Write 3 pages with APA style on A Logic-Driven Analytics Process. From your past and current experience, which approach might work better for a business analytics project, and why?

This depends on the project and the maturity of the audience/stakeholders. In an environment where the KPIs are not already established and the data is not well known, it might be appropriate to start with a logic-driven process to focus the process on the most impactful issues. This keeps the noise to a minimum and keeps a project smaller in scope while not creating distractions. A data-driven approach can be added as a second step in this case. In a more sophisticated environment where basic KPIs are established, there is an opportunity to find the hidden levers in a process by using a data-driven process to provide deeper insight. Again, these choices are not universal and depend on the maturity of the audience and company, the quality, availability, and knowledge of the data and the appetite of the organization for a complex project

– The choice of an analytic model to use does indeed depend on many factors, including the nature of the project and the environment. It is worth noting that both logic-driven and data-driven models can co-exist and complement each other. In my opinion, a data-driven approach is more suited to a situation where the data is not known since a logic-driven approach requires an intimate understanding and anticipation of the data before a model can be constructed.

3. List one question about logic-driven analytics projects that you still have.

What would an effect hybrid first analytics project look like for an organization that has not approached true analytics in the past and what elements from each methodology would you apply? Assume the organization is relatively mature (in the life cycle) but a novice in deep analytics and has relatively clean and accessible data as well as well educated users.

Second Peer Review

1. What are some advantages and challenges of using a logic-driven analytics process rather than follow a data-driven analytics process?

The main advantage I can see using logic-driven analytics (as one of the requirements) is having such deep knowledge of the company to drive such a model. Such knowledge and experience should provide a good guideline and model for the company to follow. advantage. On the contrary, establishing such a model could take time to get the process and established through to maturity as the level of experience needed does not happen overnight. Staying current with all the variables from business systems and relationships could be challenging, disadvantage.

– A logic-driven analytics project requires a deep understanding of the entire processes that define a particular business. This requires a lot of time and study. In some businesses where variables could keep on changing, for example, due to new business strategies and policies, this also poses a problem to the analyst since this would require modifications to the analytics model.

2. From your past and current experience, which approach might work better for a business analytics project, and why?

I believe that Data-Driven would work best in a project. Being able to compile data from different sources for a business analytics project would seem relatively easier to establish and get incorporated into the business sooner rather than later.

– I believe that both data-driven approaches and logic-driven models are beneficial in their own unique ways. Depending on the situation, one may be preferred over the other and in some cases, they can complement each other.

3. List one question about logic-driven analytics projects that you still have.

Given the investment in time, resources, and people, what are the advantages of a Logic-driven analytics implementation in the way of business assumptions and problem-solving over other types of BA implementations?

– I think the main advantage of a logic-driven analytics implementation is flexibility. This approach is easily scalable to incorporate new factors, and this makes it suitable for what-if-analysis.