A best practice, according to Wikipedia, is a “method or technique that has consistently shown results superior to those achieved with other means, and that is used as a benchmark”. This definition exemplifies one of the approaches to defining best practices which usually emphasise a combination of the following aspects:
Further important aspects exist as well. For example, in his 2010 talk The Myth of Best Practices, Diego Piacentini describes Amazon's emphasis on the aspect of business model uniformity by stating: “In our business model everything is equal unless proven with data that it needs to be different”.
With our lives, businesses, and social fabric in constant transformation, it is a key observation that the lifetime and validity of best practices is rapidly diminishing. For example
The insight resulting from these observations is that best practices have to be reformulated and implemented in a way that acknowledges the new, changing, and often real-time context we live in. A transferable example of how this can be accomplished is found in telecommunications technology and Self-Organising Networks (SON). SONs is an automation technology designed to make the planning, configuration, management, optimisation and healing of mobile radio access networks simpler and faster. In operation, mobile network base stations will regularly self-optimise parameters and algorithmic behavior in response to observed network performance and radio conditions. The SON best practice lies in an iterative, data-driven, self-optimising, real-time approach. Diametrically opposite to Amazon's approach, the SON operating model presumes constant change, unless data proves it is not required.
In this new context, a best practice is an iterative execution of a method on a data set, delivering new data sets that are subsequently used to improve the method and refine the initial data set. In other words a best practice is a function of data, method, process, perpetually iterated to create more accurate data, a better method and a more optimised process. More and more, such iterations need to be executed in real-time.
This definition makes clear that
The latter is a frequently neglected yet highly significant aspect: to transfer a best practice from A to B without loss, B must have the ability to execute the iterations with the same level of skill as the originator A. Coming back to the The Myth of Best Practices, this is the reason why Amazon in 2010 decided to own the logistics of the last mile delivery in China, but relied on well-established logistic companies in the US.
The tools to implement best practices according to the new definition are readily available. We previously touched briefly upon those while discussing Big Data, Analytics, Actionable Business Intelligence. By applying Big Data and Analytics methods on accumulated knowledge (e.g. idea & knowledge databases, supply process chains, etc) it becomes possible to enhance existing practices with a feedback and learning loop, that will allow for optimisation at every subsequent iteration.
Here are two long-existing examples that demonstrate how Big Data and Analytics are applied to implement the “new” best practices definition (albeit in a narrower context):
Introducing this way of operating into an organisation is a non-trivial task, as it requires establishing a culture focusing on perpertual optimisation. Carl-Henric Svanberg, Chairman of BP and a former Ericsson CEO, instinctively captured the need for acknowledging perpertual change as he coined the statement “what brought us here, won‘t keep us here”. Based on our experience, and the examples we listed in this article, we affirm his statement.