anatomy of the new decision - cdm media...2 anatomy of the new decision moreover, the technology...

Anatomy of the New DecisionHow 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:

■n 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

■n A law enforcement agency deploys more of its officers to District A, one its highest crime areas

■n 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:

■n What products or services have Florida customers been most positive about on social networking, blogs, and free-form feedback sites?

■n What factors – such as weather, special events, and economics – influence various types of crimes and where they are likely to occur today?

■n 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:

■n 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

■n 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

■n Social media analytics help us to understand how other people are reacting to our actions – far more quickly and reliably than focus groups would

■n 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

■n 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 AnalyticsPredictive 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

AB

C

D

EF


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.

RetailA 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.

IntelligenceThe 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:

■n Powerful BI that makes reports, queries, and dashboards available to power and business users

■n Advanced analytics, visualization, location intelligence, and enterprise search to enable accurate customer analysis, revenue forecasting, price simulation, and more

■n Comprehensive performance management that aligns strategy with key performance indicators (KPIs), and balances them against risk

■n The ability to build once, and deploy across all online and mobile channels for a consistent user experience

■n 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.

IntegrationiWay 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:

■n A robust integration infrastructure that allows companies to rapidly and economically build broad-reaching integration architectures

■n Data integration solutions that facilitate coordination and cohesiveness across even the most diverse and disparate information environments

■n 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


■n 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.

IntegrityiWay 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:

■n Data quality management tools, with an automated rules engine, for creating a real-time data quality firewall that proactively preserves information integrity

■n Master data management to synchronize disparate data sources and create a single, golden record for each product, customer, patient, or citizen

■n 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

Worldwide Offices

Corporate HeadquartersTwo Penn Plaza New York, NY 10121-2898 (212) 736-4433 (800) 969-4636

United StatesAtlanta, GA* (770) 395-9913

Baltimore, MD (703) 247-5565

Boston, MA* (781) 224-7660

Channels (770) 677-9923

Chicago, IL* (630) 971-6700

Cincinnati, OH* (513) 891-2338

Dallas, TX* (972) 398-4100

Denver, CO* (303) 770-4440

Detroit, MI* (248) 641-8820

Federal Systems, DC* (703) 276-9006

Florham Park, NJ (973) 593-0022

Gulf Area (972) 490-1300

Hartford, CT (781) 272-8600

Houston, TX* (713) 952-4800

Kansas City, MO (816) 471-3320

Los Angeles, CA* (310) 615-0735

Milwaukee, WI (414) 827-4685

Minneapolis, MN* (651) 602-9100

New York, NY* (212) 736-4433

Orlando, FL (407) 804-8000

Philadelphia, PA* (610) 940-0790

Phoenix, AZ (480) 346-1095

Pittsburgh, PA (412) 494-9699

Sacramento, CA (916) 973-9511

San Jose, CA* (408) 453-7600

Seattle, WA (206) 624-9055

St. Louis, MO* (636) 519-1411, ext. 321

Washington DC* (703) 276-9006

InternationalAustralia* Melbourne 61-3-9631-7900 Sydney 61-2-8223-0600

Austria Raffeisen Informatik Consulting GmbH Wien 43-1-211-36-3344

Bangladesh Dhaka 415-505-1329

Brazil InfoBuild Brazil Ltda. São Paulo 55-11-3285-1050

Canada Calgary (403) 437-3479 Montreal* (514) 421-1555 Ottawa (613) 233-7647 Toronto* (416) 364-2760 Vancouver (604) 688-2499

China Beijing 0086-010-5128-9680

Estonia InfoBuild Estonia ÖÜ Tallinn 372-618-1585

Finland InfoBuild Oy Espoo 358-0-207-580-840

France* Puteaux +33 (0)1-49-00-66-00

Germany Eschborn* 49-6196-775-76-0

Greece Applied Science Ltd. Athens 30-210-699-8225

Guatemala IDS de Centroamerica Guatemala City (502) 2412-4212

India* InfoBuild India Chennai 91-44-42177082

Israel SRL Software Products Ltd. Petah-Tikva 972-3-7662040

Italy Milan 39-02-30314-558

Japan KK Ashisuto Tokyo 81-3-5276-5863

Kuwait InfoBuild Middle East Safat 965-2-232-2926

Latvia InfoBuild Lithuania, UAB Vilnius 371-67039637

Lebanon InfoBuild Middle East Beirut 961-4-533162

Lithuania InfoBuild Lithuania, UAB Vilnius 370-5-268-3327

Mexico Mexico City 52-55-5062-0660

Netherlands* Information Builders (Benelux) B.V. Amstelveen 31 (0)20-4563333

Nigeria InfoBuild Nigeria Garki-Abuja 234-9-290-2621

Norway InfoBuild Norge AS c/o Okonor Tynset 358-0-207-580-840

Portugal Lisboa 351-217-217-400

Qatar InfoBuild Middle East Doha 974-4-466-6244

Russian Federation InfoBuild CIS Moscow 7-495-797-20-46 n Armenia n Azerbaijan n Belarus n Kazakhstan n Kyrgyzstan n Moldova n Tajikistan n Turkmenistan n Ukraine n Uzbekistan

Saudi Arabia InfoBuild Middle East Riyadh 966-1-479-7623

Singapore Automatic Identification Technology Ltd. Singapore 65-6286-2922

South Africa Fujitsu (Pty) Ltd. Cape Town 27-21-937-6100 Sandton 27-11-233-5432 InfoBuild (Pty) Ltd. Johannesburg 27-11-510-0070

South Korea Uvansys Seoul 82-2-832-0705

Spain Barcelona 34-93-452-63-85 Bilbao 34-94-452-50-15 Madrid* 34-91-710-22-75

Sweden InfoBuild AB Solna 46-7-024-656-50

Switzerland Dietlikon 41-44-839-49-49

Taiwan Galaxy Software Services, Inc. Taipei (866) 2-2586-7890

Thailand Datapro Computer Systems Co. Ltd. Bangkok 66(2) 301 2800

United Arab Emirates InfoBuild Middle East Abu Dhabi 971-2-627-5911 n Bahrain n Egypt n Jordan n Oman Dubai 971-4-391-4391

United Kingdom* Uxbridge Middlesex 0845-658-8484

Venezuela InfoServices Consulting Caracas 58212-763-1653

* Training facilities are located at these offices.

Corporate Headquarters Two Penn Plaza, New York, NY 10121-2898 (212) 736-4433 Fax (212) 967-6406 DN7507213.1012Connect With Us informationbuilders.com [email protected]

Copyright © 2012 by Information Builders. All rights reserved. [102] All products and product names mentioned in this publication are trademarks or registered trademarks of their respective companies.

Printed in the U.S.A. on recycled paper

anatomy of the new decision - cdm media...2 anatomy of the new decision moreover, the technology...

Documents