Today’s business environment drives an unprecedented need to understand, analyze, and take action on the ever-increasing mountains of customer and operational data. This coupled with the need to make decisions faster, and within a more complex business environment, is resulting in organizations demanding better business intelligence tools.

A critical step for organizations in constructing their Business Intelligence infrastructure is selecting the tools that will deliver and present information to their business users. This is the last step in a complex value chain that starts with raw source data from many systems, both internal and external, flows through various transformations to validate and cleanse the data, and finally loads into a data mart or data warehouse. If the information-delivery tools in the last step of this value chain are inadequate, then all the effort and expense of the preceding infrastructure is for naught.

Selecting a tool that will deliver useful and valuable information to business users is much more than a technical comparison. One can put much effort in judging the merit of a specific feature or capability among candidates.

However, the real test of merit comes more on the subjective match with business users than the technical features of the tool. Does the tool enable business users to understand complex interactions with customers and the like? Does the tool stimulate the creativity of business users to generate action plans for improving customer profitability...and enable them to monitor the effectiveness of those plans? It is deeper questions like these that should drive your tool
selection.

Visual Analytics

There are three different ways of delivering information to business users within your enterprise.

First, there are reporting tools that summarize and display historical data. The relationships within the data are known and usually obvious. For instance, a report may list the top 25 customers and their total sales for the past month.
Second, there are forecasting tools that analyze trends in historical data and predict future values. Forecasting tools are also based on known relationships within the data (which may not be as obvious) and are useful for planning.

Third, there are exploring tools that probe the unknown relationships among customers, products, stores, and other key business entities. Exploring tools are often referred to as analytic tools or simply analytics. The purpose of analytic tools is to break a complex business issue into smaller and simpler parts, thereby gaining an understanding of the issue and generate insights into the unknown relationships hidden within your data. As compared to reporting tools, analytic tools probe deeper into the casual relationships, such as the seasonal buying habits of high-frequency customers in a particular region.

There are analytic tools, such as data mining and predictive analytics, that rely on sophisticated computation to detect frequently occurring patterns. These tools are useful for experienced analysts who have the skills to configure the analysis runs and interpret the results. These tools are not appropriate for direct use by business users. There is a need for a different type of tool.

Visual analytics rely on a users intrinsic ability to detect patterns visually, thereby being able to see and understand the complex relationships within their business data. Aptly put, seeing the relationship in business data is the basis for managing your business. To the business user, visual analytics is the means for cutting through the fog hiding business reality.

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