BUSINESS INTELLIGENCE: A NEW NAME OR THE FUTURE OF DSS?

The ISDSS meetings began in 1991, 12 years ago. Over the years, the focus,

rightly, has been on supporting individuals and groups in making decisions,

taking new developments into account. In 1997, in Laussane, for example, I

talked about “Data Warehouses, OLAP, Data Mining, and the New DSS”.

Decision support systems, themselves, go back to the late 1960’s when people

started moving past transactions and MIS, to consider how we should support

the top of the organization. Our early efforts involved such things as the

assumption that managers would use Iverson notation to write their own

systems. Later we recognized that top decision makers would not spend their

valuable time actually writing programs.

By 1982 we had Sprague and Carlson’s (Sprague and Carlson, 1982) insight that

DSS consists of three components:

the (analytic) model

the database

the human interface

where the model was supported at the input by the data and at the output by the

human interface.

We also began to realize that for managers, the modeling and analysis was too

tough and that they had neither the time nor the interest to actually do modeling.

So we changed the paradigm. Managers would use a simplified version of the

DSS that gave them outputs from the data – we called them Executive

Information Systems (EIS) – that consisted mostly of pretty pictures plus some

drilldown capability. As Jack Rockart, I believe, said, senior executives used EIS

to find problems and then told their analysts to use DSS to solve them.

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EISs were the 1980’s. By the early 1990’s it became clear that DSS groups are

very good at analysis and interface, but generally were deficient in the database

area. Fortunately, in the early 1990’s, data warehouses with their clean, single

truth form of data came along and the buzzword was OLAP, a good name for a

racehorse, but really standing for on-line data processing.

An entire industry was built around these variations on a theme. Vendors

ranging in name from Brio to SAS each doing multimillion dollars in business

each year came on the scene, and some even survive today.

The purpose of this brief retelling of history is not to make you nostalgic for the

past, but to indicate that, over the years, DSS assumed many forms, changing

with both the technology and our understanding of managerial work and work

patterns. Moreover the buzzwords changed. The vendors changed what they

produced very slowly, always claiming they had the latest, whether they did or

not. I recall one firm turning out a 24 page brochure on their executive

information system one year and then turning out a 28 page brochure the next

year telling everyone that they supported OLAP. Of course, their product was

identical from year to year. if you read both brochures, you saw that nothing had

changed. The original 24 pages were reused and a claim was added that they

supported OLAP,

As we go into the early 2000’s, the same situation obtains. We have two “new”1

buzzwords:

Business intelligence

Competitive intelligence

And in recent months, the term “Business Process Management” has come to

the fore. A new buzzword, it looks at developing metrics on how well a business

is performing.

1 The term business intelligence goes back to 1989, when it was coined by the Gartner Group

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which the same vendors are now selling as their prime product rather than DSS,

EIS, or OLAP but, when we look under the hood, most of the outputs look the

same.

At one level, they are selling something new. At another, it is just an update of

what you and I have called DSS over the last 30 to 35 years.

Let me begin this discussion of what business intelligence is with an interesting,

but discouraging piece of data. When I searched the academic literature for

business intelligence, I had a hard time finding academic publications.

I checked the latest three books on DSS in my library and I found only one, Dan

Power (2002) , who mentioned Business Intelligence, and even that is brief.

Power, however, makes the important distinction that business intelligence is a

form of Data-Driven DSS as distinguished from seven other types of DSS.2

Analyzing business intelligence products and practice shows that existing

systems are broader than data-driven but that data-driven use dominates.

In terms of academic literature, the number of articles is few. Examples of the

references found are shown in Table 1. Of the ten references in the table, eight

deal with competitive intelligence, a branch of BI. One of the eight (Rouibah and

Old-Ali) deals with competitive intelligence even though it uses business

intelligence in its title

Table 1. Academic Articles on Business Intelligence and Competitive Intelligence

Cody et. al.(2002)

Hall (200)

Markus and Lee (2000)

Powell and Bradford (2000)

Rouach and Santi (2000)

Rouibah and Ould-ali (2002)

Teo and Choo (2001)

Vedder and Guynes (2002)

Wiggins (2001)

Weir (2001)

2 Powers’ complete list includes 10 types of DSS: (1) data-driven, (2) model-driven, (3) knowledge-driven,(4) document-driven, (5) communications-driven and group, (6) inter- and intraorganizational, (7 general purpose or function-specific, and (8) web-based.

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Thus, although the vendors are pushing it and trade magazines such as

Intelligent Enterprise and DM Review are full of it, business intelligence seems to

have flown under our radar as academics.

My remarks, therefore, are based principally on what is in the trade literature so

that you can gain a picture of what practice is doing and to urge you to do

research that moves forward from where industry is

The purpose of my talk therefore is to tell you what the shouting is all about and

to help you to decide whether business intelligence is simply a new name, a

repackaging if you will of DSS in a more appealing wrapper, or whether it

represents the true future of DSS.

WHAT IS BUSINESS INTELLIGENCE

Whereas many of our previous efforts were focused on the decision and how it is

made, over the years we have come to realize that our real contribution comes

from creating the knowledge climate in which decisions can be made. As a

group we are analysts at heart—trying to find out what the situation is and

presenting it to people who make the choice. Thus, we create knowledge – both

about our own firm and about our competition. That knowledge is intelligence

about where we have been, where we are, where our competitors are, and most

important about where things are moving in our firm, in our competitors, and in

the global business and government that is our environment.

We use the following as a working definition of business intelligence systems:

Business Intelligence systems combine data gathering, data storage, and

knowledge management with analytical tools to present complex corporate and

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competitive information to planners and decision makers. The objective is to

improve the timeliness and quality of the input to the decision process

Implicit in this definition is the idea (perhaps the ideal) that business intelligence

systems provide

-actionable information and knowledge

-at the right time

-in the right location, and

-in the right form.

Sometimes business intelligence refers to on-line decision making. Most of the

time, it refers to shrinking the information time window so that the intelligence is

still useful to the decision maker when the decision time comes. In all cases,

business intelligence is viewed as being proactive.

SO WHAT IS NEW?

Two fundamental sea changes have occurred:

(1) in the technology and

(2) in the DSS process.

Let’s begin with the essential components of proactive BI( Langseth and Vivatrat,

2003).

1. real-time data warehousing,

2. data mining

3. automated anomaly and exception detection,

4. proactive alerting with automatic recipient determination,

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5. seamless follow-through workflow,

6. automatic learning and refinement

7. geographic information systems (Sidebar 1)

8. data visualization (Sidebar 2)

When you look at these enhancements you see that they are the result of

advances in technology. For example,

-the data warehouse with its single truth,

-data mining which lets the data tell you rather than requiring you have a

hypothesis

-automatic tasks previously done manually or by face-to-face interaction,

-AI-based tasks using bots

These advances are not just incremental, they are step increases and they

change what can be done.

SIDEBAR 1

A TECHNOLOGY FOR BUSINESS INTELLIGENCE:

GEOGRAPHIC INFORMATION SYSTEMS (GIS)

In the narrow sense, a Geographic Information Systems (GIS) is a software

package that links databases and electronic maps. At a more general levels, the

term GIS refers to the capability for analyzing spatial phenomena. These

systems are an important business intelligence tool for exploiting the increasing

amount of two (and more) dimensional data available in a form that can be

understood by analysts and managers.

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In addition to collecting, storing, and retrieving spatial location data, GIS are used

to identify locations which meet specified criteria (e.g., for new store location),

exploring relations among data sets, assessing alternatives and aiding in

decision making, and displaying elected environments both visually and

numerically. In practice, a GIS consists of a series of layers, each presenting a

particular two-dimensional feature, that can be superimposed accurately on top

of one another. Some examples:

a marketing group overlays customer locations, school locations,

distribution centers and existing retailers selling their own and/or their

competitors products.

A telecommunications company selects the number and location of

switching centers and routes in a communication network. The system

displays such quantitites as traffic, costs, and transmission times. Users

can redefine the network on the screen, can create multiple views, see the

effect of ‘what if’ changes and new data because the system re-computes

for each change, take constraints into account, and see where the

proposed solution fails to meet criteria.

SIDEBAR 2

A TECHNOLOGY FOR BUSINESS INTELLIGENCE:

VISUALIZATION

With the flood of data available from information systems, business intelligence

analysts and decision-makers need to make sense out of the knowledge it

contains. Visualization is the process of representing data as visual images.

Unlike Geographic Information Systems which typically deal with physical

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spaces, the underlying data could, for example, represent abstract objects, such

as profit, sales, or cost. If the data is abstract, then a visual analog must be

created. And visual analogs today go far beyond the pie chart and the bar chart

(Tegarden 1999).

Visualization has been used to create advanced dashboard in which large

amounts of information are presented on a single screen. Visualization allows:

Exploiting the human visual system to extract information from data,

Provides an overview of complex data sets,

Identifies structure, patterns, trends, anomalies, and relationships in data,

Assists in identifying the areas of "interest"

That is, visualization allows BI analysts to use their natural spatial/visual abilities

to determine where further exploration should be done and where action is

required. .

Visualization technologies already have been deployed in finance, litigation,

marketing, manufacturing, training, and organizational modeling

BI is also a process that involves humans and how they act, and much research

still needs to be done here. In simple terms, to obtain high-impact analysis

(Morris, 2003) you have to:

1. Recognize the application imperative and focus on an important business

issue.

2. Individualize intelligence to each person who makes decisions

3. Build discipline in the decision making processes

4. Recognize new skills are required for knowledge workers

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5. Deal with the complexity of closed loop, adaptive systems

Sidebar 3 is a case example of the use of technology and the use of process for

BI. It involves two DSS companies whose names should be familiar to you.

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SIDEBAR 3

EXAMPLE OF USING BUSINESS INTELLIGENCE FOR AN ACQUISITION

An example of using BI is the acquisition of Execucom of Austin TX by Comshare

of Ann Arbor, MI several years ago. Both firms were arch competitors in

developing and selling 4th generation languages, which are standard business

intelligence products. The President of Comshare at the time, Crandall by name,

used a service that monitored newspaper articles about specific competitors.

One day his screen showed an article that reported an interview with Execucom’s

Chairman, Anderson, in the Austin newspaper in which Anderson unburdened

himself. Anderson discussed the miserable relation between his company and

its owner, a firm in an adjacent state in a different business who had acquired

Execucom to get into the computer business but which, he said, did not

understand what Execucom did. Reading the way Anderson unburdened himself

to the reporter, Crandall recognized that Execucomm could be bought, probably

relatively cheaply. The acquisition would eliminate a major competitor, broaden

Comshare’s product line, and perhaps most important, move a cadre of very

smart people into Comshare. Acting on this intelligence information, Comshare

bought Execucom.

Although the business intelligence led to the acquisition, the end result was

ironic. Comshare rapidly stripped the Austin operation, moved key personnel to

Ann Arbor, and a couple of years later closed down Execucom’s premier product

in favor of its own. Lesson: Good intelligence may not lead to good decisions in

the long term.

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WHAT DO FIRM’S USE BI FOR?

A survey by the Gartner Group (Imhoffe, 2003) of the strategic uses of

business intelligence, found these uses were ranked by firms in the following

order:

1. Corporate performance management

2. Optimizing customer relations, monitoring business activity, and

traditional decision support.

3. Packaged standalone BI applications for specific operations or

strategies

4. Management reporting of business intelligence.

One implication of this ranking is that ordinary reports about a firm’s

performance and that of competitors, which is the strength of many existing

software packages, is not enough. Extensive analysis is required.

A second implication is that too many firms still view business intelligence

(like DSS and EIS before it) as an inward looking function.

SIDEBAR 4

BI APPLICATIONS

The following examples come out of the special case of business process

management. The data are not publicly available yet, so the company names are

disguised.

A company that provides natural gas to homes created a dashboard that

supports operational performance metric management and allows real time

decision making. In one application of the dashboard, the number of repeat

repair calls was reduced, resulting in a saving of $1.3 million

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At a large member-owned distributor to hardware stores, using a dashboard

reduced the amount of inventory that must be liquidated or sold as a loss leader

from $60 million to $10 million. Their BI system also allows their member stores

to see their own performance relative to similar stores.

The foregoing examples deal with the special BI case of Business Process

Management. Because the data are not yet publicly available,

the company names are disguised.

RETURN ON INVESTMENTS

Like most information systems, BI up-front costs are high as is upkeep.

Unfortunately, although reductions in information systems costs from business

intelligence efficiencies can be forecast, the IT savings are only a small portion of

the payoff, which comes from the opportunities seized and the difficulties

avoided. It is rare for a BI system to pay for itself through cost reductions.

Costs.

Most firms do some form of business intelligence, although only a few have

complete BI systems. Putting a BI system in place from scratch includes costs of

additional hardware and large amounts of software. In addition to buying a BI

software package, external data needs to be paid for and often a dependent data

mart specifically for Business Intelligence is established. And then, of course,

there are the costs of the analysts and their support staff, the maintenance and

update of hardware and software, and the time spent by the user community

reading and thinking about the outputs of BI.

Benefits.

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Most BI benefits are soft. The hope is that a good BI system will lead to a

“big bang” return at some time in the future. However, it is not possible to

forecast big bangs because they are fortuitous and infrequent.

Despite the softness of the benefits, companies are willing to invest

because they need to understand what their business and their competitors

business is about.

One indicator that firms believe the benefits are worth it is the shift from

providing BI for specialists to providing BI for the masses of employees as a way

of closing the gap between operations and analysis. Established analytic

practice, especially with online analytic processing (OLAP) typically involves a

solitary user exploring data in what’s usually a one-off experience (Russom,

2003). Decisions are made at many organizational levels not just the executive

level, the emergence of new analytic tools purports “BI for the masses”. Rolling

out BI tools like data mining to the masses has shown improvement (McKnight

2003). Examples of large deployments include deploying BI tools to 70,000 at

France Telecom, 50,000 users at US Military Health System, and to several other

firms at the 20,000 user level (Schauer 2003).

COMPETITIVE INTELLIGENCE

Competitive intelligence (CI) is a specialized branch of Business

Intelligence. It is “no more sinister than keeping your eye on the other guy, albeit

secretly” (Imhoff, 2003). A more formal definition is given by the Society of

Competitive Intelligence Professionals (http://www.SCIP.org 3) Yes, there is such a

society!

SCIP’s Definition:

Competititve Intelligence is a systematic and ethical program for

gathering, analyzing and managing external information that can affect your

company’s plans, decisions and operations.

3 http://www.scip.org/ci/ “What is CI?”

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In other words, CI is the process of ensuring marketplace competitiveness

through a greater understanding of competitors and the overall competitive

environment. “You can use whatever you find in the public domain to ensure that

you will not be surprised by your competitors.” (Imhoff 2003).

SIDEBAR 5

EXAMPLES OF COMPETITIVE ANALYSIS

*Texas Instruments made a $100 million acquisition based on their

knowledge of a competitions potential bid, (Lavelle, (2001)).

* Merck & company developed a counter strategy to its competitor’s forth-

coming product based on competitive intelligence reports, a savings of $200

million, (Imhoffe, 2003)

* Illuminet, a company that delivers advanced network, data base, and billing

services, stayed a step ahead by using a vendor (QL2 software) to retrieve

information posted on their competitors web sites (Moores, 2002)

Competititve intelligence is not as difficult as it sounds. Much of what is

obtained comes from sources available to everyone, including:

* Government information

* online databases,

* Interviews or surveys,

* special interest groups (such as academics, trade associations, and

consumer groups),

* private sector sources (such as competitors, suppliers, distributors,

customer)

* media (journals, wire services, newspapers, financial reports, public

speeches by competitor executives)

The challenge is not lack of information; it is the ability to differentiate

useful competitive information from chatter or even disinformation.

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Of course, once you start practicing competitive intelligence, the next

stage is to introduce countermeasures to make the CI task about you more

difficult for others. The game of measure, countermeasure, counter-

countermeasure, and so on to counter to the nth measure is played in industry

just as it is in politics and in international competition.

THE MARKET AND THE VENDORS

AMR research estimates the current BI market at $6 billion with projection to

reach $12 billion by 2006 (Darrow, 2003). At a time when demand for most IT

products is soft, demand for BI applications continues to grow. Gartner forecasts

that this growth will push demand for consulting and systems integrators pushing

the worldwide market for professional and support services from $10.5 billion in

2002 to $15.6 billion in 2006, a 10% average annual increase (Soejarto 2003)

As we indicated earlier, a large number of firms are involved in aspects of the BI

business. For example, ratings of companies to watch business intelligence in

2003 published in Intelligent Enterprise (Stodder,2003) included Aydatum, Brio

software Decisions, Cognos, Crystal Decisions, E-Intelligence, Fair Issac,

Hyperion, MicroStrategy, ProClarity, Siebel, and Spotfire. In the over-all category

of “most influential” the companies4 rated were Teradata, SAS, IBM, Outlook

Soft, Business Objects, Microsoft, Manhattan Associates, PeopleSoft, Oracle,

Ilog Inc., Insight Software, and Open Souce/Linux.

Whereas in the past a large number of companies built their own systems, the

trend is toward buying packages. Gartner Research found a reduction in the

number of firms that plan to manage their BI integration internally dropped from

49% in 2001 to 37% in 2002 (Soejarto, 2003). The reason for this change is that

the traditional custom design, build, and integrate model for BI systems takes too

4 Companies were listed only once, rather than the same company being repeated in several categories, which some might well have been.

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long (at least six months) and costs too much (2 to 3 million) (Rudin and Cressy,

2003). In contrast, pre-built analytic applications result in lower total cost of

ownership. Implemented quickly, they deliver rapid return on investment, and

provide, performance, scalability, and flexibility (Rudin and Cressy, 2003).

MANAGERIAL QUESTIONS AND ANSWERS

1. Is business intelligence an oxymoron? A shorthand for cloak and dagger

spying on competitors and government? An important, legitimate activity?

Despite its name, business intelligence is about trying to understand your

own position, your customers, and your competitors. It is neither ethical nor legal

to spy on competitors. BI is an important part of a firm’s planning and operational

decision making.

2. What is new about today’s business intelligence compared to previous

systems?

Business intelligence is a natural outgrowth of a series of previous

systems designed to support decision making. The emergence of the data

warehouse as a repository, including the advances in data cleansing that lead to

a single truth, the greater technical capabilities of hardware and software all

combine to create a richer business intelligence environment than was available

previously

3. What types of business intelligence are there?

Business Intelligence is used to understand:

The capabilities available in the firm;

the state of the art, trends, and future directions in the markets,

the technologies, and the regulatory environment in which the firm

competes; and

the actions of competitors and the implications of these actions.

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4. What will you be able to do if you invest in BI?

Business Intelligence systems present complex corporate and competitive

information to planners and decision makers. The objective is to improve the

timeliness and quality of the input to the decision process.

5. Who uses BI?

Business intelligence is used by managers throughout the firm. At senior

managerial levels, it is the input to both strategic and tactical decisions. As lower

managerial levels, it helps individuals to do their day-to-day job.

6. How do you gather and transfer BI?

Business intelligence is a form of knowledge. The techniques knowledge

management for generating and transferring knowledge (Davenport and Prusak

1995) apply. Some knowledge is bought (e.g., scanner data in the grocery

industry) while other knowledge is created by analysis of internal and public data.

Knowledge transfer often involves disseminating intelligence information to many

people in the firm. For example, sales people need to know market conditions,

competitor offerings and special offerings.

7. Do you need a separate organization for BI?

Most medium and large firms assign people, often full time, for planning

and for monitoring competitor action. These people are the ones who form the

core groups for business intelligence initiatives. Whether they are centralized or

scattered through SBU’s is a matter of organizational style.

8. What technologies are available?

Most of the technologies needed for business intelligence serve multiple

purposes. For example, the World Wide Web is used for both knowledge

generation and knowledge transfer. However, specialized software for doing

analysis is the heart of business intelligence. This software is an outgrowth of the

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software used for decision support and executive information systems in the

past. It incorporates many of the technological advances that we discussed

above.

FINAL THOUGHTS

We titled this article “Business Intelligence: A New Name Or The Future Of DSS?”. I

hope this paper gives you enough insight that you can answer this question for

yourself. However, I’m sure you would like to know where I think we are.

My view is that both alternatives are true. Much of what is going on is the result of new

technology, particularly data technology, being made available, for the kinds of studies

that DSS performed, and performed well, over the years. So, in one sense, the

business intelligence name is simply a replacement for the DSS name that overcomes

the old view in industry that DSS is principally about modeling. A new name gives us a

new skin—and makes people think about us in a different way. Semantics matter.

Yet, there is also something deeper going on.

We are getting better at solving business problems and becoming more aligned

with the imperatives of the firm.

We are better at dealing with the really large amounts of high quality data

becoming available and in incorporating new technologies and new analysis

methods.

We are democratizing the dissemination of our results and moving to support

people from the bottom to the top of the organization.

I therefore view business intelligence and competitive intelligence as steps along the

way. The terms are the short term future. However, as we become more adept at the

business intelligence function we will inevitably find new ways of thinking about

decision problems and new technologies to incorporate. We will have new names for

our field but, at heart, we will be solving the next generation of decision support

problems.

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