After having just published three rather lengthy articles in a series , here is a piece whose size is at the opposite end of the spectrum.
I am often asked to distinguish between data and information. Indeed this happened just the other day as part of LinkedIn discussions relating to some of my recent articles . In the Data and Analytics Dictionary, I offer the following definition of Information:
Here, I will look to be more visual in my definitions, hopefully also embracing the spirit of the time of year. In my opinion, the following image provides a good way to think about the difference between these two related concepts:
Consistent with my Dictionary definition, Information is something you get by organising data based on some knowledge of how it is meant to fit together.
As with most analogies, there are both some interesting ways to extend this and some areas in which it breaks down. In the first column, sometimes not all of the bricks you need are available or the right size (a data quality problem). In the second, you can clearly build a set of Lego bricks  into several different forms. It is to be hoped that data, particularly Financial data, is not massaged to provide more than one meaning.
However, I think the up-side of this simple analogy outweighs its fairly obvious limitations. I offer it to readers as a final thought before the 2017 holiday season commences.
The Anatomy of a Data Function, Parts I, II and III.
The discussions may be viewed here (you need to be a member of LinkedIn to view these).
As is probably already apparent to regular readers of this blog, I take rather a visual approach to both understanding things and communicating them. Seldom will I leave a one-on-one meeting without having scrawled on a sheet of paper to explain my train of thought, or to ensure that I have properly understood what someone else has said; equally I tend to be an avid scribbler on flip-charts or wipe-boards during larger gatherings.
The above path led me to an article on systems-thinking.org entitled Data, Information, Knowledge, and Wisdom, written in 2004 by Gene Bellinger, Durval Castro and Anthony Mills. Returning to the visual theme that I introduced at the start of the article, my eyes were drawn to the following graphic (I have re-drawn this as a larger version was not available on the site, but it remains the work of Messrs Bellinger, Castro and Mills):
Of course I appreciate that systems-thinking.org piece is intended to have a broad applicability. However, to me, this schematic pithily captures the fact that Business Intelligence is not just about technology and cannot be effective in isolation. To live and breath it needs to be part of a broader framework covering the questions that its users need to answer, the actions that they take based on these answers and the iterative learning that occurs in the process.
Again thinking in terms of pictures, the data to wisdom hierarchy outlined by Bellinger et al brings another image to mind, the one appearing below:
In the same way that Natural Selection offers a compelling framework for the phenomenon of Evolution, all-pervasive business intelligence can offer a compelling framework within which an organisation can evolve towards collective wisdom. Of course, in the same way that Evolution does not always imply increased sophistication (just better adaptation to a particular niche), the technological part of business intelligence, in and of itself, does not guarantee an improved organisation. Such an outcome is instead the product of developing an appropriate vision for how the organisation will operate in the future and then working assiduously to get the organisation to embrace this.
I have often spoken about the importance of incorporating BI in an organisation’s DNA. The above analogy brings a different dimension to this metaphor. Both the evolution of species and the evolution of organisations are driven by incremental changes to what makes them tick, but also by occasional great leaps forward; a concept known as punctuated equilibrium in Evolutionary Biology. Introduction of good BI can be such a great leap forward, but hopefully without the connotation of Mao Zedong.
Returning to the original model, Data and Information may have strong technological elements (though the former certainly has more than the latter, see BI implementations are like icebergs), but Knowledge and Wisdom imply a more human angle; even in these days of automated decision-making with the results of analysis fed back into operational systems. This anthropocentric approach, in turn, raises the profile of cultural transformation in business intelligence programmes; something that my experience teaches me is crucial to their success.