1. Challenges in transforming business questions
Welcome! In this video, we will discover the main challenges encountered during the translation process.
2. The importance of the translation process
The translation process can help the analytics team to identify the data that is needed to answer the question and to choose the best analytical solution. An analytical solution that is focused, actionable, and relevant to the business problem and provides insights that ultimately bring business value.
In addition, it helps to facilitate communication and collaboration between the business and the analytics teams. It can ensure that everyone is on the same page and working towards a common goal. This ultimately leads to greater trust and appreciation between the business and the analytics teams.
3. The challenges of the translation process
Even though the translation process sounds straightforward, in reality, it presents several challenges. Let's consider the following scenario. A business team raises a request to the analytics team without providing enough detail about the business problem. The analytics team responds quickly, conducts the analysis, and delivers the results. However, the analytical solution provided doesn’t solve the business problem and the solution is never used by the business. This is a typical scenario in the analytics world and it is caused by several reasons.
4. Interpreting the business problem
The first reason or challenge, is interpreting the business problem.
The questions formulated by the business teams often are given in business terms and are provided with little discussion and not enough information.
In an ideal world, both teams exchange a series of questions and clarifications during this process to clearly understand what the business team is trying to achieve. This includes understanding the business goals, the target audience, and any constraints or limitations that may be relevant. Unfortunately, this doesn’t happen often.
5. Interpreting the business problem
Sometimes the fast-paced business environment which comes with tight deadlines to deliver the analysis can be the main factor. Another reason is that often analysts don’t want to bother the business with too many details. As a result, the translation of the request to an analytical question and the selection of the solution which follows leaves room for significant misinterpretation.
6. Forming relevant analytical questions
The second challenge appears in forming the analytical question. While business teams often have a clear understanding of their strategic goals, they are often not clear about how data-driven analytics methods can help achieve those goals and formulate appropriate analytical questions or hypotheses.
This is, to a great extent, due to the large conceptual distance between business strategies, and processes on the one hand, and the implementation of analytics solutions in terms of data and algorithms on the other hand.
7. Forming relevant analytical questions
Similarly, analytics teams are often focused in their local view, deep in the data and algorithms, and struggle to form the necessary analytical questions. They tend to focus on the solution, for example, clustering or classification from the beginning. In summary, both sides speak different languages with regard to their goals and processes.
In addition, the data that is needed to answer the analytical question may not be available, or may not be of sufficient quality to provide reliable insights. This would affect the analytical questions that should be formed.
8. Choosing the right analytical solution
A well-formed analytics question can give clear guidance on what analytics solution can be used. However, in complex questions, there may be various analytical solutions that could potentially be used, and it can be difficult to choose the one that is most appropriate for the data and the business question.
9. Bridging the gap
Both business and analytics teams should work towards bridging this gap. Due to these challenges, we need a set of best practices that will enable the efficient translation of business problems into analytical questions and the selection of the right solutions that will make a business impact. This is what we will be covering in the following chapter. I hope you are looking forward to it!
10. Let's practice!
Now, let's put these concepts into practice.