Creating Action From Analysis

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As is common with most digital analysts I know, I love solving problems. And while these problems come in many shapes and sizes, there is one problem that is far and above the worst. Inaction. Reports are created, analysis is performed, insights are gleaned, recommendations are made, and yet, nothing happens. Inaction has to be the single most frustrating aspect of a digital analyst's job, and probably every job, for that matter.

Over the years, I created a framework I try to follow for increasing the resulting action(s) I want to see from my analysis. They may not work 100 percent of the time, there will always be challenges to overcome, but after some fine-tuning and ongoing evolution, here are the three aspects that I try to make sure every deliverable possesses, to ensure the greatest chance for success:

1. Answer the true question/objective. With all the data available, it's easy to get distracted when performing analysis and making recommendations. It's also easy for the clients to whom you are delivering this analysis to become distracted with different key performance indicators (KPIs) and various data points that seem relevant to them. By constraining your analysis to those metrics that truly matter and make the most impact for your organization or client, you ensure that your deliverable has a cohesive focus.
2. Provide only what's necessary; be concise. This one is probably the most challenging for me. As mentioned above, the capabilities of capturing and recording data are insane. To help keep the focus on the true objectives of your analysis, make sure the data, charts, and written context provided fully make the case for your recommendations, but nothing more. To help get a better understanding of this, I would recommend reading up on the works of Edward Tufte and Stephen Few. Both are great with the optimization of creating impactful and actionable data visualizations.
3. End with action. No matter your recommendation, always include an expected impact if your recommendations are implemented. Even if the impact presented is a range of values and results, the anticipated impact will help the end client prioritize their efforts and next steps. Without an expected impact, your recommendation will likely make it to the bottom of the pile, if it even makes it there at all.

By incorporating the above items into your analysis projects, hopefully you will see increased levels of action taken from your efforts. Just remember to continually ask yourself:

• Is this relevant to the client/organization objective?
• Can I say this in a clearer or more direct manner (whether through data visualization or written context)?
• If this is implemented, what can the client/organization expect to see in return?

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