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Data Mart vs. Data Warehouse

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Do we need a single, enterprise-wide data warehouse, or are the information-intensive departments' data marts sufficient? This question is an industry debate and a common one for organizations that are considering an investment in an integrated information system. Data marts are often an attractive alternative to the mammoth job of implementing an enterprise-wide data warehouse.

A data warehouse incorporates information about many subject areas--often the entire enterprise--while the data mart focuses on one or more subject areas. The data mart represents only a portion of an enterprise's data--perhaps data related to a business unit or work group. Typically, a data mart's data is targeted to a smaller audience of end users or used to present information on a smaller scope.

The smaller-scale data mart is typically easier to build than the enterprise-wide warehouse; can be quickly implemented; and offers tremendous, fast payback for the users. The downside comes when several department-focused data marts are implemented with no forethought for a future data warehouse that serves the entire enterprise.

What at first may seem like a quick and easy solution can cause a problem rather than solve it. Implementing several data marts to serve as reporting systems for individual departments can lead to data mart anarchy. Danger looms when individual departments select different hardware and software platforms, and the organization neglects to standardize and integrate information. This leaves the information technology (IT) department potentially supporting multiple databases, network operating systems and a variety of OLAP reporting tools.

The ultimate goal with any integrated information system--whether it be a data mart or a data warehouse--is to provide consistent, accurate data about the organization to the users. Department-focused data marts have only the information that group needs. Each department has its own specific uses for a data mart, which often ignore the information needs of other areas.

Having different departments with various data marts also escalates the number of problems and issues for the IT group to resolve. Unlike the enterprise-wide data warehouse, IT cannot manage and maintain these information stores from one central location. And one solution cannot address the myriad of problems that may arise from the data marts.

Despite their potential pitfalls, data marts can pave the way for a large-scale IT investment in data warehousing. The key rests in designing and implementing a scalable technical infrastructure for the data marts that will allow the leveraging of information for an enterprise-wide data warehouse.

A critical component of a scalable infrastructure involves using a standardized technical architecture across all data marts. Like building a data warehouse, the data mart's architecture should be stable, yet flexible. There are several key components that must be in place to ensure this flexibility and stability.

One component is centralized, integrated meta data and consistent definitions. Such consistency will smooth the transition from data marts to data warehouse by making the individual systems compatible. All of the tools used to build the data marts, and eventually the data warehouse, must "speak" to each other. This communication is accomplished by selecting tools that have integrated meta data.

The extraction, transformation and loading (ETL) tools selected for the data mart must transform data into common formats and integrate, match and index information from disparate sources. Using a standard technical platform--including a standard operating system, ETL, meta data management tool and reporting tool--can be an effective way to accomplish this task. Use of data marts with a standard infrastructure can offer unsurpassed business analysis and management capabilities.

Data must ultimately be put into the hands of the people who are responsible for the achievement of business objectives and strategies. Issues to consider in information management are: data load times, synchronization, recovery, summarization levels, method of data security implementation, data distribution, data access and query speed, and ease of maintenance. All of these issues should be addressed when implementing the data mart. If not now, they will have to be considered when an enterprise-wide system is implemented.

With these key components, organizations implementing data marts will be able to scale the technical architectures they put in place today into an enterprise-wide data warehouse to serve the information demands of tomorrow. While many of the issues are technical, the core issue of the data warehouse versus data mart is often political. Can IT deliver the data warehouse fast enough to meet the expectations and needs of the departments that are demanding them? How much does standardization slow down the organization?

IT must wrestle with and overcome the time and standardization issues if they are to build a flexible, expansive, data warehousing architecture that meets the future needs of the organization.

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