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The Analytics Process Isn’t Working. Let’s Fix It

2 minutes

Analytics reports are supposed to follow a well-defined process, yet they often end up all over the place. Pages of analysis bury the real problem without delivering a clear solution. Why does a seemingly structured process fail, and how can it be fixed?

Analytics Between Idealism and Reality

Given sufficient time and resources, the ideal analytics process can produce excellent results. Like consulting reports, customers (decision owners) struggle to judge the quality and value of the work, while analysts (analysis experts) struggle to demonstrate them. As a result, the process is often incomplete with a detrimental effect on the results.

The claim is that the ideal analytics process should produce the best results possible. The decision owner must accept extra time and cost for thorough execution and the analysis expert must carry recommendation risk. The result is that the ideal process is rarely executed properly. The ideal process is as follows: problem definition, data understanding, data preparation, analysis and modeling, validation, interpretation, recommended actions, and monitor and improve.

Worse still, some stages remain incomplete, while others are revisited repeatedly to fix problems and often creating new ones. The result is work that falls far short of what the customer needs and what the analyst is capable of delivering.

The reality is that the ideal process is compromises due to time, cost and risk. The result being that the analysis expert focuses on maximizing output and the decision owner is left with decision risk thus the result is that the process is reduced to producing more data. This realistic process consists of a vague and shifting problem, excessive data gathering and preparation, and no context analysis and modeling all done in parallel, then resulting in insights and recommendations without clear actions.

Rethinking the Analytics Process

The ideal process is therefore not enough. We need to redesign it around the real challenges facing both customers and analysts, so that the work can be completed effectively and produce real solutions.

The goal of a redesigned analytics process is to make it feasible to produce the best results possible. A clear, precise, and easy-to-use problem framework tailored to the desired solution covers problem definition, interpretation, and monitoring and improving. This results in less data being required, the ability to use readily available information, and makes analysis and validation easier and faster which covers data understanding, data preparation, analysis and modeling, and validation. It also provides a reliable way to find the best actions which covers recommended actions.

Advanata embodies this redesigned process. By letting the customer lead, it speeds up execution and keeps the work aligned with actual needs. By giving analysts clearly defined problems to solve, it enables them to do their best work, have greater confidence in their results, and take responsibility for their quality.

The Advanata process embodies this redesigned analytics process concept. Problem definition powered by the business problem framework allows for clear and simple definition that can be performed by the customer which allows for reduced cost and time for execution and increases confidence in the solution. Problem parameterization powered by the inductive approach allows for a problem framework that accelerates analyst work which reduces data requirements and establishes validation mechanisms. Problem solution is powered by the data effectiveness methodology which results in optimal actions being recommended thus allowing for monitoring and easy constrain modification that can be done by the customer. The outcome is a proper analytics process that is feasible with optimal actionable outputs.

Unlocking the True Value of Analytics

Better analytics reports do not come from doing more analysis or following an ideal process that rarely works in practice. They come from structuring the work so that customers define the problems that matter and analysts can solve them effectively. Advanata makes this possible through its decision-owner-driven approach to business decision optimization, turning analysis into actionable results that maximize business effectiveness.

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