the free concert) suggests that there is going to be an
“after party” at one of the nightclubs near the concert in sector A – meaning additional police may
be needed hours after the concert concludes.
This information shouldn’t automate every decision the dispatcher makes – people should still
be in control of critical decisions – but it can crystallize the factors that should affect his choices.
Predictive scoring may tell him that he needs more coverage in sector A at the concert and near
the after party hours later, and he’ll choose to fulfill that need with foot patrols. Meanwhile, he’ll
reallocate traffic police to cover sector E on the highway, and deploy police on bicycles in sector F
in the parking lots of the shopping mall.
Retail
A large retailer sells an average of 100 cases of water each week. However, predictive models
that include weather data show that stores in the southwest will sell nearly twice as many cases
when temperatures soar above 90 degrees. Since temperatures are expected to be high for the
next week, the retailer can adjust its forecasts accordingly. The same retailer has also found that
outlets within five miles of a large body of water (where boating is a common pastime) tend to
sell nearly three times as many marine-grade nuts, bolts, and fasteners as outlets in other areas.
This information was discovered using a web service that provides maps and distances for specific
addresses and points of interest.
Store managers can also monitor a real-time dashboard that displays recent posts on the
company’s Facebook page. These posts are solicited via a large sign at each checkout counter,
asking shoppers to comment on their experience. The dashboard allows managers to track sales
and shopper sentiment, so they can quickly adjust staffing and labor levels accordingly, to ensure
optimum service.
The bottom line is that making decisions is not always a black-and-white, yes-or-no effort.
A decision itself can be a complex array of smaller decisions that combine to produce a
desired result.
Information shouldn’t automate every decision – people should still
be in control of critical decisions – but it can crystallize the factors that
affect those decisions.

Information Builders designs and develops high-value solutions that help companies to boost
revenue by providing unmatched integration of enterprise information assets, dramatically
improving the integrity of the data contained in those assets, and transforming that data into
powerful intelligence for wide-scale use.
Intelligence
The WebFOCUS BI platform combines broad data access with unparalleled usability, scalability,
and low cost of ownership to make information and analytics readily available and easily
consumable to an unlimited number of internal and external users. WebFOCUS features:
■■ Powerful BI that makes reports, queries, and dashboards available to power and business users
■■ Advanced analytics, visualization, location intelligence, and enterprise search to enable accurate
customer analysis, revenue forecasting, price simulation, and more
■■ Comprehensive performance management that aligns strategy with key performance indicators
(KPIs), and balances them against risk
■■ The ability to build once, and deploy across all online and mobile channels for a consistent user
experience
■■ Innovative sentiment analysis to help companies mine data from social media sites, and analyze
it to accurately assess customer opinion
Information Builders’ Intelligence solutions also offer unparalleled scalability, reliability, and ease of
use. So companies can rapidly and economically create and deploy comprehensive, yet intuitive
self-service systems that meet the information needs of thousands, tens of thousands, and even
millions of external customers.
Integration
iWay Software Integration solutions from Information Builders help you to collect every kind
of information, whether you need it in real time or for historical purposes. iWay supports
unstructured data, such as blog posts and social media streams; cloud-based data from web
services or API queries; structured data from enterprise resource planning (ERP), customer
relationship management (CRM), legacy, and other systems; or sensor data, such as RFID or UPC
scans and utility gauge readings. With iWay, organizations can empower real-time decision-
making for competitive advantage and revenue optimization. iWay Integration solutions provide:
■■ A robust integration infrastructure that allows companies to rapidly and economically build
broad-reaching integration architectures
■■ Data integration solutions that facilitate coordination and cohesiveness across even the most
diverse and disparate information environments
■■ A comprehensive universal adapter suite that contains pre-packaged integration components
to provide direct, native access to more than 300 sources, including data, applications, B2B
interactions, and cloud-based systems
Solutions From Information Builders

■■ Big data solutions that support high-performance data stores, such as IBM Netezza, Oracle
Exadata, SAP HANA, Teradata and Teradata’s Aster Data, EMC Greenplum, HP Vertica, 1010data,
ParAccel, and Kognitio, as well as MapReduce databases such Hadoop and MongoDB
With iWay, it’s easy to take data from every kind of system and bring it to your mobile apps –
whether the information is cloistered away in legacy systems or stuck inside proprietary ERP and
CRM applications, whether it’s big data or many tiny transactions, and whether day-old data is
okay or you need it in real time. The most time-consuming and labor-intensive step of mobile app
development – information integration – is cut short dramatically with iWay Integration solutions.
Integrity
iWay Integrity solutions include data quality capabilities that can help you create a data quality
firewall, ensuring the quality of data before it spreads into other parts of your enterprise. The
result is better operational processes, better BI, and – as the data moves into the realm of big data
– better correlated and managed big data analytics. iWay data integrity solutions also provide
master data management (MDM) technology that can correlate disparate information from very
different system types – and can be overseen by data stewards, who can even manage data from
their mobile devices. iWay data integrity solutions comprise:
■■ Data quality management tools, with an automated rules engine, for creating a real-time data
quality firewall that proactively preserves information integrity
■■ Master data management to synchronize disparate data sources and create a single, golden
record for each product, customer, patient, or citizen
■■ Data governance solutions that provide end-to-end control over how information is managed as
it is collected, used, and maintained

The examples in this paper demonstrate how complex effective decision-making can be. Because
a variety of factors influence outcomes, straightforward “yes” or “no” answers simply don’t exist.
Every decision is made up of an array of smaller choices, which will impact the eventual result.
Decision-makers go through great pains to make sure they have gathered all of the appropriate
data to make a well-informed decision.
What is different today is that the new decision can be made with more complete data, more
easily, and in less time than before. The people making those decisions have the means to take
all influencing factors into account, and weigh them based on their relevance, importance, and
impact. The new capabilities outlined in this paper mean less time is spent gathering data, and
more time is given to mulling over the influencing factors, so they have an appropriate amount of
time to use their experience and intuition to make the best decision.
More and more companies are arming their employees with business intelligence tools, like
Information Builders’ WebFOCUS, and iWay Integrity and Integration solutions, to help them with
these types of data-influenced decisions. And as more and more data – primarily from social
media vehicles, mobile channels, and other Internet sources – becomes available for use by those
tools, the accuracy and effectiveness of those decisions will continue to increase rapidly.
Conclusion

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Anatomy of the new decision

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