Understanding the Analytic Development Lifecycle
As discussed in one of my earlier articles, it is critical to understand the analytic development lifecycle. Your analytics will not last forever, and instead, become obsolete and need to be retired. As you collect more and newer data, you will need to continue maintaining your current analytics while creating new ones. It is essential to understand what you will need to do at each stage of the analytic lifecycle. As seen below, I view the analytic lifecycle as five critical components to development: R&D, Deployment, Testing & Validation, Maintenance, and Retirement. So let’s walk through each element together!

Analytic development lifecycle — image created by the author using LucidChart

Research and Development

Research and development encompass the first few steps in the analytic development lifecycle. After receiving your data, you will spend time looking it over, understanding its structure, and cleaning it. As you go about this process, you need to consider what analytic opportunities you see from the data, any business problems you are aware of, and how they overlap. As you work through developing your problem statement, you can begin to test out analytic concepts and develop your analytic.

This process varies if you are working with existing analytics. For current analytics, you will want to understand the areas that need improvement, methods you will utilize, and any subject matter expertise (SME) you have acquired to help you through these updates. Having followed up conversations with an SME will help you validate if your updates are going in the correct direction or not.

I find the research and development phase of the process to be the most interesting as it is where you can learn the most about your data. I work alongside other data scientists, data engineers, and subject matter experts daily. These individuals help in creating data sets and developing a data dictionary to understand what the data represents. I can then understand how to combine this data with other datasets to build my analyses. I want to make sure I see the larger picture and tell a compelling story before moving further down the process.
['Rose Day']
['Machine Learning', 'Artificial Intelligence', 'Software Development', 'Technology', 'Data Science']