{"id":200,"date":"2018-06-14T19:04:50","date_gmt":"2018-06-14T19:04:50","guid":{"rendered":"https:\/\/pressbooks.ccconline.org\/bus3060\/chapter\/ch11-5\/"},"modified":"2026-02-03T15:51:34","modified_gmt":"2026-02-03T15:51:34","slug":"ch11-5","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ccconline.org\/bus3060\/chapter\/ch11-5\/","title":{"raw":"11.5 Data Warehouses and Data Marts","rendered":"11.5 Data Warehouses and Data Marts"},"content":{"raw":"
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Learning Objectives<\/span><\/p>\r\n\r\n<\/header>\r\n

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After studying this section you should be able to do the following:<\/p>\r\n\r\n

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  1. Understand what data warehouses and data marts are and the purpose they serve.<\/li>\r\n \t
  2. Know the issues that need to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts.<\/li>\r\n<\/ol>\r\n<\/div>\r\n<\/div>\r\n \r\n\r\n<\/div>\r\n

    Since running analytics against transactional data can bog down a system, and since most organizations need to combine and reformat data from multiple sources, firms typically need to create separate data repositories for their reporting and analytics work\u2014a kind of staging area from which to turn that data into information.<\/p>\r\n

    Two terms you\u2019ll hear for these kinds of repositories are data warehouse<\/a><\/span> and data mart<\/a><\/span>. A data warehouse is a set of databases designed to support decision making in an organization. It is structured for fast online queries and exploration. Data warehouses may aggregate enormous amounts of data from many different operational systems.<\/p>\r\n

    A data mart is a database focused on addressing the concerns of a specific problem (e.g., increasing customer retention, improving product quality) or business unit (e.g., marketing, engineering).<\/p>\r\n

    Marts and warehouses may contain huge volumes of data. For example, a firm may not need to keep large amounts of historical point-of-sale or transaction data in its operational systems, but it might want past data in its data mart so that managers can hunt for patterns and trends that occur over time.<\/p>\r\n\r\n

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    Figure 11.2<\/span><\/p>\r\n

    \r\n\"Information\r\n<\/a><\/p>\r\n

    Information systems supporting operations (such as TPS) are typically separate, and \u201cfeed\u201d information systems used for analytics (such as data warehouses and data marts).<\/p>\r\n\r\n<\/div>\r\n

    It\u2019s easy for firms to get seduced by a software vendor\u2019s demonstration showing data at your fingertips, presented in pretty graphs. But as mentioned earlier, getting data in a format that can be used for analytics is hard, complex, and challenging work. Large data warehouses can cost millions and take years to build. Every dollar spent on technology may lead to five to seven more dollars on consulting and other services (King, 2009).<\/p>\r\n

    Most firms will face a trade-off\u2014do we attempt a large-scale integration of the whole firm, or more targeted efforts with quicker payoffs? Firms in fast-moving industries or with particularly complex businesses may struggle to get sweeping projects completed in enough time to reap benefits before business conditions change. Most consultants now advise smaller projects with narrow scope driven by specific business goals (Rigby & Ledingham, 2004; King, 2009).<\/p>\r\n

    Firms can eventually get to a unified data warehouse but it may take time. Even analytics king Wal-Mart is just getting to that point. In 2007, it was reported that Wal-Mart had seven hundred different data marts and hired Hewlett-Packard for help in bringing the systems together to form a more integrated data warehouse (Havenstein, 2007).<\/p>\r\n

    The old saying from the movie Field of Dreams<\/em>, \u201cIf you build it, they will come,\u201d doesn\u2019t hold up well for large-scale data analytics projects. This work should start with a clear vision with business-focused objectives. When senior executives can see objectives illustrated in potential payoff, they\u2019ll be able to champion the effort, and experts agree, having an executive champion is a key success factor. Focusing on business issues will also drive technology choice, with the firm better able to focus on products that best fit its needs.<\/p>\r\n

    Once a firm has business goals and hoped-for payoffs clearly defined, it can address the broader issues needed to design, develop, deploy, and maintain its system1<\/sup>:\/p><\/p>\r\n\r\n