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TDWI Checklist Report: Cloud Data Warehousing

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tdwi.org 4 TDWI RESE ARCH T D W I C H E C K L I S T R E P O R T: C LO U D DATA WA R E H O U S I N G S T R A I G H T TA L K THE RISE OF OPERATIONAL DATA STORES 2 For example, a manufacturing company might use a data warehouse to analyze how item production correlates to sales over time across different geographies. However, the factory floor managers might want more up-to-date reporting about the status of the manufacturing and production processes—such as production speed, product quality, and injection of final product into the supply chain. ODSs are valuable as the collection points for data relevant to lines of business, especially when they provide operational personas access to more real-time information for operational reporting and analysis tasks. Organizational platforms supporting business decision making have been part of the information management landscape for decades. The traditional data warehouse environment architecture that effectively jelled in the early 1990s embraces a set of staged processes to extract data from operational or transactional sources, land that data in a staging platform for cleansing and stan - dardization, and perform bulk uploads to the data warehouse platform. However, as data warehouses became established in the enterprise, the complexity of juggling the integration of numerous data sources and growing data volumes increased. The appetite for analytics grew in lockstep with increased data volumes, data variety, and the number of data sources. At the same time, downstream information consumers' expectations for data availability also grew—along with their desire to rapidly access data to support operational reporting demands, especially within independent lines of business. The staged process for data warehouse population can be adapted to support both holistic organizational analyses and line-of-business reporting. A data warehouse ultimately supports strategic analysis and decision making and must provide visibility horizontally across the organization from the perspective of the lines of business and how they interoperate, as well as from a historical context over time. However, as data sets are extracted from individual application systems, they could be made available for operational reporting. This evolved into the concept of the operational data store (ODS)—an interim data platform intended to support the operational aspects of a line of business or a particular business process.

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