10,000 (or 10,000 hours) is about expertise. In 2008, Malcolm Gladwell wrote about the “10,000 Hour rule” in his book “Outliers: The Story of Success” (ISBN 978-0-316-01792-3)
This “rule” is based on the work of the renowned psychology professor Dr. K. Anders Ericsson, who specializes in the field of expertise. In the Harvard Business Review, and other journals, Ericsson writes that experts in domains such as medicine, music, chess, and sports, acquire their superior performance through extended deliberate practice. It takes someone with a natural talent, i.e., skill, about 10,000 hours of deliberate practice to reach a truly superior level of expertise.
Illustrated by this graphic, we may define expertise the following way.
If knowledge is represented by the understanding of principles and theory in a given domain; and skill represents the ability to carry out tasks in that domain, based on knowledge; and experience demonstrates a track record of carrying out those tasks, generally many times; then expertise is the knowledge and skill to execute effectively and efficiently, with a track record of repeated success.
It is easily argued that the 10,000 rule, or principle, extends to practitioner’s expertise in the domain of Information Technology. In fact, one of the examples Gladwell cites is that of the technology expertise of a young Bill Gates, of renowned Microsoft fame.
One of the specialized IT areas that this principle certainly applies to today is that of Business Performance Management, Business Intelligence and Analytics. For the sake of brevity, let’s not debate whether this is one area or multiple. Practitioners who have over 10,000 hours of extended deliberate practice in this domain can realistically claim to be experts. By deliberate practice, we don’t mean sitting in meetings talking, we mean doing.
So why is this relevant?
If you are looking for expertise, whether to hire a new employee or to find suitable specialized consulting resources to augment internal staff, then you need to look for appropriate levels of real expertise.
Are you willing to recruit talent with knowledge and skill, but perhaps less experience, who command less of a premium price, and to mentor and train them to gain experience over time thereby building your own experts? Or do you need that high degree of expertise out-of-the-box, hitting the ground running and perhaps mentoring to develop internal expertise, but commanding a premium price?
When it comes to finding consulting skills, there are a couple of well-known (and flawed) procurement models. Perhaps in an effort to avoid bias, government organizations frequently procure using two criteria, namely price and experience. In this case experience is a measure exclusively of quantity. The attempt to determine quality, i.e., successful results, and therefore real expertise is frequently completely ignored.
Some private sector organizations take a different approach. They may want expertise, but may not recognize that what they are seeking commands a premium. They have a model which is a chargeback of IT costs to the business community, and limits what can be paid for consulting based on those internal costs. This implies that if a consultant is required who is more expensive than internal IT resources, then the internal IT organization must bear the cost differential. This completely fails to recognize that if a consultant is required, it is likely because of a specific specialization, combined with the level of superior expertise needed, which is not available from internal resources. This can legitimately command a premium, though a smart organization would want to ensure both coaching and mentoring to ramp up internal expertise, to limit the future use of more expensive consultants.
Yet the world is not standing still. There are things which can be done today with technology that not so long ago were unthinkable and could only be achieved with human expertise. For example, in 1996 and again in 1997, an IBM expert system called Deep Blue beat the then reigning world chess champion, Garry Kasparov. In 2011, another expert system, called IBM Watson, beat the all-time best human competitors in the game of Jeopardy. In 2013, a Big Data text analytics engine discovered relationships on the internet that no human could ever have discovered.
Clearly, all of these examples cite cases where human experts were “replaced” (or beaten) with very expensive technology. As we know from experience, technology becomes commoditized, cheaper and more attainable. Human experts better keep advancing their knowledge and skills if they expect to command a premium and not be replaced by technology at some point in the future.
In the meantime, organizations seeking real expertise in the 10,000 hour class, will pay a premium, and should expect to receive the value and highest quality results that they have paid for. So, do your research on people before you hire an expert.