Knowledge hinders sharing – Part 1

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There is mounting evidence over the years that knowing gets in the way of learning. We will bring out two stories over two posts that confirm our belief. In this post we speak about how knowledge hinders sharing. In 1990, Elizabeth Newton from Stanford University conducted an experiment in psychology. She paired the participants or subjects into teams of two. Each team had a designated tapper and one designated listener. All tappers picked one of 25 best-known songs and would tap out the rhythm Read more [...]

What does it take to be Anti Fragile?

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Nassim Taleb’s book ‘Anti-Fragile: Things That Gain From Disorder’ gave a new term to the lexicon: “anti-fragile”, to describe systems that not only can survive stress, and volatility, but also gain from them, that require them to survive. He goes on to make a distinction between the resilient which has the ability to resist shocks and stay the same; the anti-fragile gets better. This is clearly a powerful idea and we believe that this is going to be a very important concept in the Read more [...]

Mindset Required to Solve Clear Problems

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A few weeks ago we discussed the mindsets required to tackle muddy and fuzzy problems. In this one, we would like to talk about the mindset required while solving Clear Problems.  Clear Problems are not about just having clarity on what needs to be done. It is also about having clarity on what it took to get to a clear problem. To address a Clear Problem, it is more important to have a solid understanding of everything that happened to reach the “clear” stage – the iterations, the failures Read more [...]

Mindset required to solve muddy problems

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Last month, we introduced you to the concept of muddy-fuzzy-clear problem life-cycle. Depending on the life stage of the problem, we argued that analytics practitioners and businesses need to have different mindsets to address these problems. In this post we will focus on the mindset required for addressing muddy problems. Muddy problems are, well, muddy. The problem in itself requires defining and re-defining several times. In this regard, a muddy problem requires slow cooking. Solving muddy Read more [...]

Top Three Big Data Analytics Technology trends to watch for in 2013

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2012 was a watershed year in analytics, as Big Data dominated the media, if not data scientists’ lairs. In 2012, the most forward-thinking companies began to incorporate analytics into their strategy work, rather than using it as a tactical tool or in reactive mode. What will 2013 bring? Which technologies will still be relevant and which will fade into the background? In 2013, look for more and more organizations to embrace analytics as a key driver of strategy for making better decisions and Read more [...]

Muddy-Fuzzy-Clear: Life stages of a business problem

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Business problems evolve. When a business encounters a problem for the first time, very little about the problem is understood. Details about the nature of the problem, scope, approach, solution etc. are all muddy. With time, as we start addressing the problem using heuristics/experience it starts getting a little clearer. We gain a better understanding of the finer details of the problem, but the “right” solution is still fuzzy. Eventually, after multiple iterations of solving the same business Read more [...]

Lessons from Nate Silver’s success in 2012 election

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Not surprisingly, Nate Silver’s  simulation models got it right 100%. And deservedly, he is getting a lot of praise for this success (he can do with some after all the flak he took pre-election). We at Mu Sigma have always believed in some of the core philosophies that made Nate and his fivethirtyeight blog successful. Some of these beliefs are: Methodology before data: We believe in front loading all the thinking. The analytical methodology should be decided based on the business situation Read more [...]

Why we like “I don’t know”

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At some companies, the thought of saying, “I don’t know” in any meeting strikes terror into employees’ hearts. At Mu Sigma, we think “I don’t know” is a good response – because it’s the first step toward finding out. It is assumed – because we hire people who are naturally curious – that they will go ahead and learn from first principles. Read more [...]