Knowledge Summary: The Next Decade in Digital Transformation

A knowledge summary is a semi-long to a long post that synthesizes positions, concepts, and lessons learned around a topic.  They consist of a mix of primary research with ideas and frameworks I built based on conversations and working sessions.

This knowledge summary will focus on concepts you have to know to succumb to embrace digital transformation in the next decade.

Understanding Digital

I wrote about digital transformation first in 2013.  Then an update later in the year, and finally a call to end the talk in 2015.  And I wish, you don’t know how much, we could do that.

But we can’t.

The problem is that digital has become the new crutch word much like e-anything or i-anything became the crutch letter in the late 1990s.  And we have seen decent value arise from that use: iPhone and eCommerce are the two most recognized iconic words (and concepts).

The purpose of Digital is to replace all that has to do with computer-driven or computer-assisted.  We have had computers since the 1980s in the organization, we cannot say “computer-driven transformation” as that ship has already sailed;  we talk about digital as if it was a magical, mythical approach to changing things.

But it’s not.

At the core, this machine-driven revolution is different from the one 30+ years ago in what is focused on – data.  That’s it, as simple as it can get.  This transformation is about data.

It would be more appropriate to say it was about information (a combination of data, content, and knowledge that enables companies to solve problems better in context), but — as I said before… i-anything was already taken.

Seriously, it’s not about data only, it could never be.

Data is the representation of an event.  What happened to whom or which, when, how, and where – it’s the pieces of information you need to recreate something: a customer signed up for a newsletter (what), they received the newsletter (what, when), they opened three of the five links (what, when, which), they did a search for further information (what, how, when), they bought a product (what, how, when, where), we shipped it (what, when, where, how), they received it (what, when).

That’s all data – every piece of that.

Data does not mean much without the other two parts of information: content and knowledge.

Content is the static information we (or someone else, if we believe in communities) created that describes an entity (product, solution, use cases, manuals, etc.).  For example, the customer got a newsletter with content – information about products, services, discounts, coupons, etc. and acted on that.  The data about the newsletter being distributed and the action remains, but without the content, we would not know why the client acted and we couldn’t track specific actions they took.

And that brings the final piece: knowledge.  Knowledge, if you follow my writings, is wisdom – the applied, contextual, intent-driven use of content.

And that is where the reality of what data can do comes together — if you combine data about events with content related to that event and intent and context in the form of knowledge – what do you have? the right information, at the right time, in the right place personalized for each person, optimized for each situation, and outcome-focused.

In other ways, you have the reason your organization is trying to adopt a business transformation strategy.

Becoming Digital

Now you know what digital is and why it’s the source for business transformation this time around: even though data has been around from the beginning, it’s the leveraging of data mixed with the right content and knowledge, yielding information, that makes it the basis for this new evolution.

How do you use it?

There are two slides that I use to show my clients and people I talk to where / how / why information is to use it.  First, where does information come from?  Big Data — rather Big Noise.

Check it out…

The concept of Big Data is a misnomer: how can something be “data” or be anything if we don’t even know what it is.  If one trillion IoT devices generate measurements, which ones are truly data and which ones are noise? If there are 20 millions tweets in one week mentioning your company name, product name, or someone in the company directly by name, how do you know which ones to reply to directly and which ones to safely ignore? What about corrupt or misspelled or damaged “data”? it’s not data until you structure it, until then is noise – simple.

Although the first reaction is always to store everything and “later we will figure it out” the volumes are becoming too large for that, and the need to use the data (and the speed at which data usability and usefulness decay) increases dramatically every day.  Storing for future use is no longer possible these days.  The next step is to filter the noise and create a signal.  If a plane that uses Rolls Royce engines generates over a terabyte of data per flight, and there are thousands of flights daily worldwide – which piece of “data” should be stored? used? discarded?  Filters make that decision.

Once the filters take their turn and the noise is selected as valuable, then we structure the information.  This is the decision as to what it is: data, content, or knowledge?  This is the moment the noise becomes structured and ready to be acted on.  The concept of digital transformation being about acting on unstructured data is another misnomer – how can something be acted on if we don’t know what it is? where it came from? what is used for?  once we filter the noise (create a signal) then we figure out what it is that we have, structure it, and store it in the proper place (or not, but that’s another post) and act on it.

These actions, the analytics or workflows applied to them, generate an insight – a something we did not know.  These insights are what we use to make decisions, to act, or simply to inform – via dashboards and reports.  These signals are also the components of information that will be used to power algorithms and AI components – and that will become the new filters for Big Noise.  The cycle of optimization based on what we know and what we learned is then complete and we have both insights we didn’t before, and data (and content and knowledge) stored.

Very different from simply capturing everything to figure out later what to do, no?

Being Digital

This section is the second framework I use with clients and in conversations when talking about what it takes to become a digital enterprise.  I must confess, this is not a new model – I first introduced it in 2013 – but have been working and optimizing it since.  Thanks to all of you that helped me along the way.

Once you get past the awesomely-designed majestic use of colors and boxes, you end up with six components you can focus on: four yellow boxes, and two blue boxes.  I know, mind-blowing.

The yellow boxes represent infrastructure, the purview of IT and Architects in your organization.  As your organization becomes more entrenched in the cloud (which by now is commoditized, so I am assuming you are either already there or on your way there) they will notice more and more a need to structure their approach.  The yellow boxes separate the different pieces they need to focus on:

  1. cloud infrastructure, the core components used to run and interconnect everything
  2. legacy access, because I am still proud of the code I wrote in the 1980s in COBOL and to be fair – a large number of you still use it and need it to run your organizations
  3. interface connectivity, because in the age or mobile and IoT there is no longer a requirement but actually a necessity to be device-independent — this is not mobile-first, this is mobile-also
  4. and to accommodate the hype of the day – AI (or advanced analytics, if you read my writings on the topic) with three specific outcomes: optimization, personalization, and automation.

I wrote about this framework before, the link is above and there is an update to it here as well, which is why I am just summarizing it here.  If you need more details read those posts, or contact me.

Once you configure your distributed computing architecture (AKA cloud) to operate in a model similar or comparable to the above, you will end up with two blue boxes – which is where the magic behind digital business transformation happens.

The information blue box is what we were discussing above in the previous section: how to find the right data, content, and knowledge to create the necessary information for every transaction.  The information will come from anywhere: devices, legacy data or applications, or AI and analytics engines.  The role of the information layer is to make sure that all information is considered, and the best selected, when crafting the response to an interaction.

The experience layer is the one where most organizations would love to have control – but they can’t.  The concept of building experiences for customers is archaic and, frankly, dumb.  This is not about understanding customers journeys or planning for them – or anything like that.  This is about understanding that stakeholders (notice it says customers as well employees, partners, and public) will interact with the system on their own terms, according to their expectations, via any channel, at any time, in any way they see fit.  Today they may have more time, tomorrow less – and they would appreciate a quick summary today, and more information tomorrow.  To accommodate these shifting expectations, each experience will be built ad-hoc by the stakeholder – provided they have access to all systems, information, and rules that apply.

This is the essence of being digital: building an infrastructure that allows any stakeholder to interact with any part of your organization, at any time, anyway they want, for anything they need.  if you can do that, you are “living la vida digital”

To get there, that’s what you need to do over the next decade; this is not a simple system purchase and deployment.  It requires extensive changes in all layers of the organization: people, process, and technology.  It also requires new thinking in governance and metrics.  and that — that takes a decade or so to complete.

Your move.

disclaimer: first things first, thanks to Jon Reed for the idea of using crossed-out text.  Since Sameer Patel does not like my parenthetical digressions, I am testing some minor ones using crossed-out text to see if it works.  if it does, all credit belongs to Jon who uses them to great effect (far greater than I could ever do) at Diginomica. I would reluctantly, but understandably, relinquish the use if he asks.  second, I also have to give credit to the kind folks at OpenText who helped me with the first chart a bit during a consulting session in December.  the other people, many, who contributed over time are also very kind and i am profoundly thankful – but they were more motivation and inspiration, OpenText tweaked the slide for me and let me use it.  third, no vendors are mentioned – but y’all know that i work mostly with them, so there’s a chance that some of this stuff shows up in something they do / use and i will gladly take all the credit for that.  ideas are mine, originally and follow-through, so feel free to yell at me for anything i got wrong.  if it works, not me – if it didn’t, me.  comment box is below.  thanks for reading.

7 Replies to “Knowledge Summary: The Next Decade in Digital Transformation”

  1. Help me get past one piece, as I try to wrap my head around the difference between what you have above and DIKW that has been the bronze standard (note, not gold) for a long time. I think about it differently and would place context where you have content and would switch information and knowledge (I like thinking about Knowledge as the wrapper). Not religious for me, so help me to see the light without getting Rick-Rolled


  2. i started at the DIKW model, even used it for a short while – but there are a few things that bother me about it. Same with other models that focused on knowledge or content exclusively. The ones that think that data is simply the digital representation of everything miss the functional aspects of each component. I worked on this for over 12-15 years

    context is what? there is no way to define context in there, i think (nay, i know) that the context is provided by specific elements (data provides context, knowledge provides intent) and i have been using this model for some time with good results.

    its easy to say “context is queen” or king or whatever piece of royalty you want o assign to to, but what does that mean? how do you define context? and intent? i worked a lot with intent when i was at Gartner, trying to define it — same problem, how do users recognize one or the other?

    this model is an evolution, something that shows what each piece means and how to use it. i don’t discard DIKW as a beginning, but as Scott Nelson (former Gartner manager, and the only one that was good at it) says: sacred cows make the best hamburgers.


  3. E,

    Nice post. While I broadly agree that the approach and posture of most organizations related to their attempt to “control” the customer experience (through architecting customer journeys) is outdated and was conceptually flawed from the start, I would simultaneously argue that every stakeholder does not wish to carry the cognitive load necessary to navigate every experience in an ad hoc manner. Most stakeholders love “when it just works”.

    Recommending likely best choices/actions (or automatically automating some steps) adds value for each stakeholder like a concierge or sherpa does in the physical world.

    While, I know you have quite a bit of experience in this domain, the way that it’s written above might convey that journey orchestration, recommendation engines, and contextual content & journeys have no place – which might also be a flawed posture.

    The key might be to recognize how, when, and where to apply each concept and framework to achieve stated outcomes (wisdom!)

    Thanks for moving the conversation forward.


    1. You make a great point, and it needs more than a flippant answer. i have been thinking how to answer and came up with 3-4 diff perspectives, and all require more room that answering this will make justice to.

      will make the answer my next post, should be out early next week since i am taking some time off this weekend to ponder on the role of humans in the future… (or to go out of town, either way :)).

      thanks for reading, promise you that will address your point in detail soon.


  4. On the contrary I am f̶l̶a̶b̶b̶e̶r̶g̶a̶s̶t̶e̶d̶ ̶d̶i̶s̶m̶a̶y̶e̶d̶ ̶l̶i̶t̶i̶g̶i̶o̶u̶s̶ thrilled that you went with the strikethrough format. I think I mentioned on Twitter that I’m glad someone with the savvy and twisted wit to use strikethroughs like this has joined me. Love it!

    – Jon


  5. Thanks for making Digital business clear. Another way to look at digital is through the eyes of a digital oscilloscope. Connect scope to an unknown signal source, all you see is noise. Apply some filters (turn a few knobs) and bam a signal appears. Tune the time scale and bam the signal is recognizable. Connect up the digital signal output to a Fourier transform for signal transformation to control systems. Usually hot rolled steel or an Iron Dome missile defense system (can just as easily be a sales forecast prediction system). Data scientists can jump in and add a machine learning feedback loop, if the loop can learn in real-time and keep a stable signal its behaving with artificial intelligence (coda AI is a big IF).


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