Despite efforts to improve effectiveness, many organizations realize their existing application software (ERP, CRM, POS, etc.) does not always live up to the promises associated with better business insight. Why are these systems challenged with providing better business insight? The focus of this discussion is to investigate challenges regarding the sheer physical number of possible theoretical combinations. After reviewing the issues, we can conclude that advanced techniques designed to reveal patterns must be part of a business intelligence framework.
Reporting and Analysis Evolution
Most of the data that supports reporting and analysis comes directly from the database applications. However, for more meaningful results, many organizations have responded by creating focused data marts or large data warehouses in order to view key measures across many different views. These views increase relevance and provide new business insight.
Many reporting and analytical packages provide the capability to graph and chart measures against particular views. For example, those interested in operations would like to see the profitability of a particular asset across different geographies. After reviewing the results, the end user most likely will ask another question and subsequently review those results. The end user formulates ideas and opinions based on this type of interaction. With a manageable number of possibilities, this type session is likely to identify actionable business insight.
However, working with a manageable number of possibilities is not the norm; the opposite is true. The failure to uncover actionable business insight is due to the volume and complexity associated with data sets. The most astute business analyst can ask the best questions, observe the results, and attempt to draw conclusions. Unfortunately, as complexity and interrelationships grow within the data, the number of possibilities grows exponentially. The interrelationships across measures are the key elements that bear fruitful analysis.
Dealing with Possibilities
Cross-tab tables are useful for examining various inter- relationships associated with data. Dimensions refer to how one views the data within the cross-tab table, and each dimension has members. For example, a four member dimension called "geography" consists of four members east, west, north and south. Additionally, a five member dimension named "deal type" consists of swap, option, future, fixed price and physical. Therefore, the total number of combinations that exist between the two dimensions, geography and deal type, equals 20.
The Figure 1 shows how quickly the number of combinations resulting from interrelationships can become unmanageable. The measure of interest is profit and the members of each dimension are A=4, B=5, C=2, D= 8, E=11, F= 14 and G=25. The number of results represents the different combinations of profit available.
Figure 1: How Quickly Combinations can Become Unmanageable
Adding new dimension combinations quickly increases the number of possibilities that the analyst must evaluate. Due to time constraints and other restrictions, even the best analyst must find ways to navigate through the growing number of combinations in order to identify actionable business insight. All combinations cannot be fully understood without an army of analysts or other sophisticated techniques.
Given today’s economic climate, with more organizations struggling to get by with less, adding additional resources dedicated to analysis is not a reasonable option. However, making current analysts more effective is an avenue worth pursuing. This can be achieved by investigating platforms and analytic environments that leverage advanced techniques capable of rapidly scanning through the various combinations and revealing interesting anomalies and patterns that can further explored. Until recently, solutions supporting these requirements have been evolving slowly.
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