Problem structuring a Data Analytics project is really critical for a project’s success. Understanding where your project is in terms of complexity and its importance, helps estimate the time and effort needed for the project.
In a nutshell, the time you put into a project depends on both
- Importance of the project (Strategic/Financial value to the business)
- Complexity of the problem
There are three ways to look into problem complexity when I evaluate a project. Have to admit, I was really excited to see them written in the book “Techniques of Structured Problem Solving” by Arthur B. VanGundy, Jr. which was this weekend’s read for me. Those three types of problems are:
- Well Structured: This is where all the information needed to solve the problem is available and we have confidence in the data. The team working on the project would also know how to close the gap between the current situation and the ideal end goal for solving this problem. This is normally a routine problem or a process that happens often.
- Example: Creating a simple BOT to automate a straight forward monthly journal entry in Finance
- Semi-Structured: This is when there is just about enough information available to solve the problem. The current situation might not be clear or the destination is a bit blurry for the team. For a semi-structured problem, there is a link to a similar process or model. This link is not straight forward or ideal but there is a kind of structure.
- Example: Which products should be targeted for which customers in different regions.
- Ill-Structured: This is when there is so little to no information available to solve the problem. The ambiguity in this case is massive and it is very complex. Critical Problem Solving Techniques (CPS) are usually ideal for those kind of problems.
- Example: Political or economic unprecedented time. Arab Spring, Covid, new regulations.
There are other questions and techniques I use when approaching a Data Analytics projects and structuring it. Thought to share this as a start for other problem structuring articles and hear your thoughts and experience.
What about you? How do you start approaching your analytics projects? What factors do you take into account?
Looking forward to hearing your thoughts and experience!