How to turn data into knowledge

Martyn Jones

Zurich, Switzerland – 3^rd August 2017

If anyone can turn data into knowledge then that person is me.

Let me explain.

I am a data architecture and management professional. For more than three decades I have been acquiring knowledge and experience in the design and delivery of effective data and solutions architectures for a wide range of projects and in a wide range of (mainly large global or regional) enterprise clients.

Therefore I think I can reasonably presume to have built up quite a good personal body of knowledge when it comes to applied data architecture and management.

As well as being a professional in the management and architecture of data, I have also considerable knowledge and experience in the areas of information management, artificial intelligence and knowledge management (structured intellectual capital).

So, what about turning data or information into knowledge?

Well, a lot of people claim that data can be turned into information. I am more or less comfortable with this idea, because even if it’s a skill-set that not everyone can master – requiring as it does a unique blend of technical, management and business skills – it is achievable and to satisfactory levels.

But what about turning information into knowledge?

Well, to put it bluntly, this is nonsense.

If anyone tells you they can turn information or data into knowledge then they really don’t have a clue about what they are talking about.

If anyone can turn data into knowledge, it is me.

And I can’t. I’ve never met anyone who can. I suspect that this is what there is.

So, the next time someone tells you they can turn data into knowledge, send them to me. Or better still, get them to read this piece and afterwards get them to affirm that their data alchemy skills are as good as they claim them to be.

Data and information are tangibles we can deal with. Sometimes these tangibles are complex. Many times they can be simplified, without losing essential facets.

Knowledge is quite another matter, to say nothing of wisdom.

The best we can do after ‘data and information’ is to use those facets to complement structured intellectual capital (a facet of knowledge and records management, but not knowledge itself).

Now, whilst I’m on my soap box, I would like to address another facet of data and information management. Data analytics.

Not only is information not knowledge, but the naïve idea that we can accurately interpret the past just by looking at some data (small, fast or big data) is just that. Indeed, the analysis of available data in order to understand what has happened in the past is inherently risky, not to say patchy, and is frequently to do with how we organise our ignorance, biased interpretation and justification of the past, rather than presenting an accurate, verifiable and trustworthy picture of the past, one that will help us to accurately predict the future.

Data, of all kinds, represents at best a sketchy view of the past. Yes, this is the best we have in many cases. But we should never, ever, overstate the importance or value of this data and information.

We should learn to temper our arrogance and ignorance, and accept that we can do much, whilst recognising the many limitations to our current knowledge and experience. We must learn to be wise, whilst avoiding the dopey claim that there is a value chain that goes from symbols, data, information, knowledge to eventually arrive at wisdom. It may look neat on a PowerPoint slide, but it’s ultimately bogus, supercilious and wrong.

And before I go. Just to reiterate. You simply cannot turn data into knowledge. This is simply idle and baseless opinion and speculation, and such claims should be pointed out and laughed at, at every opportunity.

Many thanks for reading.

If you’d like to know more about any of these aspects, including but not confined to the architecture and management of data and information, knowledge management (structured intellectual capital), or discuss the meaning of wisdom, then please let me know.

You may also be interested in the following blog posts at Good Strat:

5 Simple Tips to Help You Survive the Big Data Bullshit Revolution – https://goodstrat.com/2017/07/02/5-simple-tips-to-help-you-survive-the-big-data-bullshit-revolution/

Consider this: The ten key dimensions of Applied Business Knowledge and AI – https://goodstrat.com/2017/06/22/consider-this-the-ten-key-dimensions-of-applied-business-knowledge-and-ai/

Become an Instant Big Data Rock Star with 10 Insider Tips from the Top – https://goodstrat.com/2017/05/28/become-an-instant-big-data-rock-star-with-10-insider-tips-from-the-top/

Top 10 Amazing Big Data Gurus That All Amazing Big Data Gurus Should Know – https://goodstrat.com/2017/05/25/top-10-amazing-big-data-gurus-that-all-amazing-big-data-gurus-should-know/

How to turn internal user data into a massive liability – Zéro pièce – https://goodstrat.com/2017/05/18/how-to-turn-internal-user-data-into-a-massive-liability-zero-piece/

TEAM 2.0 Total Eminence Analytical Mapper – The highest level architecture – https://goodstrat.com/2017/05/13/team-2-0-total-eminence-analytical-mapper-the-highest-level-architecture/

AI and Big Data: A pig’s breakfast –

https://goodstrat.com/2017/05/10/ai-and-big-data-a-pigs-breakfast/

Data-Less Apps: Revolutionary IT –

https://goodstrat.com/2017/05/09/data-less-apps-revolutionary-it/

BIG DATA GURUS: Trifling little fibbers? –