The failure of many data science projects could be prevented in the beginning. This is because the problem is not clearly defined. Albert Einstein fully recognized this issue and there are a few reasons why this is so important.
First, everyone in the organization must have the same understanding of the problem. If you can achieve this, it is much easier to get a shared understanding of the complexity of the problem. Next, you must understand and agree on the impact of the problem and the solution. Then, you can assess whether the solution is aligned with your business strategy. Once this happens, your organization can determine the resources and required level of support.
5 AREAS TO ADDRESS
ONE – The problem across the organization.
TWO – The impact of the problem and solution.
THREE – Whether the solution is aligned with the business strategy.
FOUR – The arrangement of resources and support within the organization.
FIVE – The complexity of the problem.
Defining a clear problem statement is not a task. It is a process. The first step is to explore the current situation to get a high-level understanding of the problem and its impact. This is then followed by a deeper examination to explain the problem.
Before thinking about a solution, it is important to assess whether there is a deeper, underlying problem. Only then can you start thinking about writing a problem statement.
Remember, the solution must be aligned to the business strategy, clearly articulating the desired benefits for the organization. Before writing the problem statement, it is important to contextualize the problem. This step has a backward- and forward-looking perspective. You must look back at the lessons learned from your previous approaches. You must also look forward at any possible constraints that might exist when implementing a solution.
By asking the right questions, you can arrive at a clear and agreed problem statement and your desired outcome. In real-life, organizations often make mistakes during this process, adversely affecting your chance of success. The most common mistakes are linked to a lack of common understanding of the problem. This can be caused by thinking about the solution too early, not identifying the deep-lying problems, or not understanding or describing the problem adequately.
Problem statement issues can also arise if the relevant domain experts are not involved in the process. It is also important to link this work to the wider business, aligning the problem with your business strategy and ensuring buy-in from stakeholders.
5 KEY SUCCESS FACTORS
With a good understanding of the particular business challenge, we can prepare for a data science project. Here are 5 success factors to consider: