Planning data analytics projects for business outcomes

In this article, we discuss a high-level framework to streamline business analytics project planning. Using the four-step approach presented here will allow companies to make informed decisions about committing time and resources to data mining projects using a comprehensive cost-benefit analysis.

The areas where companies struggle are in defining the specific business problem, allocating resources, evaluating the outcomes from a business perspective, and then operationalizing the modeling process recommendations.

Successful implementations require that expected outcomes be clearly defined, resources earmarked, ballpark delivery estimates published, and risks captured in advance. We identify a four-step approach to translate this strategic objective into viable, tactical delivery assignments:

Translate strategic objectives into specific problem statements

Conduct a high-level cost/benefit analysis of options

Define SMART (Specific, Measurable, Actionable, Realistic, Timebound) business analytics goals where possible

Define global practices, tools, roles, and responsibilities

Actual data integration, sampling, data preparation, model development, and model testing activities are deferred to the implementation phase. All these steps follow their own delivery method, but unlike the planning framework, that method is strikingly different in terms of focus, approach, and level of detail involved.

When applied properly, the planning framework presented in the article results in a robust repeatable set of steps for translating high-level business objectives into specific project initiatives along with ballpoint estimates for time, cost, and resource requirements.

A best practice guide to planning data mining projects. The article presents a simple and robust process for business leaders and IT managers.

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