Why We Need To Expand The Big Data Questions Gene Pool
In the September 2013 article “What Businesses Need to Understand About Big (and Small) Data”, for ArCompany, Danny Brown invokes Maslow’s Hammer: “If you have a hammer in hand, you eventually start to see a nail.”
Abraham Maslow’s actual quote is perhaps even more telling than the statement now attributed to him: “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.”
This raises two issues in one brilliant sentence:
We tend to mold our available tools to the problem in hand, even if they are not essentially those that might be best suited for the task
We tend to begin searching for answers with an end point in mind, almost invariably fashioning questions that will lead to that answer.
Add one more important element to this:
We tend to be led in the questions we ask, by the current prevailing “wisdom”.
In the article “Why Experts Always Seem To Get It Wrong” for his ever thought provoking Digital Tonto blog, Greg Satell talks of Richard Feynman, an eminent physicist who, after World War II, entered a debate that was raging amongst his peers around “the decay of some obscure subatomic particles”.
That his work in Quantum Mechanics earned him a share of the Nobel Prize for Physics in 1965 speaks to his undoubted genius. But perhaps the most important lesson that all of us can learn from is that he first discovered that the questions his peers were asking made no sense and then stepped back to find that the original paper around which the debate was raging, was itself flawed, so he discarded it and embarked on finding his own solutions.
It turned out that everyone had presumed the original basis for the discussion to be correct and used that as the foundation for their own studies.
Which brings me back to the points made and questions asked in two of my earlier pieces for Curatti: Why Thought Leaders Need Provocateurs and Good Communication Is Not Just About The Words You Use.
People are talking to each other within niches. Either they are not questioning the findings of the leaders within their chosen subject or those leaders are ignoring their questions. But how many dead ends have scientists been led towards by their esteemed predecessors and why should anyone presume that no current prevailing wisdom is flawed? Surely it is inevitable that some of it is?
How can we be expected to find the flaws in current theories when the basic fundamentals governing them are seen as the domain of the field experts and anyone from the outside who dares question them might be deemed an upstart?
This is not remotely a new phenomenon, by the way. How many theories regarding the birth of the universe were discarded before the Big Bang Theory disproved them all? Brilliant scientists, for centuries, based opinions on flawed science, along the way cruelly dismissing those who challenged them.
Simply put, no one group of people can form a complete picture of anything, as no one group of people has a panoramic wisdom of experience.
So when marketers talk only to marketers, and Google+ experts talk only to Google+ experts; when brilliant Harvard alumni talk only with fellow Ivy Leaguers and Data Scientists look only within their own ranks, you can be absolutely sure that aspects of the bigger picture are being missed.
To apply this to the world of Big Data, remember that this field is new and:
- There is no such thing as a Big Data expert. At all costs, avoid anyone proclaiming to be one.
- There is no one path that will provide all of the answers
- There is no one person or group of people who can ask all of the right questions
- The importance of outside experiences being used to introduce new paths of investigation cannot be minimised.
- NEVER embark on a Big Data analysis with an end goal in mind that is anything other than the full truth depicted by the numbers.
- Even the most naïve questions can provoke further questions or thoughts in others, so all must be encouraged to ask
- Form question committees encompassing several competencies from both inside and outside of your current organisation or circles
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Data Eyes: http://bezzina.cc/atmtrg/big-data
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