Barnaby would like to thank Glyn Heatley of Palladium Group for his contribution to this column.
A primary objective of any analytic architecture is business enablement - the ability to provide business users with access to quality information that can be used to drive decision-making. The analytic architecture, in the simplest terms, helps turn operational data into information that is accessible in a usable format and delivered at the right time to the correct audience.
The business is physically enabled with tools and technologies provisioned through the analytical platform, and there are four core processes that provide a critical link between IT processes and the physical architecture.
The analytic platform includes implementation and the processes surrounding planning, analysis and reporting tools both at an enterprise and departmental level. The data management area covers the centralization, consolidation and historical representation of an organization's data assets. Data integration ensures the consistent application of business rules and definitions. IT enablement covers the people and processes that are needed to support the analytic architecture, including the selection, assignment and organization of people to align with the architecture. Figure 1 depicts what I consider an example of best-practice physical architecture.
Analytic applications. This area is depicted to the left of the user and is represented by the suite of example applications that support performance management processes and the typical reporting and analytics that correspond to business enablement. Analytic applications include such tools as multidimensional technologies, ad hoc reporting, batch reporting, drill-down and drill-through analytics that directly support the user. This is a critical layer of the architecture that provisions the data in the data warehouse to usable information for the business.
Data management. This area covers the centralization and consolidation of data from the enterprise. Data often resides in a multitude of sources throughout the business. The centralization of this information is depicted by the box in the center of the diagram and typically contains components such as staging, operational data stores, the central repository itself (what is most often considered the data warehouse layer) and dependent department-specific subsets of data that are optimized for access and represented as data marts.
Data integration. The integration area is depicted in the physical diagram as the box below the enterprise data warehouse, supporting it with extract, transform and load (ETL), master data and metadata management. Data integration, for the most part, occurs throughout the analytical information architecture, from source systems through to the provisioning of data into the analytical applications layer.
IT enablement. Represented behind the entire analytical information architecture, the alignment of human capital with best-practice processes is critical to the success of any analytical information architecture.
While most organizations have some of the necessary components of an analytic architecture, most do not have a best-practice analytic architecture that fully enables business users to make the informed decisions necessary to proactively and better manage their business. However, by conceptually connecting the core processes to the physical architecture, you can understand how to evaluate a given company's current state. By identifying the opportunities in technology, human capital and process that are candidates for improvement, you will ultimately allow the organization to obtain best practices and reap rewards through the enablement of the business.
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