Earlier this month, I had the honor of being interviewed by Ajay Ohri on his blog Decision Stats, which is an excellent source of insights on business intelligence and data mining as well as interviews with industry thought leaders and chief evangelists.
One of the questions Ajay asked me during my interview was what methods and habits would I recommend to young analysts just starting in the business intelligence field and part of my response was:
“Don’t be afraid to ask questions or admit when you don’t know the answers. The only difference between a young analyst just starting out and an expert is that the expert has already made and learned from all the mistakes caused by being afraid to ask questions or admitting when you don’t know the answers.”
It is perhaps one of life’s cruelest paradoxes that some lessons simply cannot be taught, but instead have to be learned through the pain of making mistakes. To err is human, but not all humans learn from their errors. In fact, some of us find it extremely difficult to even simply acknowledge when we have made a mistake. This was certainly true for me earlier in my career.
The Wisdom of Crowds
One of my favorite books is …
Earlier this month, I had the honor of being interviewed by Ajay Ohri on his blog Decision Stats, which is an excellent source of insights on business intelligence and data mining as well as interviews with industry thought leaders and chief evangelists.
One of the questions Ajay asked me during my interview was what methods and habits would I recommend to young analysts just starting in the business intelligence field and part of my response was:
“Don’t be afraid to ask questions or admit when you don’t know the answers. The only difference between a young analyst just starting out and an expert is that the expert has already made and learned from all the mistakes caused by being afraid to ask questions or admitting when you don’t know the answers.”
It is perhaps one of life’s cruelest paradoxes that some lessons simply cannot be taught, but instead have to be learned through the pain of making mistakes. To err is human, but not all humans learn from their errors. In fact, some of us find it extremely difficult to even simply acknowledge when we have made a mistake. This was certainly true for me earlier in my career.
The Wisdom of Crowds
One of my favorite books is The Wisdom of Crowds by James Surowiecki. Before reading it, I admit that I believed crowds were incapable of wisdom and that the best decisions are based on the expert advice of carefully selected individuals. However, Surowiecki wonderfully elucidates the folly of “chasing the expert” and explains the four conditions that characterize wise crowds: diversity of opinion, independent thinking, decentralization and aggregation. The book is also balanced by examining the conditions (e.g. confirmation bias and groupthink) that can commonly undermine the wisdom of crowds. All and all, it is a wonderful discourse on both collective intelligence and collective ignorance with practical advice on how to achieve the former and avoid the latter.
Chasing the Data Quality Expert
Without question, a data quality expert can be an invaluable member of your team. Often an external consultant, a data quality expert can provide extensive experience and best practices from successful implementations. However, regardless of their experience, even with other companies in your industry, every organization and its data is unique. An expert’s perspective definitely has merit, but their opinions and advice should not be allowed to dominate the decision making process.
“The more power you give a single individual in the face of complexity,” explains Surowiecki, “the more likely it is that bad decisions will get made.” No one person regardless of their experience and expertise can succeed on their own. According to Surowiecki, the best experts “recognize the limits of their own knowledge and of individual decision making.”
“Success is on the far side of failure”
One of the most common obstacles organizations face with data quality initiatives is that many initial attempts end in failure. Some fail because of lofty expectations, unmanaged scope creep, and the unrealistic perspective that data quality problems can be permanently “fixed” by a one-time project as opposed to needing a sustained program. However, regardless of the reason for the failure, it can negatively affect morale and cause employees to resist participating in the next data quality effort.
Although a common best practice is to perform a post-mortem in order to document the lessons learned, sometimes the stigma of failure persuades an organization to either skip the post-mortem or ignore its findings.
However, in the famous words of IBM founder Thomas J. Watson: “Success is on the far side of failure.”
A failed data quality initiative may have been closer to success than you realize. At the very least, there are important lessons to be learned from the mistakes that were made. The sooner you can recognize your mistakes, the sooner you can mitigate their effects and hopefully prevent them from happening again.
The Wisdom of Failure
In one of my other favorite books, How We Decide, Jonah Lehrer explains:
“The brain always learns the same way, accumulating wisdom through error…there are no shortcuts to this painstaking process… becoming an expert just takes time and practice… once you have developed expertise in a particular area… you have made the requisite mistakes.”
Therefore, although it may be true that experience is the path that separates knowledge from wisdom, I have come to realize that the true wisdom of my experience is the wisdom of failure.
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All I Really Need To Know About Data Quality I Learned In Kindergarten
