I remember a high school lesson on analysis and synthesis – with the teacher emphasising why they should always go hand-in-hand in a complimentary manner. It apparently did not register very deeply in my mind – and for a number of years, I was a bigger fan of analysis than one of synthesis. Higher education in engineering and management pretty much helped me firm up by belief that analysis is the big deal and this is the area I should master. Engineering taught me more about how to break a problem into smaller parts and solve each. It did not teach me with the same vigor on how to put things together to better solve the problem. Same deal with my MBA – I became pretty good at analyzing issues, but when I look back – I don’t think I had the same zeal for putting things together to aim at a better solution.
This craze for analysis must have some how played into my decision to take an active interest in the world of Business Intelligence too. Over a period of time, I got exposed to more and more of the challenges that my clients face. While I had a decent ability to figure out why they were having the problem, and give them advice on how to analyze the issues – I was not equipped with the tools or training on how to use synthesis and put it all back together to give a better solution that gave more value than the sum of solutions to the problems my analysis pointed out.
In real world, the best business brains have the ability to use analysis and synthesis together – and not just analysis alone. These are people who use tools and other people to do the analysis part and come to them with the required information, and then like a master chef – they mix the parts to create an extraordinary dish. However, the fact that this type of people are few in number makes me believe that we have a fundamental issue with how our education, tools and thinking is preparing us for taking on grand challenges.
A primary reason for this is our simplistic view on solving problems. Here are three that come to mind
1. Not all problems have exactly one root cause. But we have been taught to think that there is one such cause. Even if our analysis comes up with 3 causes, we try harder to somehow rank them – many times artificially, till we can defined “The” root cause. And in this process, we lose out on the ability to gain a better solution by understanding the relation between all the causes. When analysts scream that a CEO has to be replaced, or when opposition screams that the President is ineffective – we lose sight of the fact that there are many things that cause issues – and it cannot be all attributed to one person. But since we are tuned to think about the world in a hierarchical fashion – and the CEO or President is visually the top node – we attribute too much to them, whether it is good or bad.
2. Over use of the 80-20 rule can be counterproductive. We almost always find something using analysis along the lines of 80% of revenue comes from 20% of customers. And hence we think if we spend most of time and resources in making these 20% customers happy, then we are in good shape. Well…think again. If you have a large customer base, then 80% of your customers is a large enough number to drag you down in a variety of ways, using the various channels available to them to do so.
3. Analysis is always done assuming certain boundary conditions and assumptions. However, we do not always factor this when we interpret the results. Just by asking the same question in a different way – you can get a different answer. Here is a recent example. We asked a set of stakeholders – “How important is dashboarding/ graphical representation of data to you?” as a part of 10 questions in a survey. When we compiled the results, we found it was one of the least important. Around the same time, some one else had done a similar survey which asked “how often do you use charting and other graphical representations of the data you analyze?”. And guess what – the answer indicated that many of them used it quite regularly. Eventually,after many more discussions with the people who answered the survey, we figured that other questions in the survey had an influence on how the users answered each question.
I still think that analysis is crucial to decision-making – all I want to add is that people should not stop there. They should use the principle of synthesis and take better decisions.