Data Warehousing is a copy of data that resides for later query, retrieval and subsequent analysis, according to an example definition of data warehousing given by industry expert Ralph Kimball in his book The Data Warehouse Toolkit. The warehouse is designed to support analysis of data which is usually transactional in nature, and its flexible structure reflects the needs of its specific user community. The results of the data analysis is frequently used to monitor the effectiveness of key operational tasks within a business without interfering with live, operational systems. Here are some recognized benefits of using a data warehouse system, the features common to most data warehouses and some issues to consider when supporting data warehouse development efforts.
Data Warehouse Common Features
One of the common features of most data warehouses is the flexible way queries are conducted by subject categories. Data analysts who support business areas like supply chain management, sales and marketing can quickly find the data that they need to detect trends and risks. For instance, a data analyst can query a data warehouse for all the people in a certain demographic who purchased a particular product. This group can be compared with other demographic groups to confirm decisions made about a company’s target market. As a result, directors of marketing departments could opt to focus advertising campaigns on different groups to increase sales.
Data and business intelligence analysts can quickly gather data from a number of sources for analysis projects that require integrated reports when using data warehouses. For example, business intelligence analysts may require a customized report that includes input from a company’s financial management and inventory management systems. The results of the report may show the funding requirements to sustain the inventory of a particular product at a certain location for a specified period of time.
The data that is queried using a data warehouse is often used to feed a variety of statistical models. While the data within the warehouse is a static copy of transactional data, the computations used during statistical modeling can give way to different iterations of the originally queried data. An audit trail function that describes where the original data was sourced can add greater depth to data analysis reports.
Benefits of Using a Data Warehouse
Using a data warehouse is a highly efficient way of gaining post transactional data for the purpose of analysis and continuous operational improvement. These databases can be designed for simple queries or complex ones that extend to various source systems like a company’s enterprise resource planning system. Business intelligence analysts are able to identify patterns that a company can exploit to gain a competitive advantage by offering special promotions of certain products or services to particular customer groups for example.
Issues Encountered During Data Warehouse Design and Development
Taking on a data warehouse development project can be a daunting task for a development team because of the great potential for both scope and requirements creep. This is why these development efforts usually follow an iterative process that heavily involves the user community. The common issues that savvy project managers overcome while architecting data warehouses are ensuring that the warehouse produces data query results that are compatible with common analysis tools like the ubiquitous spreadsheet as well as training users on how to read the data drawn from the warehouse. For instance, a data analyst might be very good at performing the right computations and data modeling, but a data warehouse may give them data in a format that they do not readily recognize. As part of that user training, developers often include a lexicon or data directory that helps further define the data drawn from the warehouse.
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Data analysts and business intelligence professionals consider data warehouses highly beneficial for their analytical and reporting activities. However, the design and development of data warehousing systems can be as complex as the data models and reports that receive the queried data generated by business intelligence analysts.