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The resulting data silos create more problems than they solve and increase the need for further DI.
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In the short term, this approach may create reporting applications, but it lessens business productivity with the time lost on reconciliation and debating the numbers costs more because of overlapping and redundant activities and poses too much business risk due to inconsistent, inaccurate and incomplete data. Independent data marts address information analysis but at the expense of not integrating data and creating silos. But now that we have self-service BI, it’s no longer viable to have data discovery and data visualization tools accessing the EDW. Historically, when IT was responsible for creating all reports, this was acceptable because they could navigate the labyrinth of a DW schema. ĮDW-only architecture addresses DI, but makes it extremely difficult for business people to use it for information analysis.Each of these architectures is too focused on only one of the two data management categories: Although they provide reporting, they are not likely to provide the five information C’s (consistent, clean, comprehensive, conformed, and current). The history lesson earlier in this chapter covered the EDW and independent data marts. Data is later subsetted into small dimensional models as needed for specific users and is often structured to specifically support the needs of a particular class of data analysis, such as sales volumes and profitability. The data in the integration layer is then de-normalized into a dimensionalized model and stored in an enterprise presentation layer of the warehouse. Because most DW/BI designers suspect that duplicate information stored within a database inevitably allows data discrepancies to occur, most CIF integration layers are highly normalized because the normalization process leads to tables that make such redundancy impossible. In the CIF model, the data stored in the integration layer should be a “single version of the truth” within the company. From there, the information is subsetted out to departmental data marts, delivering the specific columns and rows needed by each one. As popularly understood, a CIF gathers data from sources and transforms it into a repository in the integration layer of the reference architecture. The corporate information factory (CIF) is an enterprise data warehouse that follows a high-level data flow architecture advocated by Bill Inmon and Claudia Imhoff. Ralph Hughes MA, PMP, CSM, in Agile Data Warehousing for the Enterprise, 2016 Corporate Information Factory