How do you accurately define problems before data analysis?

To accurately define problems before data analysis, I start by identifying the specific goals of the analysis and understanding the decision or improvement that needs to be supported. I gather relevant information and insights from stakeholders to ensure a comprehensive context and define the scope to focus on relevant data while clarifying what is outside the purview of the analysis. I then formulate a clear, specific, and measurable problem statement. Additionally, I verify any assumptions related to the data or problem context and clearly set objectives to outline expected outcomes and measures of success.

I agree that the very first step is to be clear in what the problem statement is and what you need to achieve. But according to me in my experience before any other steps are taken its important that you as a leader ensure your team understands it as well. (They need to know how a problem and its solution impacts any process). When your team works towards the solution they take the learning as well ensuring the problem doesn’t occur in the future.

Do not use conjunctions, you are adding additional problems. Problem statements should be singular. Avoiding this will save many hours of sifting through non relevant information.

Strategic Data Sourcing: The road to effective problem-solving begins with precise data selection. Be rigorous in aligning your data sources with the core problem statement. Prioritize data quality, ethical handling, and real-time relevance. This focused approach paves the way for sharper analysis and more accurate insights.

As mentioned above, start with clearly defining the problem you want to solve. Be specific about what you want to achieve and the question you want to answer. A well-defined problem statement sets the direction for your analysis.

Remember your 5-why is not necessarily a singular issue. Each why can have multiple causes, each of which have to be investigated. Your fishbone may morph into a fault tree. Be sure when when you have identified a cause, you can clearly show a therefore (this happened, and therefore this was the failure mode).

Gain a deep understanding of the context surrounding the problem. Consider your business objectives, stakeholder involvement, and any constraints or limitations. This understanding will help you frame your analysis appropriately. Determine variables or factors that influence the problem. These variables could be related to customer behavior, market trends, operational processes, or any other relevant aspects. Identifying key variables helps you focus your data collection efforts.

Reductio ad absurdum may occur if the context is not syllogistically maintained. Be careful how the priors are defined – your problem statement hinges on the correct use of "thus", "because", and "therefore".

Undoubtedly, the problems should be broken down further because it becomes easy to identify the root cause. It further helps in suggesting better solutions to mitigate the issues.

Data analysis is no doubt a powerful tool for finding solutions. Define problems before diving into the data.Avoid wastage of time, opportunity, resources or misleading data.. Problems should be precise. Find root cause of the problem. Use new techniques to identify the problem. There should be specific measurements achievable, time ,bound for improving the revenue satisfaction of the customers.

Beware, you might have problem criteria which need variables beyond what you have. Exhaust all ad hoc analyses before approaching the need to enhance your data feature set. Without that, adding new data or augmenting data alone won't suffice. The previous step and this one can mix into agile sprint-like time boxes. If, for whatever reason, your data cannot capture your requirements once, go through at least one more iteration before moving on.

Analysts have the uncanny ability to turn meaningless numbers and words to actionable insights. But analyzing data requires that the analysis and even the tools you do and use is the most appropriate given the data. Is it more appropriate to do descriptive? diagnostic? predictive? prescriptive? It all boils down to what you want the client/target audience needs to see and know.

Stories are misleading. Ask your client to get an independent analyst to verify your claims. Don't rely on pitches and narratives which are not backed by data.

Communicating the results is probably the most important part of this process. Of course, clearly understanding the problem and doing quality analysis is key to get to this point, but this is where the impact can be felt. If you can tell someone else it shows you really understand it. Whoever the audience is, it’s crucial that they see that you truly understand the root cause of the problem, and that you found the solution. If they feel whatever your presenting truly solves the problem the buy in will be there and all this work will impact the business.

Be sure to interview and include the employees actually performing the task where the problem originated. They are often the best sensors/sources of information you have. They know how the process runs! (E.g. how it looks, sounds, smells, feels and maybe even tastes!)

There are two major points which got missed - 1. Is/Is Not - Which clearly segregates the issue with non-issue. 2. Defining the scope. Without scope, one will be running endlessly and may result in waste of time.