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The Changing Face of Business Intelligence

Originally published November 18, 2008

Howard Dresner originated the term business intelligence. In the early 1990s, he defined businenss intelligence (BI) as “a set of concepts and methodologies to improve decision making in business through use of facts and fact-based systems.” While many of us focused on data integration and believed that data warehousing was leading-edge, Dresner created the vision that shaped what we know as business intelligence today.

The business intelligence that was once visionary is now commonplace, but sometimes disappointing. Tomorrow’s business intelligence must become something very different. Too much of today’s business analytics has little connection with real business analysis. At times I am tempted to declare that “the emperor has no clothes.”

But I believe that a significant BI shift is about to occur. Conditions are aligned to drive change. Economic factors demand smart business. New expectations for corporate and executive accountability raise the stakes. Consolidation of the BI tools market opens the door to new and innovative vendors. The next evolution of BI will happen soon, it will happen quickly, and it will expose and overcome the self-delusion that is part of business analytics today.

In recent years, we have strayed from Dresner’s early vision. Current definitions describe business intelligence largely as tools and technology. Some fail to mention business and others include it almost as an afterthought. The next evolution of BI must return to the vision, enrich that vision and expand upon it to create opportunity for truly intelligent business. The next developments in business intelligence will occur in five significant areas:

  • Compelling definition

  • Focus on business analytics

  • Closing the gap between analytics and analysis

  • Focus on business analysts

  • Focus on business

Compelling Definition

The evolution of business intelligence begins with a definitional shift. Perhaps the most widely quoted BI definition today is David Loshin’s “the processes, technologies and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business actions.” Larissa Moss describes BI as “an architecture and a collection of integrated operational as well as decision-support applications and databases that provide the business community easy access to business data.” And Steve Dine defines it as “the process, architecture, technologies and tools that help companies transform their data into accurate, actionable and timely information and disseminate that information across the organization.”

While all of the definitions are technically correct, it feels like something is missing. The troubling thing is that all of the definitions are IT-centric. They describe processes, tools, technologies, data, databases and applications. In each of the few instances where the word “business” appears, it is used as an adjective that qualifies seemingly more important nouns: action, community and data.

So what is compelling about these definitions? What do they offer as motivation for a business to spend time, money and energy on business intelligence? What do they provide to a BI program as purpose, direction, and the basis for goals and measures of success? I think that they fall short on all counts.

I define business intelligence as “the ability of an organization or business to reason, plan, predict, solve problems, think abstractly, comprehend, innovate and learn in ways that increase organizational knowledge, inform decision processes, enable effective actions, and help to establish and achieve business goals.” This definition, I believe, is compelling. It describes the qualities of an intelligent business and is sufficiently specific to serve as the basis for purpose, direction, goals and measures. Equally important, it reflects and builds upon Dresner’s BI vision. To further understand the origin and the implications of this definition, see my April 2008 BeyeNETWORK article Business Analytics – Getting the Point.

Focus on Business Analytics

The second major transformation in the changing face of BI is a shift of attention from data to analytics. Don’t be tempted to read “focus on analytics” as “focus on dashboards and scorecards.” Dashboards and scorecards are not analytics; they are simply useful ways to deliver metrics to analytic processes.

Business analytics encompasses the science, disciplines and processes of business analysis. It follows reporting (which may use dashboards and scorecards) and precedes understanding (which is done by people). This is the point at which information leads to knowledge – it takes both analysis and understanding to achieve knowledge. To achieve useful knowledge, the analysis and understanding must have purpose. For business analytics, that purpose is positioning to reason, plan, predict, solve problems, innovate and learn – the defining capacities of intelligent business. Figure 1 illustrates the role and placement of analytics in business intelligence.


The point of business analytics is knowledge: knowing what has happened, knowing why it happened, knowing what to expect in the future and knowing what to do about it. This is the next “hot spot” of business intelligence. There is power in analytics, but also complexity. It involves statistics, profiling and pattern recognition, behavioral analysis, time series analysis, predictive modeling, visualization, cause-and-effect studies and more.

The complexity that makes business analytics powerful also presents a dilemma. Despite integrated data and powerful tools, most business analysis is performed by loading local data into simple spreadsheets. Anecdotal evidence suggests that as much as eighty-five percent of business analysis is actually performed using "manualytics" processes. It is clear that a gap exists between the potential of business analytics and the realities of business analysis.

Closing the Gap

The analytic gap shows itself in virtually every organization as two distinct and highly polarized approaches to business analysis. Figure 2 illustrates the polarity as described by Gartner. The IT-intensive approach is one of managed reporting, end-user query, OLAP, dashboards, scorecards and data mining. It is regarded as expensive, rigid, slow, inaccessible, server-centric and dependent upon a large infrastructure. The office productivity approach is dominated by Excel spreadsheets and Access databases. It is a world of data dumps, locally created data, manualytics and spreadmarts. This desktop-centric approach is considered to be non-scalable, untraceable, unrepeatable, unsecured and particularly difficult to audit.


Despite negative characterizations – expensive, rigid, untraceable, difficult, etc. – both of today’s analytic approaches have merit. The goal of next generation analytics is not to choose – certainly not to eliminate one approach in favor of the other. Instead, it is to fill in the middle, moving from two extremes to a continuum of analytic options. Trends in BI practices and in analytic technologies show movement in the right direction.

Among the practices that will shape the future of business analytics are:

Pervasive BI – Extending the reach of business intelligence into the business community by reaching more people at all levels with valuable information. The key to pervasive is more people.

BI for the Masses – Extending the range of BI capabilities that are within the grasp of small businesses and small IT departments. Where the pervasive focus is more people, the goal here is more affordable.

Role-Based BI – Analytic outputs are tailored to the needs and interests of specific audiences. Measures, metrics, trends, scorecards, dashboards, etc. are tailored to business functions (research, marketing, sales, finance, etc.) and to business level (strategic, tactical, operational). Role-based business intelligence seeks to achieve more relevance.

Discovery-Based Analytics – Interactive, exploratory, investigative analytic processes recognize the natural cycle of analysis where each answer brings new questions. Discovery analytics enable the principle of “listening to the data” to learn what it can tell you. The goal here is more knowledge.

Agile Analytics – Providing rapid response to situations where immediate need for analytics exists. Agile analytics encompasses the ability to quickly and continuously adapt to changing circumstances, both business and technical. The agile objectives are more adaptable and faster.

None of these trends, of course, are practical without the aid of supporting technology. Innovative companies now offer tools and technology to enable the next generation of analytics. Some of the interesting products are:

Cloud9 Analytics – role-based, on-demand business intelligence applications using a software-as-a-service (SaaS) model.

eThority – web and desktop based, user-focused, accessible and scalable approach to business-driven analysis of enterprise data.

illuminate – a correlation database that brings agility to the back-end data integration tasks that are barriers to agile analytics.

Lyza – depolarizing and “filling the middle” with desktop-based data gathering, data analysis, reporting and analytic publishing.

Netmap – a visual approach to discovery-based analysis.

PolyVista – extending OLAP with prepackaged, easy-to-use data mining and discovery automation capabilities.

QlikView – rapid deployment of visual analytics from back-end data integration to front-end data views.

This is certainly not an exhaustive list, but a sample of the kinds of products that are shaping the future of analytics and changing the face of business intelligence. Each company in different ways contributes to closing the analytics-to-analysis gap.

Focus on Business Analysts

Analytics are an important aspect of business measurement and performance management. The analysts, however, are even more important than the analytics. It is analysts – the people who perform analysis – who find meaning in the data. These are the people who explore cause-effect relationships and who guide decision-making processes. It is they who will lead the charge to reshape decision making in business.

The shift to analyst focus is already underway. It goes hand-in-hand with the focus on business analytics. The goals of technology providers – user focused, ease of use, desktop based, agile, visual and accessible – all recognize and respond to the important role of business analysts.

But it takes more than technology. To achieve the right focus we must first answer the question: Where are the Business Analysts? Bear in mind that analyst focus is not reserved for those with business analyst job titles. Every manager in a business is a de facto business analyst. The controller performing cash flow analysis, the compliance officer performing risk analysis and the marketing manager analyzing campaign effectiveness all have some business analyst roles and responsibilities. These people are analytic professionals, though they may not be professional analysts.

Focus on Business

The final piece in the BI evolution puzzle is focus on business. The concentration here needs to be much deeper than the lip-service to business alignment. BI and business need to be consciously and actively aligned in three dimensions: management, motivation and measurement. Figure 3 illustrates this multidimensional view of analytic alignment – a business-oriented BI framework.


The management dimension is used to achieve functional alignment. It describes what is managed and measured in analytic systems – the functional domains that are areas of management responsibility. The diagram in Figure 3 shows eight domains that are common to virtually every business. Don’t hesitate to adapt and customize these to be specific to your business. Those in the insurance industry, for example, might choose to show claims, actuarial and underwriting as items in the management dimension. Higher education may show education, research and student services. Retail might include merchandising, customer relations, supplier relations, etc.

The motivation dimension supports goal alignment. It describes why we measure and manage – the criteria used to determine quality of management. The diagram illustrates four criteria: performance, compliance, profit and risk. This dimension may need to be tailored to the nature of your enterprise. Public sector organizations, for example, may need to include public service and public perception. Higher education institutions will certainly want to include accreditation.

The measurement dimension connects management and motivation with analytics. It describes the how of measurement-based management. The framework shows six elements that apply to enterprises of all types and in virtually every industry. A measure is a single, quantitative data value coupled with data describing the thing that is quantified and the time of measurement. A metric is a system of measures with sufficient context to provide information through sorting, grouping, filtering, summarization, etc. References are the comparative values that give meaning to metrics – the basis by which metric values can be evaluated as “good” or “bad.” References include thresholds, targets, previous values, etc. A trend is a specific kind of reference in which a series of metric values is compared to observe behavior over time. Indicators are metrics used to evaluate performance against tactical and operational goals. An index is a composite of multiple indicators that is used to evaluate performance against strategic goals.

The three-dimensional approach to analytics is a powerful alignment tool. As illustrated in Figure 3, the framework contains 192 cells. Each cell represents analytic opportunities. When used to align, prioritize and identify analytic needs, the framework places the right emphasis on the business part of business intelligence.

In Conclusion

Over the coming several months, business intelligence will experience change that will have broad, deep and lasting impact. Changing focus to simultaneously concentrate on business analytics, business analysts and business itself is significant. Ultimately, it will change the way that we think about business and the way that business decisions are made. When thoughtful analysis replaces gut feel, conventional wisdom, tribal knowledge and “the way we’ve always done it,” then we will realize the true potential of business analytics and enter into the next generation of business intelligence. We will truly enable business capacity to reason, plan, predict, solve problems, think abstractly, comprehend, innovate and learn. We will finally come full circle to realize Howard Dresner’s BI vision.

  • Dave WellsDave Wells

    Dave is actively involved in information management, business management, and the intersection of the two. He provides strategic consulting, mentoring, and guidance for business intelligence, performance management, and business analytics programs.

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