icm2re logo. icm2:re (I Changed My Mind Reviewing Everything) is an 

ongoing web column edited and published by Brunella Longo

This column deals with some aspects of change management processes experienced almost in any industry impacted by the digital revolution: how to select, create, gather, manage, interpret, share data and information either because of internal and usually incremental scope - such learning, educational and re-engineering processes - or because of external forces, like mergers and acquisitions, restructuring goals, new regulations or disruptive technologies.

The title - I Changed My Mind Reviewing Everything - is a tribute to authors and scientists from different disciplinary fields that have illuminated my understanding of intentional change and decision making processes during the last thirty years, explaining how we think - or how we think about the way we think. The logo is a bit of a divertissement, from the latin divertere that means turn in separate ways.

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What is the essence of data science?

About the first rule of any continuous development programme

How to cite this article?
Longo, Brunella (2016). What is the essence of data science? About the first rule of any continuous development programme. icm2re [I Changed my Mind Reviewing Everything ISSN 2059-688X (Print)], 5.1 (January).

How to cite this article?
Longo, Brunella (2016). What is the essence of data science? About the first rule of any continuous development programme. icm2re [I Changed my Mind Reviewing Everything ISSN 2059-688X (Online)], 5.1 (January).

London, 20 May 2016 - Not at all demotivated by the tale of my failure to be accepted as an Executive MBA student (see icm2re 3.4), a customer asked me what in my opinion should be then the essence of a management development initiative on data science and if I could design a short programme accordingly. Oh dear, I thought. But of course the question was absolutely appropriate and legitimate, and I was honoured and delighted to have a small assignment I could invoice for at the end of the month, instead of applying for dozens of improbable job vacancies that are filled, when they are genuine, with young graduates and trainees.

Since many of the employees of the company hold degrees in mathematics, statistics, computer science, physics or engineering, and I was sure they work on data science issues on a daily basis, I asked back, first of all, why they wanted to organise a developmental programme on data science for managers. My customer put on the table various flattering considerations citing my past endeavours, as he had had a look at my encyclopaedic CV on LinkedIn. I said they are long bygone and not pertinent to the design effort required in their specific circumstances. He admitted the idea to involve a consultant came because the Chief Executive asked him “what’s in there for us” - alluding to the data revolution. They both realised they had not yet a technical, content or knowledge problem but a change management issue. He thought I could help find or design an answer.

So we started and successfully concluded a small project in few weeks. I found it so intriguing that at the end of it I came up with a neologism, knowdging, fusion of knowledge management and nudging, I will surely write about in other occasions, and with several other ideas useful to reconsider and update my own past evaluations of social networks analysis and social media.

But, for now, here are my short and long answers to the question “how to find the essence of data science”. The contents of the bespoke continuous professional development programme on the matter remain, of course, very peculiar to my customer requirement and strictly confidential.

The short answer

For a short answer, I said, we can borrow the golden rule of the Royal Electrical and Mechanical Engineers Corps: "above all, keep an eye on the ball“.

In his epilogue to the second volume of the Corps history, published in 1996, Major General Peter J. Girling, then the Director of Electrical and Mechanical Engineering (Army), wrote what this rule consists of: "in this changing world, the Corps may look forward to the future with confidence if it goes on as it has started - engineering professionalism, a lively and flexible approach to new problems, a willingness to change methods, a continued belief in the importance of a thorough clinical knowledge of equipment, a spirit which allows every soldier to go as far as his abilities and ambitions will let him, unhindered by artificial barriers, and, above all, keeping an eye on the ball - the fitness for operations of the Army's equipment."

The long answer

For a long answer, it may be convenient to look at the evolution of the knowledge management (KM) field.

In fact, like KM in the 1990s, data science has recently erupted as an organisational priority any vendor of software or systems should be able to talk about and yet it seems nobody really knows what its core (science) is made for nor what its object (data) clearly consists of.

I made such considerations about KM few years ago and I even tried to market them to librarians, archivists, computer and information scientists, calling for a more integrated and interdisciplinary approach to the whole matter (no surprise I was sent away by pretty much everybody). Knowledge management - I argued - is like a commuter train with communities, teams, groups and single practitioners coming on board at any time from multiple disciplinary backgrounds, may be for just few stops, sometimes making a lot of noise.

All the folks pretend to understand each other because they share some common theoretical foundations established in the 1990s, like the distinction between implicit and explicit knowledge (from Nonaka e Takeuchi). But actually the last is just a vehicle: a very abstract distinction that was very fashionable and fascinating at the time to catalyze attention on soft and organisational aspects of process re-engineering and on the consequences of the micro-electronics revolution.

Arguing about invisible assets and knowledge capital in the 1990s was hard stuff to sell. But thanks to that distinction between implicit and explicit knowledge and other successful ideas such as quality assurance and employees empowerment, I was able to obtain budgets for investments in groupware software and to build sort of pre-historical intranets.

So, jumping on the KM train in the early 90s was for me a simple matter of opportunistic convenience the end of which coincided with the end of the hype cycle. That came when the USA Global Knowledge Economics Council (GKEC), the OECD and other international organisations defined the expression "knowledge management" as a sort of intellectual synonym for just... "management". In other words, we reckoned that knowledge management is a very broad way to refer to skills the information society or the digital economy requires to come to grips with in any practice, process or workflow. Therefore it is quite predictable that KM changes name and object every time that practitioners and academic experts, technocrats or marketers choose and push "special" products or solutions to solve specific data, information and knowledge problems.

I have seen changes in knowledge management roles and functions that, from my point of view, were just different ways to relate to:

We need to recognise that, for the last thirty years, the KM field has delivered semantic bubbles into the public discourse about science, technologies and information.

A number of corporate programmes and public curricula and policies in KM have lasted few years (at their best), quickly superseded by competitors or alternative designs. We see now behavioural economists and other social scientists are entrapped in similar ways to discuss the best ways to engineer the selection, treatment, delivery and creation of knowledge “choices”.

Microelectronics first and then the internet have determined massive unemployment and changes in the workforce since the late 1970s because the mix of skills required to stay competitive has dramatically changed and it continues to change over time following disruptive trajectories nobody seems very good at forecasting. Of course, if you knew universal principles to design learning and educational programmes to lead businesses and people in this crazy digital economy, you could rule the world.

In 1995 the IBM Institute for Knowledge Management addressed this level of the problem (that perhaps we could agree to call epistemological) and changed name in Centre for Action Research in Organisational Complexity (CAROC). Since then, the focus of all the disciplines dealing with information and communication technologies has gradually become more holistic, consumer centered, results oriented, market driven, dispersed into the innumerable streams of the creative industries.

Executives programmes as well as instructional design and learning technologies have encountered even more risks to lose the essential eye-contact with the ball.