How to Implement an Analytics Strategy

In my last blog post, I gave an overview of how to create an analytics strategy – a framework for identifying the resources, processes and procedures that help drive your business. This post aims to provide you with some thoughts regarding implementation and execution to help support that strategy.

Strategy Review

I’ll start with a brief review of the approach described in my previous post:

  1. What are the organization’s goals? Create your strategy to support those goals.
  2. Who are the stakeholders? Get their buy-in.
  3. What is your current and desired future state of analytics? Address and rectify gaps.
  4. How will the culture support your strategy? Create an analytics culture at your company.
Get Internal Support

This is different from getting stakeholder buy-in. This step refers to getting support from the teams you’ll need to leverage most when implementing your strategy: analysts, IT and developers.

The need for support from analysts is clear: you need someone to pull, organize and crunch the data. In many cases the analyst will also help you make sense of the data – where it comes from, what nuances may be involved, what other ways you can look at the data. These individuals are the front line for your analytics strategy needs.

Outline Your Reporting Criteria

Here you add more detail to what your analytics outputs will look like. By now you will have already determined the audiences, types and cadence of reporting. Other items in this step include but are not limited to:

  1. Validate, validate, validate. I can’t stress enough the importance of this step. The data itself is the foundation of your program. Make sure it is as sound as possible. Ever hear of “Garbage In, Garbage Out”? This statement applies here.
  2. Establish historic reporting for benchmarking purposes. If you can, pull data for the last year or two and use these as your measures to track against current performance. If it’s not possible to gather data from previous years, simply start anew with your new program. The data will build eventually.
  3. Include competitive intelligence whenever possible. While it’s important to understand and benchmark internally, the competitive landscape can be a source of valuable insights and/or can help set standards for your own performance.
  4. Provide insights. Don’t just dump a bunch of data and reporting in your audience’s laps. What were the findings? What recommendations would you make based on the information you now have? Make sure to state your findings in easily digestible language. This is where the true purpose of an analytics program is evident, reflecting business value and helping support larger organizational decisions.
  5. Implement your ideal analytics state. This step is the execution phase of the analytics future state as referenced in the first blog. This is where you implement your proposed process – where you actually deliver product. Depending on your level of internal support and the number of outputs you have planned, this stage could take days, weeks or even months. That said, start with something small and manageable, and build from there. You don’t need to launch your analytics program tracking everything under the sun for every minute of the day.

It’s also important to remember that analytics plans and processes are fluid. These require vigilance and modifications based on new insights, stakeholder or staff changes, and addition or removal of resources and capabilities.

Hopefully, these two blog posts have helped you organize and execute your analytics strategy. While they’re not meant to be exhaustive resources, I hope that this information has provided the guidelines you need to get you going. For more information about how Jackson Spalding can work with you to develop and implement your analytics process, reach out to us at [email protected]. Happy analyzing!