9-Step Model for Data Analysis

Too often, data analysis is an unplanned art, with too many "rabbit warrens" being explored. A disciplined approach is required.

When training analysts how to deliver more value, two topics have proved the most popular. One is training in Socratic questioning techniques, to get to the real business need. But, as many analysts have "fallen into" this line of work, rather than making a conscious education and career choice, few have been trained in methodologies. With the exponential growth of insight analysts, marketing analysts and data scientists, the emphasis appears to be on just coding skills and software mastery. Where this is the case, too often analysis is an unplanned art, with unreliable timescales and too many "rabbit warrens" being explored. It is perhaps for this reason that the other most popular topic is a high-level structure for analysis. I call this approach the 9-step model for analysis. It comprises the following steps: 1. Socratic Questioning: getting to real business need 2. Planning & Design: defining approach and gathering resources 3. Stakeholder Buy-In: getting agreement on what will be delivered 4. Data: ensuring the needed quality data and learning from it 5. Analysis: including exploratory data analysis and hypothesis testing 6. Insight Generation: converging evidence to get to deeper insights 7. Stakeholder Sign-Off: support for or refining recommendations 8. Storytelling & Visualization: capturing hearts and minds for action 9. Influencing for Action: ensuring appropriate action is taken What's your experience of improving the capability of your customer insight team? Have you focused on developing the skills outlined above or other areas? Please do share your tips, too.

Paul Laughlin

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Paul Laughlin

Paul Laughlin is the founder of Laughlin Consultancy, which helps companies generate sustainable value from their customer insight. This includes growing their bottom line, improving customer retention and demonstrating to regulators that they treat customers fairly.


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