There are lots of great applications of different levels of sophistication such as Cognos, Crystal Reports, Excel Pivot Tables, Qliktech, and many others that help analyze internal business intelligence. However, one of the challenges in data mining this business intelligence is properly reporting on the multiple representations of the same company or contact that are in an organization’s disparate database systems. There could be duplicate records, records that are part of the same company, but have different contacts, different locations for the same organization, and other combinations. If these multiple representations of the same company or contact are counted as being more than “one”, counts will be erroneous due to over stating the number of locations and contacts rendering the business intelligence reports inaccurate.
One of the very first benefits of any customer data integration project is the ability to analyze data from disparate data systems. Typically, a company will want to perform analysis based on information in their CRM system, which contains customers and prospects, and their financial systems. For example, a common analysis question is how much revenue is received from a certain organization. This seemingly simple question can become quite complicated due to multiple records in your database systems with the same organization, but different locations, as well as subsidiaries and divisions that have different names. Customer data integration can solve this issue by developing a numbering system that places Common IDs on all the location and contact records that are part of the same organization across multiple databases. Once you have a Common ID, reports can be run that accurately count the multiple representations of the same organization and contact as “one”. Once data is matched and linked with a numbering system of Common IDs, an organization can perform analysis immediately even if there has not been any data clean-up.
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