“If I had only one hour to save the world, I would spend fifty-five minutes defining the problem, and only five minutes finding the solution.” –Albert Einstein
The quote mentioned above is very interesting. Although, it doesn’t mean that one should spend nearly ninety percent of the time defining the problem, the real intention of the above quote is to highlight the importance of defining the problem. In the world of Big Data and Data Analytics, volume and velocity of data are increasing very rapidly. In order to find the right trends or to extract meaningful information from the vast pool of data, it is very important to define the problem an organization or individual is trying to solve with that data.
Are we solving the right problem?
There is no golden rule or formula to define the problem correctly every single time. However, there are a number of approaches, which can be used to identify and define the problem. Define challenge(s) faced at the atomic level. In order to reach to the atomic or root level of each of the challenges, answer these key questions:
- Is it an old problem?
- What was done earlier to solve the problem?
- Is the problem impacting or impacted by any other problem?
- Is it SOLVABLE?
- Is the problem urgent or important or both? Identify the appropriate quadrant for the problem:
As understandable from the above matrix, if a problem is important and urgent then it can be considered as a critical problem. Identifying, defining and solving critical problems could lead to immediate and effective gains for an organization.
Over the past few years organizations have increased their IT budget, especially for data analysis. As we all know, Technology is a support service in any organization and it helps improve efficiency and margins. Defining the problem correctly not only helps in analyzing the data appropriately but also makes a significant difference in terms of dollar value by focusing technology and analytics in the right direction. On the other side, if a problem is not defined correctly or if the technology team is focusing on incorrect problem, it could lead to increased expenses at the cost of opportunity lost to competitors.