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Many years ago, industry pundits began pointing out the need to marry data analysis tools with operational systems to maximize your enterprise's competitiveness. In response, many companies were created to provide these analysis tools. This gave rise to the industry known as business intelligence. A few years ago, these same pundits began saying you need more than a tool – you need an analytics platform to build sophisticated analytics solutions. Seemingly overnight, every data analysis tool vendor's marketing materials spoke about its products as analytics platforms. Pundits have taken the next step. Now, they say, the real answer lies in providing analytic applications. Almost every vendor now claims to provide analytic applications.

You can't blame the vendors for talking about their products. However, we are left with crucial questions: What are the components of an analytic application? How should such a system be developed and executed? Is it advantageous to choose a prepackaged solution over a custom design/build/integrate model?

Cornerstones of an Analytic Application To accelerate the availability of actionable business insight that transforms gains in operational efficiency into effective return on investment (ROI), organizations need analytical applications. As the old saying goes, "If you can't measure it, you can't manage it." However, the implementation, integration and testing of every component necessary to build an analytic applications architecture is a complex, expensive and inherently high-risk strategy. Introducing analytical capability is not as simple as merely installing a query or reporting tool for an operational system.

Analytical information must span all processes and systems, not simply the billing, resource planning, sales or other systems. However, many organizations struggle to achieve this level of integration because they have developed their businesses around silos of operational information ­ products, distribution channels, billing – and few have optimized their customer-facing business processes. Organizations need to step beyond the fragmented, incomplete information contained within their operational silos and build a broader understanding of their businesses that incorporates both historically aggregated and real-time data.

To overcome these challenges, organizations must implement a prebuilt analytic application – one that has built-in functionality tailored to the needs of the business; one that seamlessly and quickly integrates with the company's existing data and systems infrastructure; and one that delivers up-to-date analytical information in the right format, at the right moment, to everybody in the organization, not just a few power users.

View Across Multiple Sources What are the building blocks of analytic applications? Clean data, drawn from a multitude of sources, and delivered in an easily computable format is the preliminary cornerstone. To do this, organizations need a data warehouse. To ensure that it addresses core business issues, the data warehouse must be carefully designed to support specific subjects useful to business users.

Unless the data warehouse is small and simple, designing and managing the data extraction, transformation and load (ETL) process from the operational system will require a powerful and flexible ETL capability.

However, the database structures supporting these operational applications are often complex. It is not unusual for the databases in these source systems to have several thousand tables. To extract the necessary information requires an in-depth understanding of the data stored within each table, as well as knowledge of how all the tables relate to each other.

Linking Real-Time and Historical Data Although the data warehouse provides a source of unified and aggregated historical information, in today's business environment it is vital to augment historical data with real-time data taken from specific tables in various operational systems.