Introduction

Whether considering a new business intelligence solution, creating dashboards or adding analytics, you wouldn’t be alone in questioning the value created by these tools. Business Intelligence vendors promote the benefits and advantages of their tools, highlight the weaknesses of their competitors, but seldom quantify the value they create. You’ve probably read industry trends for business intelligence that promote its value but rarely describe how the value is created. In this paper I provide a methodology for deriving how business intelligence creates value based on business needs.

This paper clarifies the underlying goals and objectives of Business Intelligence to help identify its value, then builds on this definition and outlines a methodology for uncovering the value it creates. This methodology relies on gap analysis and decision support principles for evaluating the value potential for an organization. This methodology thus should (finally) provide managers with the justifications necessary for procuring the right business intelligence solution for creating and delivering the value expected by the business.

Business Intelligence Re-defined

So what is business intelligence and how does it create value? I believe the term “business intelligence” is too vague and imprecise a term that doesn’t adequately describe the activities most people associate with it. Before we can understand its value to business intelligence, both implementers and consumers must agree upon a common definition of the final product and service. Asking various stakeholders about their understanding of the concept can be surprisingly revealing. To some, it represents a methodology for exposing a wealth of data locked up in databases. To others, it’s a reporting tool with “essential” dashboards, ad-hoc reporting capabilities, and analytics. Others consider business intelligence a curse and money pit for delivering overly-detailed reports of highly questionable value. Business Intelligence professionals consider it many things – a report development tool, a metadata development tool, an OLAP cube, a data mart or data warehouse, an Extract-Translate-Load (ETL) tool, a dashboard, a set of business rules and business logic, a means for mining and aggregating data, etc. But if business intelligence is to deliver value, every stakeholder of the service first must share a common understanding of its future promise and vision. All must clearly understand the value it provides beyond access to and reporting on vast amounts of data. Let’s start by re-scoping Business Intelligence to be a discipline or major business function much like marketing and finance are disciplines. It isn’t software or a set of tools. Instead, Business Intelligence is a business function with clear goals and a mission: to collect, analyze, evaluate, and disseminate relevant business intelligence information, metrics and status, for assisting leaders and managers in making informed decisions that change behaviors and move the business toward meeting goals, objectives and strategy. I offer a new term, Business Decision Analytics (BDA), to better describe the methodologies, practices, processes, analytics, applications, systems and data associated with a company’s Business Intelligence function.

Valuing Business Decision Analytics

Business Decision Analytics derives value by effectively supporting core value chain and cash flow management business decisions, strategy implementations and technology developments. Challenges faced by corporate managers today include how to maximize output and positive cash flow, minimize costs, improve efficiencies, assure governance, maximize plant, property, and equipment, extend IT investment longevity, and minimize risks. BDA could help you as it currently helps many managers today decide the best courses of action to take. For corporate managers, BDA may drive potentially higher returns on investment by providing insights and actionable metrics necessary for managing the implementation of corporate goals, objectives and strategies. A merchandise analytics department of an on-line retailer reduced the number of required analytic reports

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