Data validation and governance is of primary importance in any data project. It can make or destroy any project’s usability, acceptance and value derivation. I have seen many data projects not reach their full usability or value because of inadequate or incorrect importance given to validation and governance in the design, build and sustain processes of the projects.  However, the importance of reporting and analytics becomes more and more critical due a) proximity to value derivation both in terms of type of usage and content criticality, and b) distance from the data entry points with respect to data modifications, and rules and algorithms written on the raw data entry points.

All reporting and analytics projects have varied levels of identification and acquisition as well as integration and normalization components in them. They seldom rely on raw data without integration and normalization performed. I am not discussing the vanilla reporting projects where an online transactional processing (OLTP) code or query is written on any data base of application. Those projects are just reporting projects where a code is written and tested in the normal software development cycle. My focus is on reporting and analytics projects high in value derivation and benefits where there are substantial efforts in acquisition of data from raw data formats and significant efforts are done on normalization of the data before data is analyzed and decisions made. Figure 2 illustrates a categorization of analytics projects.

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