For the past five years companies have been developing data marts to allow them to analyze large volumes of data. In most cases, the purpose of these databases was to allow companies to enhance the capabilities of their customer-facing business units. The goal of many of these systems has been to identify, segment and classify the most valuable members within a target group. As companies develop an understanding of their customers, their focus has once again turned inward.

There are several factors that have driven this change. The primary factor is that as companies developed a more intimate understanding of their customers, they began to focus on aligning their internal processes and procedures with customer needs and corporate strategy. The end goal of this introspection is to maximize shareholder value. As a result, many companies are developing analytical systems to measure how effectively their strategies, goals and tactics align with customer needs. In order to track all of this information, companies have begun to develop analytical data marts.

Analytical data marts differ from traditional data marts in the sense that they focus on capturing, monitoring and storing key performance indicators (KPIs) that link strategy with tactics. Instead of storing all of the detail behind the calculations, analytical data marts and warehouses simply store the results of the calculations. In many cases, these data marts are implemented in support of the balanced scorecard so that the KPIs are tracked in a consistent fashion.

Another way to think about the differences is that traditional data marts focus on explaining business issues while analytical data marts focus on tracking and monitoring the company's performance. On the surface it may appear that this is a small distinction, but it has profound implications on the design and implementation of the data mart.

With a traditional data mart, raw data is processed to create atomic level data which can then be summarized to increase performance or support reporting analysis. The goal is to provide answers to business questions. (See Figure 1.)

Figure 1: Traditional Data Mart - Data Sources

With a traditional data mart, the objective is to be able to ask business questions and perform the analysis necessary to provide an explanation. In order to achieve this mission, traditional data marts contain significant numbers of metrics to ensure that they are able to answer the majority of the questions that arise. (See Figure 2.)

Figure 2: Traditional Data Mart - Uses

The goal of analytical data marts, however, is different. Rather than capturing large numbers of metrics, analytical data marts focus on the handful of KPIs that drive the business. The objective is not to provide explanations, but rather to track progress toward goals and targets and provide an integrated picture of where the business stands.

An analytical data mart focuses on capturing information from the areas that impact the business. (See Figure 3.) The objective of the analytical system is to provide a balanced view of the business that transcends the purely financial view and creates direct cause-and-effect relationships between operational performance and strategic plans. The objective is to determine whether the tactical actions are achieving corporate objectives.

Figure 3: Analytical Data Mart - Data Solutions

The primary focus of an analytical data mart is to tie information classes together. By tying these groups of information together, it is possible to identify procedural shortfalls within the organization. By tracking strategic initiatives to concrete actions, the analytical data mart allows businesses to continuously monitor performance.

The objective of the analytical data mart is to present KPIs based on cause-and-effect relationships. By aligning KPIs along areas of performance, businesses are able to focus their efforts and attention on the areas where they will achieve the greatest benefit.

By focusing attention on business drivers, the analytical data mart allows businesses to weight KPIs and derive objective targets for overall performance. By creating cause-and-effect relationships, the analytical data mart is able to measure how effectively the business is meeting its overall objectives. (See Figure 4.)

Figure 4: Analytical Data Mart - Uses

The process for developing analytical data marts differs from developing a traditional data mart in that the question is not what information is needed to make decisions, but what KPIs allow the business to link corporate strategy to tactics. (See Figure 5.)

Figure 5: Analytical Data Mart - Business Areas Supported

The analytical data mart serves as the platform for unifying a company around its core strategic objectives. This framework, while containing an inward focus, has the profound effect of causing businesses to look at the real factors that drive customer and business value. By measuring the progress toward corporate goals, monitoring the implementation of strategic visions and tracking performance enhancements, the business will know whether it is succeeding in achieving its mission.

The conversion from developing traditional data marts to developing analytical data marts is both challenging and rewarding. During the development process, each business unit will want to institute its own series of KPIs to ensure that it achieves the best results. The process of creating an analytical data mart will bring all of these issues to the surface. With perseverance and determination, it will be possible to track, monitor and link KPIs across the business to ensure that the business is able to execute its strategic vision.

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