When people think about a Data Analytics program, they many not fully consider the intricacies that will go into building and maintaining the program long term. To start, leadership must decide what data they will target, how it will be analyzed and how it will be used $($Calzon$,2023).$

Deciding what data to use: Perhaps the most important step, what are we trying to do$?$ If the task is trying to sell more blue widgets, then analyzing website data for all widgets, and neglecting the blue ones to compare to is the wrong path forward. Understanding what is needed and how to focus on that is crucial to moving on to what to do with the data.

How will it be analyzed: Having staff familiar enough with statistical analysis to run the appropriate test is essential to making heads or tails out of the numbers $($Anand$,2022).$ Should a straight regression analysis be used, or are there patterns that develop when a moving average is applied?

How will it be used: Having a plan, and the data is all fine and dandy, but now what to do with it$?$ Is the data going to the sales and marketing team to dive in to understanding how to improve, or is this just going to the CEO to tout at the next board meeting? Putting meaningful data in the hands of those who can do something about it makes all the difference in the world $($Ogungbemi$,2022).$

Keeping a team to successfully navigate the data analytics program is another task all together. People in this sector understand the value they can bring and do not want to be hamstrung in their processes.

Defining a structure: Keeping a defined structure where everyone knows their roles and responsibilities helps everyone understand their contribution and lets them hold everyone accountable $($Ethan$,2023).$

Unbiased modeling: Making sure that the data analytics team is getting accurate and unbiased data so they can do their job is important. Nobody wants to put all their effort into analyzing junk data.

Acting on recommendations: When the data analytics team produces sound recommendations based on the data, it expects the organization to act on those recommendations. Spinning your wheels when you do not feel valued makes for a short tenure at any organization.Reply to: