Before we establish that we really need Analytics Audit, we need to discern whether Analytics is an Art or Science! Since an artistic work can’t be validated, it can only be appreciated. Whereas a scientific work must be validated, before it is accepted as a truly scientific work!
Let’s evaluate typical Analytics projects approach on the constituents of Scientific Method as described by Wikipedia, to ascertain whether they really qualify to be called scientific projects. On similar lines, for data-mining and other Analytics projects, we have established methodologies like CRISP-DM and SEMMA. KDnuggets website also contains an overview of main methodologies for analytics, data mining, or data science projects.
Comparison of a Typical Analytics Projects Approach with CRISP-DM and Scientific Method is presented in Table 1 below. As seen from Table 1, typical Analytics projects approach pretty much mirrors scientific method. So, we may conclude with fair reasonableness that following this approach should lead to scientifically verifiable results. But, then why would we need Analytics Audit? Well, a little extra effort does not harm! It can provide Analytics Assurance and ensure Analytics projects’ fidelity.
| Table 1 – Comparison of a Typical Analytics Projects Approach with CRISP-DM and Scientific Method | |||
| S. No. | Scientific Method | CRISP-DM Phases | Typical Analytics Projects Approach |
| 1. | Make observations | Data understanding | Harvesting in-house data |
| 2. | Think of interesting questions | Business understanding | Data driven decisions (3D) identification |
| 3. | Formulate hypotheses | Business understanding | Organizational metrics delineation |
| 4. | Develop testable predictions | Data understanding | Metrics of interest prioritization |
| 5. | Gather data to test predictions | Data preparation | Base-lining of priority metrics |
| 6. | Refine, alter, expand, or reject hypotheses | Modeling | Analytical / statistical modeling |
| 7. | Develop general theories | Evaluation | Modeling results evaluation through 3D |
| 8. | Develop general theories | Deployment | Operationalization of modeling outcomes |
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