So now I want to draw this to your attention, the underlying story. I was recently invited to join the Future Shapers as a contributor and I was delighted to be accepted as a future shapers contributor. this is my profile link.

There are some strong reasons to add my voice to this group so I wanted to share this with you here on my main posting site

Normally I would not try to merge my posting site with others unless I have some growing level of involvement, contribution or strong identification with. Well, this is one of those but I first wanted to wait before I publicize it here, as the official launch of a funding project kicked off late last week in Madrid,(link) that radically gives it a really different meaning, one to draw to your attention as it is radically different.

So let me explain why, so I have provided the outlines of the story below in their words

Not only do I identify with its position but when you begin to explore the other contributors you can see the value for all innovators. Let one of the founders tell the story.

All I am doing it is being a conduit to this story as I believe it does have a real future value and quite frankly, a real market needs to cut through so much “clutter” that does not add a body of knowledge to advance our innovation understanding.  It often bewilders us and actually stops us actually progressing with conflicting messages, half stories or incomplete understandings. We need different ways to ‘raise the bar.’ Much of this poor thinking on innovation holds back building a real innovation movement, one that becomes the core of any new value-adding activity within our Enterprises.

So the thinking, rational and emerging business proposition

A recent thought piece by @rcopland73  one of the founders entitled “From the Rock Market to the Stock Market” is an introduction to how  the future shapers platform is set to raise their game to the next level around innovation thought pieces, returning the value back from a growing stock of fresh innovation knowledge contributors to provide others committed to understand and make innovation work, a better return of their invested time.

Richards article sets off a raw nerve for me. Does it for you? In his words:

“In the past, I (Richard) have written about our founding principle being ‘Next Generation Organisations deserve Next Generation guidance’ and our community of Future Shapers sole aim of helping businesses and other organizations to shape and create innovation-led futures. We also believe in practicing what we preach and are boldly re-imagining the digital provision of thought leadership and essentially digital news journalism around innovation; how it is produced and delivered, how it is funded and most importantly, how it can serve macro, global and diverse communities, not advertisers.

A value intent gained from offering knowledge

“The growing, unmet demand for high-quality, trustworthy and value-adding thought leadership in innovation has manifested in a significant business opportunity. Having already provided 2-years of innovation thought leadership that has reached tens of thousands of readers we have a fantastic plan for revenue growth and will be one of the first media companies in the Innovation Thought Leadership domain to seize this rare moment to reinvent an industry.

Fewer stories of importance are told or heard. Less and less independent cutting-edge thought leadership is created. The Future Shapers and our innovative platform intend to change this”.

Growth will be driven by millennials and senior executive technologists, our core audience.

“We have identified a target audience of early adopters — influential and engaged executives and agents of change who are deeply passionate about innovation and are increasingly able and willing to pay for quality trustworthy thought leadership.

The genesis of what we are going to do is putting content on a blockchain, and having the connectivity for peer-to-peer transactions – via a digital currency such as Bitcoin or a smart contract such as Ethereum – allows complete transparency and automation of execution, as well as direct payments to copyright holders”

Become a Founding Member of The Future Shapers

“We are now looking to take our platform to the next level. Our goal; to build the largest innovation thought leadership reader base and be considered as the go-to-place for innovation insight. As such, we’re extending our digital platform to enable the re-correlation of knowledge to value. We plan to develop an Ethereum smart contract to enable and support our pay as you read model. We passionately believe that single article sales are the future of online media and we are asking for your help and contributions to assist us in bringing it to life.

We’re asking you, our technologists and agents of change, to fund this new model of innovation thought leadership. Not to donate so a few more blogs can be written about a hot topic or two, but rather to invest in a long-term solution to the provision of well researched, consumable, value-adding thought leadership.

Just as we believe that great thought leadership is now needed like never before, we believe that media that exists to serve our community of readers via a value-adding product should be worth paying for. So, help us build the next generation of media platforms by becoming a Founding Member of The Future Shapers”.

Future platforms for building next-generation advice

“We believe next-generation organizations need next-generation advice supported by a next-generation platform. We are focused on the goal of returning value back to knowledge and as part of our plans, we will be crowdfunding with Crowdcube over the coming weeks.

We passionately believe we have a compelling solution that will disrupt digital media and we are asking for your help and contributions to assist us in bringing it to life. If you are interested and would like to know more, please contact us at info@thefutureshapers.com“

From my perspective, I will certainly plan to write further for them and clearly, this knowledge, value, innovation and the future, is just one of my thought pieces bubbling away in the back of my mind to add further support to this initiative.

I certainly hope this future shaper does take off and delivers as the founders want. It is one that holds a promise that will build innovation knowledge sharing in a new different way, something I strongly identify with. Get in touch with them and find out more on how you can assist them in their objectives.

The post Return Value Back to Knowledge first appeared on Building Your Innovation & Ecosystem Intelligence.

Knowledge Graphs have a real potential to become highly valuable, topical and relevant. If only we can get them prised out of the engineer, data scientists, or software experts hands.

We simply should so we can get this concept fully out into the real world, that of applying as solutions to real client problems, it would really help. I get tired of hearing about “use cases”, where concepts like KG often get caught up in, that never-ending validation.

Is this validation simply because it does not work, it is too much hard work delivering the promise within the concept? Or the approach has too much complexity around it and needs massive resources to undertake?

KG needs a real resource momentum and a determination to break through uncertainty. Its huge value should drive it, and caution should be modified and lets go out and validate it, in the real world.

If any of these “constraints” are the case, then we do need to “hack this” differently, as Knowledge Graphs has what I see an incredible potential, as an application solution that should be deemed as far too important to keep under wraps. We need to instill a sense of urgency into this. Why, well read on.

Here I want to give a deeper explanation of knowledge graphs- a ‘potted’ history and future view. So, my attempt here is to give Knowledge Graphs a more ”layman” context so we can begin to see how this “Knowledge Graph” concept can become really important to recognize, support and apply in our lives.

It will equally become a significant industrial application as well, as increasingly be part of our everyday working life in related searches as it is today by our personal use of Google or other search engines that have Knowledge Graphs increasingly sitting behind them. It might become the next big “buzz”, I recently heard being as topical as “Artifical Intelligence” as a prediction for use in an industrial application. As big a buzz- wow.

It is once we make all the connects in the knowledge that is out there, waiting to form new relationships, we unleash our connected understanding in completely new ways.

Knowledge Graphs help us to form essential context and that has been often missing or simply the poor cousin to content. Context is king, not content!

Struggling to get out of the technical jargon, so be ready and have patience.

Most of what I have read and researched gives detailed explanations of how they work and throws around all the different jargon, protocols and such, as the need to understand the important semantic technologies, concepts, and methods like RDF, URI, SPARQL, SKOS, and OWL. That is fine but we need to turn Knowledge Graphs into the compelling story I think it is but it always have to be provided in layman’s terms, otherwise, it becomes a great idea that struggles to get its importance across, to those that are the eventual decision makers, or those that have to make it work.

As we all know, the biggest buzz on the block today is “Artificial Intelligence”, well it is within this Knowledge Graphs we have a large part of its foundation. AI is getting to a point where it will drive the next wave of technology disruption. Building AI application requires Context. Knowledge Graphs provide the context.

If we don’t have increasing context we will never realize our existing human or artificial intelligence as it constantly fails to connect-up all the parts, in any search or relating knowledge. We see the Web as increasingly the point of knowledge or referral point. That works to a point but all our internal organization data is not on the web, it still sits in silos.

Google, a leader in AI introduced in 2012 “Knowledge Graphs”. As I have heard it explained they moved from links or strings to turn them more into “things”. This now allows us to have far more multiple options and it is the graph database behind this that is providing the structure, relationships and detailed information about the topic you are wanting to find out more. It is providing more options and choices of where to go in your next step, partly as it has been set up to connect to search more on “things” or concepts.

I wanted to put in here Googles view of a layman’s guide, which I can only dare you to explore. It left me feeling totally inadequate as a real layman. Go on, beat yourself up on this but come back here as this is a real layman’s guide.

Knowledge Graphs capture relationships between people, processes, applications, data, things and their relationships. The more the connections are connected the more context you can be provided to make more informed choices. Knowledge Graphs feed off more related data to give increasing contextual information.

Machine learning works at understanding dependencies so emerging from any learning you get a growing sense of intent, objective and those important aspects that you are wanting to find out more upon, to build out even further. The move from just “stringing data together” to build a picture of ‘things’ you get greater contextual knowledge.

Today we have always struggled with where our knowledge lies

It often stays ‘resident’ with the worker, scientist or locked into more traditional enterprise applications, there for one job but not recognized as connected to other jobs you might want to do. The querying of data still has limitations without significant rewriting of new code to go and undertake a search. We need to synthesize, amalgamate, classify, coordinate, organize data, information, and knowledge in multiple “different” ways to look deeper into alternatives and new choices.  Our decisions are becoming more reliant on complexity and we need to organize our data differently to manage this. Knowledge Graphs provide part of the solution.

Within my researching around Knowledge Graphs, I came across the approach by Thomson Reuters and their Knowledge Graph feed is where they are applying key identifiers to make the connections you might miss by not having a structured Metadata that builds a richer content. I think this visual (shown below) of theirs helps begin to frame where Knowledge Graphs can go for building related documents to extract, classify and tag metadata. Metadata is data about data. It describes the underlying characteristics of the data by keywords describing the content and structural information. Metadata is critical to building Knowledge Graphs.

Go to Thompson Reuters intelligent tagging page where they outline how turns large amounts of unstructured data can become into a precise advantage, to tag the people, places, facts, and events in your content to increase its value, accessibility, and interoperability.

We are in need of building semantic relationships constantly for today’s connected world.

For example, multiple documents have within them that critical semantic relationship between the keywords, phrases, and concepts. As we can group these the relationships can potentially expose new information about their interrelationship, you might not have seen or been aware of, without making that critical connecting up. We can form deeper meaning or even gain new meaning, besides gaining a more comprehensive meaning, from forming this relationship.

The semantic knowledge graphs become the backbone of a new architecture that can enable entity-centric (things) to offer views on information, data, products, suppliers, employees, locations and research topics. Knowledge can be retrieved between these various connected objects and they convert previously unorganized data into useful informing information to determine a new action, or insight, or awareness.

The world-wide-web needs different machine learning methods and the application of new algorithms is helping knowledge graphing to catch up. We see Microsoft, Google, Amazon, along with a host of other “search” providers are all working on bringing semantic knowledge graphs to us. Knowledge Graphing has been transforming our searches on the web, now it needs to transform our daily lives, inside organizations and for us personally. Go check out Googles Knowledge Graphs.

Late last year (November 2017)  Amazon announced its Neptune a fully-managed graph database service with the objective of building and running applications that work with highly connected data sets. This database engine is optimizing and storing billions of relationships and querying the graph with millisecond latency. It is offered in the cloud, very scalable and works for open graph API’s.

My interest spiked last December with a visit to Siemens R&D center

I wrote a post “As we enter 2018 we will need Knowledge Graphs” in December 2017, from that brief visit to Siemens where I saw an early conceptual, prototype, Knowledge Graph demonstration. It turned a light bulb on for me and I saw the future of “connected innovation”. Everything we all do is based on bringing data, information and turning this into knowledge. It is connecting this all up we have the greater potential for seeing new knowledge possibilities. We begin to question current or existing knowledge as we gain greater connections and new innovation is often born from this.

Grasping the industrial application of Knowledge Graphs

The industrial application of Knowledge Graphs will build the significant “domains of meaning” that is presently isolated in silo’s, caught up in single ownership, or trapped in known locations like R&D, Production, Marketing that are not linked. Then you have the actual media that is “trapped”  in PDF, word, manuals, guidelines, all operating in islands of understanding. Without a common thread of data accessibility, we suffer from a lack of no integrated view, we limit our search functions and we continue to build inefficient workflows.

It is this lack of connecting all of this up really is placing demands on organizations that manage knowledge today, often very inefficiently. They constantly are turning to limited experts who often hold a selected knowledge in their head. Yet often these “experts” fail to see the “rich” possibilities in connecting-up all aspects of knowledge, as they simply do not have all the experiences and practical understanding of the very different connections and possibilities. Domain experts do.

The promise of Knowledge Graphs allows for the collected data and the connections to provide insight, influence decisions and set direction. The more you “see” all the connections and relationships the better these become.

Nearly every enterprise has a huge amount of data hidden in textual form (documents and files) and it is estimated 90% of this enterprise data is unstructured but if properly organized, it could create better customer experience, greater efficiencies, bring better products based on increased insight to market and constantly look to reduce inefficiencies and costs across the enterprise value chain.

We are told Artificial Intelligence is meant to be a panacea to solve all this current ‘disconnects’.  No, not without solutions like Knowledge Graphs alongside them, well actually underpinning AI. As we digitally transform and improve the IoT through cognitive/ AI solutions we will automate and personalize communications. It is recognizing the semantic technologies part that will deliver cognitive solutions that have the real value.

This value can be built in very different ways than today. It can have the incredible value of being driven by domain experts and actual business users, as they make and use their knowledge to make increased the connections, not an IT professional, unfamiliar on the relationships or lacking deep domain knowledge. That’s part of the promise of Knowledge Graphs, built by those that know, not by ones who think they know! Understanding relationships are down often to the experience you have in the domain of that knowledge

Domain experts are the new influencers of tomorrow, not media influencers as we see today. Content needs context.

As I have outlined early Knowledge Graphs underpin Artificial as well as Human Intelligence.

It is the ‘enriching of data’ by building descriptive aspects, forming and applying context, building solutions that are interoperative, able to be queried and searched that can give a very different granularity.

As we build concepts (things, again) these are units of meaning that can then link to other concepts, thus creating contextual frameworks that open-up to new possibilities. Context informs us of richer meaning. It is the building of conceptual networks both in us as humans and in our learning capabilities of computer systems give us fresh knowledge bases.

In the past, we have attempted to create taxonomies and ontologies with one arm tied behind our back. They were excessive in resources to build but with the understanding of building these through Knowledge Graphs and being increasing enhanced by machine-learning algorithms, coupled with software solutions specific to the knowledge building task,  we are getting closer to knowledge-driven reasoning, based on having a wider, connected understanding.

The ability to have greater intelligence at your fingertips holds the promise of improved ways to find, reuse, reconfigure and adapt relevant information. It can form the basis of the creation phase, providing suggestions in connections for new content.

So Knowledge Graphs build the domain knowledge by making explicit the concepts and relationships of entities (things). They are the backbone of cognitive and semantic solutions. They ‘advance search, personalization, and recommendations, based on greater connected knowledge. They turn unstructured data into structural ones that offer more connected capability.

Let’s return to some basic stuff to know about how Knowledge Graphs connect.

This is if you have not read Googles laymen’s guide mentioned above- without going into the real nitty-gritty details of Knowledge Graphs.

As brief an understanding as I, as a layman can give.  We are wanting to link data. Something I still have to get my head around is RDF’s – So far I think these are binary relationships (subject, predicate, and object, SPO’s) where subject and object are entities and a predicate is a relationship between the two. So are you with me so far?

Drawing from one paper I read “A Review of Relational Machine Learning for Knowledge Graphs, written by Messrs Nickel, Murphy, Tresp, and Gabrilovich in 2015, that gave a good example as shown above and the emerging relationships below.

It is the existence of a triple SPO indicates an existing fact. It is when we combine multiple SPO’s we form multiple graphs. Here it is nodes that represent entities (all subjects and objects) and directed edges represent relationships and where they ‘point’ builds the knowledge graph. Each SPO is a (stand-alone) fact. So we begin to build a Knowledge Graph as the above example shows.

Let me give you an outline of benefits of why Knowledge Graphs – seen by Siemens

This might help lift the understanding around Knowledge Graphs. Why Knowledge Graphs can become the “next big thing” to understand and relate too for industrial application. You connect people and their knowledge in different domains, the knowledge sharing, and the platform sharing. The value builds from greater connections and relationships.

Then the way Siemens see knowledge – as a service –  that works within their Mindsphere platform

The feeding in of understanding, into any platform like Mindsphere, improves perception, reasoning, and decision-making. You get a more “intelligible domain model” of something that might be complex, that many people (partners) need to understand and can increasingly relate too. More people connect and build deeper relationships with shared knowledge. More on this in a further post, sometime in the near future.

Why I see Knowledge Graphs as important

For industrial applications why do I see these Knowledge Graphs as important? They help in decision potential, in decision making, influencing to improve collective knowledge or signaling a greater potential can all be greater in possibility. Now that has the real power of combining knowledge graphs, extracted from related data, applying learning algorithms to end up with the outcome of Augmented Intelligence. This KG approach has the potential of being the knowledge that can lead to unlocking problems, and exploring new potential in design, solutions or prompting new actions.

If I have not lost you at this point, Knowledge Graphs move us from those islands of data, held in silo’s, we can begin to build through knowledge digitalization domain-specific knowledge, and through the building and increasing these through automated structures we can begin to discover new understanding behind knowledge as it connects up.

It is from these connected knowledge linkages we can build a learning memory and extract expertise to become the basis of our collective intelligence. We can discover, learn and build from this ‘connecting all our knowledge up’ to see new possibilities, and that is where Knowledge graphs offer, for me, the most exciting possibilities for innovation, a new discovery.

They can dramatically improve our domain knowledge, improve our common sense and discovery, by linking the relevant context and through this decent labeling of this, we can then manage and interpret far more complex schemas. Up to now knowledge has been kept “closed down” and used fairly inefficiently, parceled out sparingly, so it more than often constrains greater understanding. We have allowed it to form, ossify and become rigid and static, of limited use. It struggles to be reused or recombined into new insights, as we lack the ability to access islands of knowledge and form the relationships.

Knowledge calls for a constant re-working so we can be re-applied in a new context. It is knowledge coupled with insights that lead to new innovation. Knowledge Graphs sound tantalizing, will they be the next big thing?

• In my research and reviews on Knowledge Graphs, I found some useful references. Specifically IDC in some of their white papers on Semantic Technologies to give a deeper background understanding

The post The Arrival and Potential of Knowledge Graphs into Our World first appeared on Building Your Innovation & Ecosystem Intelligence.

Overhaul though, I was a little disappointed, as it lacked the real leadership insights you come to expect, strong personalities did not seem to shine through this year from the speakers and panelists. They gave fragmented insights for the future, mostly seemed to be retrospective, caught in the present, or simply trying to catch up.

I certainly felt the WEF theme for this meeting, of “Creating a Shared Future in a Fractured World” showed through, actually more re-affirmed as fractured. We seem all to be still working on what the future will look like, as it is in danger of being fractured even more before it coalesces around new directions and order.

Some suggest we should allow the future to just simply unfold but I don’t share this view, our future does need to be shaped in so many ways but in what ways and what to allow to “simply evolve” is a very complex question and we are not getting many cohesive answers. So, it continues where it continues, based on individual perspective. The future is never easy to map out but you would expect more answers than questions

Yet what this forum provided, was in its bringing me back to refocus on the Fourth Industrial Revolution. It helped deliver a far sharper focus on its impact, potential, and scope beyond just manufacturing. Now, this was the major ‘bright spot’ of future collaborative potential for us all. Also the growing concensus around the skills and future of work

There was a number of sessions held at Davos, relating to this 4IR. More importantly, a release of a number of reports or white papers on the growing impact of this revolution. Dr. Schwab, the Founder and Executive Chairman of the WEF also released his second book on the subject “Shaping the Fourth Industrial Revolution”

To considerably help us all, the WEF have been providing their transformation maps, that fit within the excitement of my discovering knowledge maps recently, from my Siemens visit, in December

The two transformation maps on this fourth industrial revolution shown below, really do a great job of capturing the huge potential of this movement. These clarify the ‘touch points’ and complexity.

The second one is taking Dr.Schwab’s book of areas he discusses that have deeper impact and meaning for us all. He provides the broader issues we need to address as we go through this Fourth Industrial Revolution.

I achieved a breakthrough in my thinking in listening and reading about this Revolution

I had looked at 4IR with the classic Industrial perspective where autonomous manufacturing, seamless connections of value chains, a growing connection of supply chains work within supplier ecosystems were all leading to new value-added services and the potential for business model innovation will emerge from all this connecting up. No, it is far more complex and all-embracing.  Now those manufacturing solutions in themselves are significant, complex and challenging but something more emerged and this really does transform how we should be thinking about this revolution.

It was nicely put by Joe Kaeser, the President and Chief Executive Officer of Siemens AG writing an article for the WEF, where he remarked “The Fourth Industrial Revolution is not just about technology or business. It is about society” He observes, quite rightly “software helps optimize every process and every task whether performed by humans or machines.”

He was commenting more specifically about the breakthrough of the “digital twin” that can allow us to design, simulate and test sophisticated products in the virtual domain before even taking them into a first physical prototype or even setting them up in the physical world.

Now stop a minute, we can potentially see the virtual world and physical world as “twins” way beyond “just” the manufacturing world. Cyber-physical systems can leap even further, across so much that needs mapping in society. Data can give us the raw material to design our virtual world, to model them. I want to come back to this in another post. The real value is moving from ‘just’ physical to digital in new combinations. That holds exciting promise.

Let’s think about that a little more.

The whole success of this fourth industrial revolution is about running on information we can turn into knowledge. It is what we gain in new insights, in different perspectives, in modeling alternatives or “what ifs” comes from the data we can extract. This applies to everything.

As we connect up across industries, across society we can get closer to knowing the problems, then we can start searching for the solutions in potentially radically new ways. The potential of the 4IR is going to advance innovation across all sorts of fields benefiting society. Data is the new currency for understanding.

This data understanding combined with computing power and analytics can allow for lower cost genome sequencing technology. We only know the DNA of 14% of all the estimated species of plants and animals on land and this can lead to a real potential value to build the biological system potential and for human-related uses. As we connect increasingly everything up and use the ‘raw’ power of technology for example, life sciences can potentially ‘explode’ from this growing understanding. It will also have huge impacts on agriculture, medicine, and bio-based industries.. Having a “collective biological intelligence” has huge economic and social value.

The continued connecting up of billions of people and machines on mobile devices or through sensors with this unprecedented processing power and ‘raw data’ knowledge access opens up much. Technology breakthroughs in robotics, artificial intelligence, autonomous vehicles, nanotechnology, biotechnology, material science, 3-D printing all built from the IoT world we are constructing. It will be the growing combination of the physical, virtual and the human, applying intellectual capital that takes us (potentially) way beyond ‘just’ efficiency and productivity gains. It will impact us all in ways we have not yet fathomed out.

So, the Fourth Industrial Revolution is actually a Society Revolution.

We are not just connecting up machines and factories we are connecting up society and this becomes the real difference than in the past revolutions. The first revolution used water and steam power to mechanize production.The second revolution used electric power to create mass production and changes in the way would live (and work) after dark.

The third revolution fused electronics and information technology to automate production and allow us to enter a digital age.

The fourth revolution is building on this third, it is bringing together Fusion”, within our technologies that blend this physical and digital world we are all entering. It is grappling with a new geographical scope, it is bringing solutions into the equation to meet the velocity and speed that are happening and through its connecting up, massive system change. Knowledge becomes unlimited or limited to our processing power

The ability to harness all the technology breakthroughs can transform what we do and how we do it. It goes beyond a machine connecting to a network and sending back data. It will shift our lives but it will require a complete re-think on how we build future skills, nurture talent. Knowledge capital is going to be the biggest capital winner, alongside innovation and intellectual capital. It will be our ability, as humans to take physical and intellectual capital and turn it into new knowledge, through faster and faster innovation. It is how we connect, learn and share information will lead to fresh insights and new discoveries.

Of course, it will be a very uncomfortable journey I fear.

So much of our known world will become disrupted, it will challenge society, it will bring about new behavior. We will from this grapple with new solutions that will be designed differently, will shift existing market dynamics in unexpected ways and deliver very different concepts, products, and services that continue to link up and connect, to give us a different expectation of everyday life.

What also struck me was how platforms and ecosystems really come out of applying the fourth industrial revolution. I see this strong connection between these three in new forms of business but also how society will reform. We are seeing the converge at present but how will these diverge is going to challenge institutions, governments, our present national borders and where and how we live. Being fully connected-up will teach us to be adaptive, agile and alert. Nothing will stand still, we are heading to a fluid world.

This will have a huge impact on our lives, far bigger than we can presently appreciate

We will be challenged far more than the effects of the past three revolutions. Ownership, privacy, consumption patterns, where we reside, the balance between work and leisure will fuse even more. Yet as Dr. Schwab asks “will it diminish some of our quintessential human capacities, such as compassion and cooperation”.

What about communities, will we find time to think of others or get further into our own bubbles. Where does time to reflect, to seek out other humans to engage in face-to-face conversations come out in this re-alignment? Will we form around different “common” values and objectives or become even more self-absorbed?

What Dr. Schwab rightly points out “Today’s decision-makers, however, are too often trapped in traditional, linear thinking, or too absorbed by the multiple crises demanding their attention, to think strategically about the forces of disruption and innovation shaping our future.” That must be worrying, who is going to think through this new highly connected world, if our leaders have no time?

I have some work ahead to translate this shift in my thinking on the Fourth Industrial Revolution

Over the coming days, I will be exploring and relating to all the reports and white papers that have been written for the WEF meeting that relate to the Fourth Industrial Revolution. One discussion certainly stood out for me, from this 2018 Davos meeting. It is one that I will attempt to ‘translate’ in what I heard, that gave me great optimism that the fourth industrial revolution is going to be very different. It lifted my spirits.

Firstly I want to absorb more of the why this session caught my attention. Until I think about this, all I can say is “welcome, one and all, to the fourth revolution that might be called “Industrial” but it is actually Societal in its impact”. It ushers in a new paradigm, even though ‘paradigm’ is an overused word but in this case, it is the right one.

We are in the center of the digitally connected storm and technology will evolve in unimaginable ways. I feel we all need to hold on, it is going to be a bumpy ride for all of us. As we become even more digitally connected,  fusing technology with our the physical and virtual worlds we are challenging much of what we presently do. Skills will need to change but that’s another post to come.

The outcome of this fourth industrial revolution is one of where having the knowledge and understanding gives the real potential of new business and social models, ones that will continue to disrupt what we know. Will it fracture society or allow us to solve complex problems?

The post Shifting our thinking within the Fourth Industrial Revolution first appeared on Building Your Innovation & Ecosystem Intelligence.

This, for me, was so well structured and offered such a high level of clarity on the pathway they are pursuing, combining innovation and digital, with the outcomes emerging, of building a new suite of Business Models.

I can only simply touch on it here, I suggest you do your own drawing of conclusions.

The Insurance industry has been struggling to adjust and adapt to the rapid changes occurring yet so many are hanging onto the traditional way of doing things. It is so refreshing to see how Munich Re are venturing out, exploring and exploiting, in multiple ways to learn a new innovation pathway.

I follow the two big Re-Insurers, Swiss Re and Munich Re specifically and the innovative differences are quite significant. Swiss Re seems locked far more into traditions and I am still to be convinced they have yet to embrace innovation, certainly from an external perspective, in the same way as Munich Re is undertaking. I struggle with Swiss Re’s messages on where they are exploring the future of Insurance. The way I view, it seems far more unclear, even random, they seem far more closed down with isolated attempts to explore opportunities.  Whereas Munich Re is constantly opening up to change, exploring and seems far more comprehensive and holistic. It seems one is undertaking “words are simply spoken” the other “actions underscored”. Maybe I am wrong.

The reason I found the Investor Day presentation so good was its comprehensive coverage of the way those involved on the day covered the Framework and Infrastructure initially, then explained through different examples their Business Transformation journey, and how these parts are extending out for their total offering, in exploring new business models and solution offerings.

If only the vast majority of Corporate Organizations could set about describing their transformation in such a concise and visual way.  It would give such a raise on the clarity of what is being explored, experimented with, and embedded, besides showing clearly if innovation is central and core, or simply given “lip service”.

All I can achieve here is in this post, is first, a real praise for the presentation and provide just a few ‘teasers’ from a presentation deck that is 140 slides long. You get such a strong feeling of how digital and innovation are being combined in new ways from this.

They not only provide a foundation and a clarification of where innovation fits but they provide a clear outline of the ongoing progress and at the results to date.

Munich Re is “forming an interlocking business model” in what they are currently undergoing. One that is providing a growing range of digital solutions and indicating how active they are in continuously transforming and extending their business. They take this opportunity to discuss, through selective projects, the level of detail that gives you a good undercurrent of their commitment and belief, in the transforming they are undertaking, one of searching for innovation synergies in all of what they are exploring.

They are looking to enable technology and digital innovation to improving their 1) existing business models, looking to 2) disrupt existing business models and 3) establish new business models in selective insurance sectors, to continuously learn and adapt. This updating is on sales, retention and service outlooks. They are looking to put into place “best user experience” where they can through digital and customer-centric platforms. They are looking to become, where possible the virtual insurance company from what they are learning and experimenting with.

As like many other industries, Munich Re is building a new digital factory that taps into the trends occurring across industries (Industry 4.0 for example) and in managing risk in different ways. They are looking to work through new risk scenario’s, extend insurability and risk mitigation or prevention, add new products, solutions, and new extended services and open themselves up to a more hybrid customer ecosystem, serving across multiple channels. All of this with the aim of having efficiency increases and growing automation.

Within this Munich Re have recognized that the transforming occurring across industries from standard products, established borders is requiring different system solutions and different investments

Part of this set of solutions is building out the digital ecosystem they see as essential where data analytics, predictive analytics and basically looking to further exploit turning data into value and risk assessment becomes paramount.

The mix of digital ingredients and having the approach to building a more agile, flexible and modular design approach gives a strong impression they are driving much innovation within the Insurance Industry. As they continue to make further strategic investments and investments in their innovation they will continue to push the boundaries for transforming the industry. For example, they illustrate the Canadian approach to their digital marketing platform.

Without a doubt the presentation was well-structured. It shows this is a journey they are embarking on and you get that clarity from the commitment from the top in exploring avenues that can lead to new opportunity or new learning. This is no finished roadmap but it does give investors and multiple stakeholders a really decent understanding of how Munich Re are managing their transformational journey

In a further update in January 2018, I have lifted out this slide from the Equity Story on why you should invest in Munich Re. Two points come across, a vigorous management attention and a strong attention to a cohesive business strategy that explores transforming ideas into tangible results in investments, experiments and learning for moving towards a tangible business impact

An excellent job of communicating the Innovation Story in my mind

The whole value of approaching the story of innovation in this way, provided by Munich Re, can be such an example to so many others, a template for communicating out the digital innovation story. It strikes me that Munich Re is recognizing and embracing the changing competitive landscape, they see digitalization fostering innovation, they are learning to figure out what are agile business models that give them a growing awareness, preparedness, and resilience to try to contain and dampen down all the volatility around them. They are clearly in search for a business-enabling set of new business models, that is a work-in-progress that does, in their own words “turn uncertainty into business opportunities”.

Thank you, Munich Re, for such a great presentation and the continued message this is simply not a one-off presentation. As I have seen previously seen, they are constantly reinforcing their innovation transformation message. This has already gone into 2018 in the recent Equity Story, just released even further in qualifying innovation impact (see above). They are clearly showing a strong confirmation that the “digital ingredients” are embedded in their business strategy to search and explore, as well as exploit innovation.

Others might be well-advised take their time to reflect on their approaches to the future and how they are undertaking any digital transformation and where innovation does sit in this. “Sitting on your hands” or treating business in that past, traditional way, of doing business as usual, is not an option.  The lessons you can learn from Munich Re, who do seem to understand this well and explain how they are undergoing their transformation journey through innovation is really good. A terrific confidence builder and validation of actions.

* All visuals used came from a Munich Re investor day presentation in November 2017 “Munich Re driving digital transformation” or the last slide for an early 2018 update in Presentations 2018, on Munich Re under “Current Company Presentation” under “Shaping change in insurance, Munich Re equity story” a download PDF, 1.5MB. Both of these released into the general public domain from their publications section.

The post Munich Re offers a real clarity to their innovating future first appeared on Building Your Innovation & Ecosystem Intelligence.

My early present, well actually an idea, came the day before the event. I was included in the Industry Analysts visit to the Siemens Technology Center, at Neuperlach in Munich. We were provided a variety of insights in different presentations and demonstrations, of the technology they are working upon but one stood out for me, being introduced to Knowledge Graphs.

This one ‘thing’ really caught my attention. It was showcased in the technology center, briefly, as part of a broader set of presentations. It immediately struck me as having the potential to be very vital for the connected innovation I see, as our future.  These few insights set me off on a new train of thought and I scribbled down some hasty notes while listening to this concept. I then was able to review this a little more after the brief presentation. I then started to research on (Industrial) Knowledge Graphs for the initial depth of understanding I was seeking.

This really excites me in its potential. So let me tell you why

Everything we build upon is based on knowledge. If we retain this knowledge we are re-enforcing our potential for further learning and equally, beginning to question where that existing learning can go. The problem we have, all the time in my case, is our working memory gets layered over with new insights and knowledge. Often we fail to connect up old, already available knowledge with new thinking. We fail to see the connections. We don’t relate or access ‘resident’ knowledge

Why Knowledge Graphs excite me is we can build on Semantic Knowledge, building our Domains of Meaning and fill out the Conceptual Spaces, for adding new meaning and value. We can begin to capture all the knowledge relationships and make those essential connections between data (connecting the dots), deepen and reinforce our learning and then translate them into fresh insights. It can build our ‘collective’ memory, individually and in our communities.

Knowledge Graphs can fill a very big gap.

They can improve our domain knowledge, improve our common sense by linking the relevant context and through a decent labeling of this, we can manage far more complex schemas.

Siemens has partnered, firstly with Metaphacts and built their solutions on the AWS Neptune Engine, offering a query, open language system to build these Knowledge Graphs that works on building relations. Neptune offers knowledge graph that allows you to store information in a graph model and use graph queries, to enable your users to easily navigate highly connected data sets. Neptune supports open source and open standard APIs to allow you to quickly leverage existing information resources, to build your knowledge graphs and host them on a fully managed service.

Using a Resource Description Framework (RDF) a group of companies has been working with this in recent months. Besides Siemens, Astra Zeneca and Thomson Reuters have been looking to explore this. More importantly within Amazon, it is being used for the Amazon Alexa, so this is forming part of their future.

Up to now, knowledge has been kept and used fairly inefficiently, we have tended to keep it in our own way of doing or collecting things, it then becomes rigid in its design and inflexible to radically alter or redesign, without some massive reworking. What if that can change?

What if we can suddenly scale, have availability in different ways and design, be able to query in fast, reliable, easy and open ways? What does that do for knowledge? It opens it up finally, our silos of knowledge become challenged, ready to be built upon and capable of being changed with updated knowledge. Knowledge and insights lead to innovation. Our complete view opens up, it evolves over time and new insights, the more we build and enhance our knowledge, the richer potential we have to build with. Now that sounds tantalizing in what it possibly offers.

So, Siemens is working on Industrial Knowledge Graphs

They start with the premise that human decision-making depends on semantic knowledge for perception, reasoning, and decision-making

They are working to build the world of entities and relations to provide a more intelligible domain model instead of a complex (physical specific) data model. They build the multiple data sources (schema’s) and types of understanding, both structured and unstructured and present these in a formal semantic representation to enable inference (decision potential) and machine processing (decision-making or signaling)

This Machine Intelligence (MI) core can become the engine for realizing more Artificial Intelligence (AI) algorithms, based far more on this constant evolving richer domain knowledge. It reduces uncertainty and prompts greater actions.

Providing examples of everyday questions of Knowledge exchange

Now, what if we replaced Industrial with Innovation?

So, the industrial knowledge graph sounds really exciting, yet for me, I want to see the Innovation Knowledge Graph as my end result. Now is this a dream or reality? It is different and perhaps more intangible than industrial environments where knowledge is more tangible. You see, I have countless papers, references or visual prompters along with a vast collection of Mind Maps, built for specific innovation scenarios and knowledge capture and use. There is a vast array of insights, knowledge, and advice available within the domain of innovation, can this be captured and translated into an Innovation Knowledge Graph approach? Of course, you can question what is the “right” knowledge? What if I could translate these by capturing and forming better relationships, those can bring into a highly related set of data and knowledge understanding, connecting innovation?

We are all caught up in how digitalization is shaping much of what we do. Yet we all still need to break down isolated pockets of knowledge held in silo’s, we need to access data and knowledge faster and faster, we continue to be inefficient in how we go about this (our workflows) and we often yield far lower quality of outcome than ideal. Can this change? We are still at a very sub-optimal level, in innovation, in knowledge leverage, and in connecting all of our sources of knowledge and data.

What are the contributions of Knowledge Graphs?

Can we extract, generate, link and discover more, by having a system that allows us to connect data, as a graphics-based system, that can present in a highly visual form what we are seeking? Can we get closer to a more complete view, a view of innovation that does, finally, become holistic? Can we build a decent innovation domain ontology that allows expertise to come through, way beyond delivering only pieces of innovation knowledge? Siemens have a clear view it seems of Knowledge Graphs and their contribution to augmented intelligence. Can Innovation Knowledge Graphs help deliver a better relationship we all need in our innovation understanding?

Can we have an interactive application “in our hands” that is highly related in its data, its connections (over time) in relationships, to extract the best expert knowledge from the information, data, and observations, that allow us to focus on the important “sweet spots” of innovation?  To actually, constantly innovate, as we build the new, drawn from the knowledge built, on the best known, to form the new understanding, by connecting and forming a relationship, a network of innovation understanding.

What a thought as we enter 2018…….an Innovation Knowledge Graph that takes me closer to a better-connected future for leveraging innovation. Now then, how do I set about it? So I need to first find out more about this and then understand if Knowledge Graphs can be provided for innovation. Intriguing in its possibilities perhaps?

The post As we enter 2018 we will need Knowledge Graphs first appeared on Building Your Innovation & Ecosystem Intelligence.

We hear this consistently, our continual problem is trying to make sense of it for ourselves, we know all around us seems to constantly increase in its complexity but how are we keeping pace or at least trying too? I can’t check out of the human race just yet, can you afford too? If not then read on.

The post Delving into a complex world: helping to keep pace first appeared on Building Your Innovation & Ecosystem Intelligence.

To achieve a faster response we certainly need to educate the organization more than ever.
We need to absorb more, we need to encourage learning more especially to pursue innovation.

We need to actively set up learning ways within our organizations to establish their abilities to recognize the value of new, external information (knowledge), assimilate it, and apply it to commercial ends.

Innovation within the organization needs not just greater recognition of its vital parts, but also of its status as a value-enhancing and organizational life-changing event that we need to move towards increasingly in more organized ways.

Innovation needs to be recognized as a clear discipline, new expertise that is as powerful as Marketing became some decades ago.
Seeking out knowledge that feeds our innovations
The more we embrace change and recognize innovation demands more of our time, the more we seek out knowledge that ‘feeds’ innovation.

The more we ‘push’ for learning, the greater chance we have of thriving in a challenging world.

The expectation ‘bar’ needs to be raised from those practising innovation, I feel the constant need is for those working within innovation they have this real need to raise their game significantly. Innovation needs organizing but it also needs a better understanding of its contributing parts.

Learning and Education should always start at home.

The earlier we learn, have open interactions and form linkages, the more we will be ready to advance innovation into what it must become: a discipline highly valued for what it contributes with in terms of wealth and growth potential.

We need to find the determination to underpin the capacity for innovation, lying within us all, and that comes from knowledge and education through collaborative learning. So what is your capacity for innovation really like within your organization?

Is the learning required for innovation set up in structured ways or left to individual learning and experimentation?

Do either structured or informal ways feed back into the organizational learning system to benefit others? Or is the knowledge gained just left ‘resident’ in the person, not being put to that greater use?

Knowledge exchange is the way forward but we need to avoid the easy paths.
Organizations need to move well beyond their lazy reliance on best practice comparison and they need to find better ways to explore emerging practices.

But that takes many into the realm of increasing uncertainties, and most people and organizations are not trained for this exploration and experimentation.

It is easy to copy but we often fail to recognize all the contextual factors that went into making it that one specific organizations good practice, and I guarantee these are not yours!

Best practice has their comparable uses but it is your focusing on the good and emerging practices within your own organization is the area to focus, for learning and wanting to improve into those that make your practices really work. Then applying, experimenting and learning from novel practices that provide growing confidence in creative thinking.

Also give some thought for next practice, those practices that prompt reinvention. They start such totally fresh thinking, they challenge existing paradigms and move you towards considering new business models.

Organizations constantly anticipate risk by reducing all the variables within risk and play safe with just being incremental. Is that wrong? No, as long as we have our reward systems geared to short-term performance, while we measure leadership success the way we presently do, and the shareholder just expects consistent dividends as their part of the equation and is quickly mobilized to force change if it does not meet this immediate aim, we head down the wrong path.

We are not sustaining our organization and we are not advancing ourselves either, we are destroying much in our current approaches. We do need to focus more on the competence-enhancing not competence-destroying aspects.

We need to re-balance the “risk and opportunity” to push our use of new knowledge into fresh innovation that ‘advances’ on the existing.

To recognize the difference we need to encourage knowledge to be ‘freely’ exchanged, and then provide the environment to encourage a re-educating on ‘seeing and exploring’ new possibilities that allow us to grow.

Shifting the knowledge needle takes real commitment

Can we recognize that choosing the tougher pathway of building our own distinct capabilities, learning block by learning block, is the right one to follow.

This allows us to build capacities that are ours, seeking out the knowledge to build the absorptive capacity that acquires, assimilates, transforms and exploits, so as to give us greater chances of finding new innovation that is valued and allows us all to grow and gain from this dedicated learning.

I can’t change our prevailing system but I can point to alternatives and suggest we have other options, ones pursued by the few, which are more visionary and brave and often disrupt the accepted.

These are building on novel and unique practices, extracted not from others’ best practices but from emerging practices learn by deliberate design and our own personal experiences, formed within our organization’s unique view of the take on their place in the world.

Finding our own way relies mostly on us to find the answers. We grow by seeking out knowledge as it feeds our minds to find unique ways to contribute and share.

The post Learning favours the brave who respond faster first appeared on Building Your Innovation & Ecosystem Intelligence.

This has fascinated me for what we need to do for achieving any new innovation, it is the ‘work to be done’ that generates and pushes boundaries beyond the existing.  This ‘classic’ book has become regarded as the one that described the birth of modern capitalism as well as economics.

Adam Smith also introduced the concept of ‘the Invisible Hand’ as a core part of his thesis, that man’s natural tendency toward self-interest – in modern terms, looking out for No.1 – results in prosperity, not just for the individual but for society.  ‘The invisible hand’ is essential for free markets and capitalism, through how it generates wealth in competition for scarce resources.

By maximizing their own interest as the direct intention, this ‘invisible hand’ also stimulates those around you and in the society you belong. As you seek to leverage your own assets, you are promoting society as a whole. Today this can be more by design, or through an unintended consequence of how knowledge flows.

Arguably the ‘invisible hand’ can today be seen as realizing all our potential, individual and collective, exploiting all available existing assets for benefit and gain. We call these our tangible and intangible assets.  Often overlooked, or under-appreciated are those more intangible assets, that can significantly differentiate, are surely today’s ‘invisible hand?’

The make-up of intangible assets

There are many thoughts around what makes up our intangible assets but these can be summarized as made up of 1) human assets– the knowledge, skills, experiences, our abilities to organize these within our thinking, 2) structured capital as the pool of knowledge made up in our institutions, our rules, norms, knowledge diffusion across broad communities, 3) the social capital that forms today more around our networking that relies on infrastructure, access to knowledge, exchanges and relationships.

It is the ability to combine our tangibles with our intangible capitals that allows us to think and explore all that is around, to discover and exploit the potentials within this ‘work to be done’.

The volatile world we are dealing with today.

Today we seem to live in more volatile times where it becomes more important for us to focus on the work to be done is, not the work done. We have to keep focusing on the future. The ‘work done’ is the accumulated knowledge; the built up ‘stock’ that has contributed and been embodied in the organizations results to date, seen in the past where innovation has contributed in part to this. We have built up ‘know how’ and competencies in the ‘work done’ but this needs improving upon and challenging.

The ‘work to be done’ is where we push forward and explore greater possibilities. Part of this is focused on how we are going to adapt to change, to add more knowledge. For instance, we have operated in the past in far more of a mass production era, where systems could be designed for stable, more homogenized markets, where we could extract the maximum effectiveness and efficiencies. The ‘work done’ was equivalent to the ‘work to be done’ due to this predictability. This is not the case anymore, markets have fragmented, and we set about to design for the individual’s needs or in modular approaches for example.

We are in a ‘race’ to win

Today much has changed; we are faced with rapidly evolving technology diffusion. There is a race among nations and organizations to ‘win’ in global markets. This is causing increasing disruptive forces to come into play, where constant change is becoming more the norm and the emphasis has changed from ‘just’ efficiency and effectiveness but to be constantly adaptive, fluid and have increasing agility.

We focus far more on building in ‘tailored’ service, based on knowing the customers’ needs and understanding, on designing around new business models and in this, the ‘work to be done’ is becoming far more important than the ‘work done.’ It is the realization that it is the contribution of the intangibles assets, our growing intellectual capital, are becoming the real differentiation point to exploit the future potential.

The intangible assets provide the intellectual capital base, these allow us to react to changing demand for the required future value creation so we can effectively compete and sustain ourselves. This constant search for the ‘work to be done’ is fueling what has to be done.

Work to be done is the make-up of learning new skills, develop a greater dexterity and judgement based on what we need to ‘actively’ go out and seek. ‘Work to be done’ is searching for our future growth and well-being and this is derived through our future innovation activities. So much of this is made up of the intangible parts that can combine with what we know, what we have previously achieved, in work done, to provide the new wealth of organizations, as we participate more actively in the knowledge sharing economy of today and the near future.

We can’t remain islands of limited knowledge, we must seek out others to combine and achieve the work to be done. We are arguably in the network era, yet so many are failing to optimize their intangible assets and exploiting different organization dynamics that would greatly benefit their growth, especially when you operate with scarce resource.

Today’s need is for increasing interactions, linkages and seeking new knowledge to stay competitive in global markets.

As we rely increasingly on our growing ‘interactions and linkages’ we need a system to manage this. Absorptive capacity was introduced as an idea and first explored by Wesley Cohen and Daniel Levinthal in a 1990 article (“Absorptive capacity: a new perspective on learning and innovation”) and can provide us the knowledge learning path for building a real “knowledge exchange” process. Innovation ‘feeds off’ knowledge, they are inseparable, like twins, needing each other.

We can learn to exploit innovation, both in learning internally through the process of purposefully searching, doing and using what we gain and externally, by exploring new discoveries, collaborating, exchanging and generally interacting and so gaining fresh expertise, insight and knowledge.

As organizations seek increasingly outside their own walls, the appreciation of how they are managing knowledge, learning and interpreting this, is becoming a critical aspect of a more ‘open’ collaborative innovation to be successful. There is a growing need to absorb, integrate and apply this in new and novel ways for accelerating the innovation performance.  As we seek out more to compete in global markets, the more the knowledge increases in complexity we add more to the ‘work to be done’.

Markets are in constant flux, rapidly changing and we need to manage all the new insights. The more we are relying on knowledge flowing into the organization the more we have to strength our inter-dependence and collaboration efforts to extract the knowledge we are acquiring for it potential value. There is a consistent self-interest in doing this for our prosperity as well as others – the invisible hand.

Are organizations recognizing the value of structuring their knowledge flows?

This is the make-up of much of the work to be done in managing the knowledge flows. We need to recognize the importance of this shift from (physical) ‘work done’ to (intellectual) ‘work to be done’ and reconfigure the changing capabilities and capacities required, so as to grow our future ‘wealth’ of organizations, of nations and within ourselves to learn and respond. We needs to understand that today’s ‘ invisible hand’ is how our intangible assets are increasingly crucial and need to be actively managed, for this essential work to be done.

Forget ‘work done’, that’s in our past, it is already in the (knowledge) bank, we need to focus on the work that needs to be done so we can compete and thrive, there is so much more to understand and learn from as the challenges become more demanding to deliver on our innovation efforts.

There’s so much work to be done.

Our future progress is tied up in offering meaningful work, providing that sense of purpose. Building greater capabilities to quote Saul Kaplan “are the amino acids of innovation.  They are the building blocks that enable value delivery”. We are looking at increasing “the random capability collisions” where new innovation will happen at different intersections that combine knowledge from global markets, other competitors, different cultures and a variety of disciplines.

We need to build new capabilities that are far more market orientated, from co-creation and knowledge acquisition, from sense making, tapping into collective memories across a vast network of understanding. We need to unlearn, we need to reflect. All of these are in the ‘work to be done’ to stay ahead, be relevant and compete in today’s world. A world that is far, far different from the world seen by Adam Smith and what he saw needed doing.

The value in his observations are still valid today, the ‘invisible hand’ is as important today through our successful utilization of our intangible assets as we exploit new knowledge and apply this to innovating our future. One that combines our need with societies, not just based on growth but also on well-being as well. It is knowing the ‘work to be done’ that is so necessary for innovation and much of this remains hidden, waiting to be discovered.

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Part two – what needs to improve in innovation?

I asked in the first part of this series of blogs –How do we advance the learning needed for innovation?  So first, do we (all) agree that we do need to improve the education around the subject of innovation and its management?

Do we need to recognize it as an essential discipline that should be fully recognized with our organizations?

Today it is not central, it is not driving the business surprisingly when you stop and think about it, older more established practices drive the business and innovation is a responder. I think this needs reversing totally.

We live in knowledge-based societies and we need to constantly increase our share of understanding as this new knowledge becomes the building block for innovation to take hold and grow our wealth, create the next generation of products or services.

Our challenges are greater and more complex today.
Modern society is becoming a fairly intense place, it is growing in complexity, reducing constantly our need to reduce our reaction times so we need to ‘read and react’ far quicker than in the past.

We are being highly challenged in adapting our existing practices and processes within innovation to speed up. In reality top of mind for CEO’s is the innovation gap, they want to quickly see and fill and secondly, their worry over the innovation delay.

We need to find new mechanisms on innovation to allow for a better transfer.
The appreciation of knowledge – its collection, its understanding and interpretation and in its transformation and exploitation are not being valued as highly as they should within this need to speed up, to close gaps and reduce delay but more importantly, to contribute to higher value outputs that “fuels”  new innovation activity.

The production and reproduction of knowledge becomes key –  it drives activity and direction for innovation activity. As we create, accumulate and disperse knowledge we become more engaged outside our own walls. We need to seek constantly a comparative advantage and to achieve this goal we are seeking more and more open exchanges to allow this flow of knowledge to be captured.

We are becoming increasingly interdependent and permeable to disturb what “we think we know” to “what we need to know”. Relationships, networks, dedicated resources searching; collecting and assessing knowledge all rapidly contribute to our growing need for new capacities. We then need to build the appropriate capabilities to translate and exploit this new knowledge.

Our “need to innovate” is becoming our sole means to survive and prosper in this highly competitive world we continue to create and knowledge becomes the key to this.

Knowledge cannot be allowed to be left to chance today but needs a coherent, structured way to be captured, used and valued. “Our knowledge” is our potentially most highly prized tradable asset. The skills we build from this understanding allow us to build, explore and experiment so we can translate this into new innovations.

Content and Context are the essential partners.

As we look at innovation today, often one of four aspects are missing or under-served for what an organization is trying to achieve. The ‘context’ that innovation is set is usually the most poorly described part. The ‘content’ can fill rapidly but this tends to be full of endeavour and activity as the results have not been as clearly articulated as they should have been.

The ‘purpose’ and the ‘process’ make up the other parts. Knowing the purpose comes from setting the context –  this clarifies the inputs that form purpose. Lastly we have the process where the activities should flow through.

Each of these four dimensions is often not as solid or robust as they should be, and increasingly, new knowledge is not getting translated to leverage its potential due to these weaknesses within our management of innovation.

Absorptive Capacity becomes essential to understand

As we rely increasingly on our growing ‘interactions and linkages’ we need a system to manage this. Absorptive capacity was introduced as an idea and first explored by Wesley Cohen and Daniel Levinthal in a 1990 article (“Absorptive capacity: a new perspective on learning and innovation”) and can provide us the knowledge learning path for building a real “knowledge exchange” process.

We can learn to exploit both innovation and learning in the following ways:
Internal

1. Learning by searching – as we formalize our search activities we absorb new understanding that leads to new innovation potential
2. Learning by doing – as we accumulate knowledge gained, we gain experience and the more we establish repetitive activities through exploring, prototyping methods and reduce the ad-hoc activities the more we can learn and gain from this approach
3. Learning by using – as we utilize and adopt more, through exploration and adoption of new products, new technologies and methods, we are opening up to experiment and possibilities to extend this new ‘experience or knowledge’ even further.

External

1. Learning from advances in science and technology – as we absorb new discoveries we capitalize on adding further value or diffusing this even more
2. Learning from inter-industry spillovers – the increasing value of cross industry collaboration and exchanges is going beyond ‘just’ spillovers, they are increasingly significant to our learning and applying different  approaches that lend themselves to a greater commonality
3. Learning by interacting – we increasingly go ‘across’ organizations and equally move ‘up and down’ them to seek out interactions with other sources of knowledge and growing expertise. These are further augmented by external collaborative exchanges and cooperation activities allowing for deepening knowledge, greater experimentation and interactions to deliver potentially ‘richer’ innovation.

Each of these six points of learning need exploiting for innovation.

Finally in this part, I’ve discussed absorptive capacity in previous articles “making innovation practice spread”, learning to absorb new knowledge for innovation and moving towards a more distributed innovation model to provide a fairly comprehensive set of discussions on this critical aspect of innovation and knowledge provision.

We are equally in need to recognize differences and value in tacit and explicit knowledge.

The distinctions and discussions about tacit and explicit knowledge are equally important to our “knowledge exchange”. Ikujiro Nonaka discussed four different modes of knowledge conversion and subsequent organizational learning in his SECI model

1. Socialisation (the conversion of tacit knowledge to tacit knowledge);
2. Combination (the conversion of explicit knowledge to explicit knowledge);
3. Externalisation (the conversion of tacit to explicit knowledge); and
4. Internalisation (the conversion of explicit to tacit knowledge).

To explain this we need to distinguish between tacit and explicit I outlined some thoughts in a previous article “tacit knowledge rich in its innovation implications” and further explored this in “making the appropriate impact”.

The critical message here is that tacit knowledge vs. explicit knowledge is where the interaction between these two is vital for the creation of new knowledge that leads to future innovation potential.

Knowledge for innovation needs to build in both formal and informal ways.

With the recognition that absorptive capacity and richer combinations between tacit and explicit knowledge needs a real recognition of their vital part they are playing within innovation’s future health.

Without new knowledge, we can’t explore the potential for innovation. We do need to explore these far more in our advancing knowledge and education for innovation

My last part within this knowledge exchange series I want to finish with the ‘coupling’ within the innovation system, our need for greater convergence and the dangers lurking in innovation. Each has its place within knowledge and education for innovation.

The post The real value of knowledge exchange first appeared on Building Your Innovation & Ecosystem Intelligence.

How can we learn to recognize it more actively as as essential part of our lives, when to trust it, how to teach it to others, how to share what it has offered to us, as individuals, to others.

Then imagine what it could provide us for this knowledge to be leveraged within any broader community use, so it is knowingly valued by others as something they can gain from, not as we often do, simply reject it as not within ‘our’ experience.

That could be pretty valuable. It could give us a deeper understanding and empower us to function better in many sorts of situations. Then surely we must search for understanding this more and what it means, as in this case, for relating it to innovation.

Let’s start off by stating tacit knowledge is inherently inefficient, so is good innovation; it is messy, often unstructured. Why do we continue to not give this TK sufficient ‘head space’ in our thinking? I

s this because it is not tangible, that softer aspect that we reject as we don’t have time for it or simply we don’t ‘trust’ it like those ‘hard’ quantifiable measuring points?
Using Michael Polanyi as my main anchoring point here.

Michael Polanyi, was a scientist and philosopher, who while writing Personal Knowledge he identified what he calls the “structure of tacit knowing”. He viewed it as his most important discovery. He claimed that we experience the world by integrating our subsidiary awareness into a focal awareness.

To quote part of an entry in the Wikipedia and get the ‘heavy stuff’ out of the way:

Tacit knowledge is not easily shared. It involves learning and skill, but not in a way that can be written down. Tacit knowledge consists often of habits and culture that we do often do not recognize in ourselves. In the field of knowledge management, the concept of tacit knowledge refers to a knowledge possessed only by an individual and difficult to communicate to others via words and symbols. Knowledge that is easy to communicate is called explicit knowledge.

Tacit knowledge has been described as “know-how” – as opposed to “know-what” (facts), “know-why” (science), or “know-who” (networking). It involves learning and skill but not in a way that can be written down.

The process of transforming tacit knowledge into explicit or specifiable knowledge is known as codification, articulation, or specification. The tacit aspects of knowledge are those that cannot be codified, but can only be transmitted via training or gained through personal experience.

Tacit knowledge (TK) is a crucial input to the innovation process
We depend on many of our different levels of tacit knowledge on how to innovate.

Firstly, new and novel problems require TK about ‘ways’ to tackle the unknowns associated with discovery and how to bring this out into a final innovative concept. The more we can form into teams and share what we know or why we do things in certain ways the greater the potential power of the collaborative effort.

So the more interactions we have, especially face-to-face, direct contacts the more our tacit knowledge is seen and applied. The value of knowledge transfer to the innovation process is vital. Often we employ knowledge experts, subject matter experts and have a diversity of specialists collaborating as they are surprisingly as individuals, often unaware, often unable to articulate, communicate and describe what they know; it often requires others around them to bring it out.

The important point is they know but often cannot articulate it without prompting or drawing out what they know in a ‘given’ context to make clearer meaning of it, moving it from tacit to explicit.

Secondly, tacit knowledge can be a real sustainable competitive advantage. The more you can transfer knowledge, tacit and explicit and embed it in group settings, to in-build in shared core values, assumptions and beliefs, the better but as we know, it is often hard to pin this down, to locate this, quantify it, map or value it. Especially without encouraging and stimulating the environment to allow time to draw it out.

Thirdly, TK makes up a large part of our human capital, the knowing part. To give TK the chance to spread or diffuse across the organization or outside, organizations not have to invest in their individuals but bring groups together to greatly increase this human capital. Also this knowledge has to  simultaneously be captured (if possible) so it ‘resides’ and can be diffused out even more.

Knowledge management systems can help here if tacit knowledge was given the appropriate weight of focus it does need and a skill to decide the ‘what’ in its relevancy (and value) to any discussions.

Fourthly, and central to Michael Polanyi’s thinking on TK was the belief that creative acts (especially acts of discovery) are filled with strong personal feelings and commitments. He saw this as ‘creative tension’ where informed guesses, hunches and imaginings are part of exploratory acts and motivated by what he describes as our ‘passions’.

This is why there is always such a strong argument for innovation champions as well as the need to encourage anyone with a strong belief or insight, to find the time to explore it by clearing the path for them to do this as part of their workday- not just in their ‘free time’.

Conviction is always required for realizing innovation.
Polanyi talks of knowledge of approaching discovery. To hold such knowledge is an act deeply committed to the conviction that there is something there to be discovered -what we try to do through invention and innovation?

It is through our determinations and beliefs we ‘push’ our tacit knowledge as we believe this is a path to go on bringing something new into the world, our new innovation. He placed a strong emphasis on dialogue within an open community- our networks for innovation to work. This is why open collaborative innovation is making such headway. We are allowing more ‘convictions’ to enter the innovation pipeline

Polanyi wrote in The Tacit Dimension, we should start from the fact that ‘we can know more than we can tell‘. He termed this pre-logical phase of knowing as ‘tacit knowledge’.

We need to move tacit knowledge out- into the open and main stream- so it can be shared.

Finally, tacit knowledge is certainly very personal, rooted in action and for us needing to acquire new experiences consistently we need to push ‘the organisation’ to find the ways to be more committed, involved and identifying with wanting to strenthen this.

Organizations seek the knowledge owned by the individual as it is highly valuable to be shared, to be drawn out and not, as is often the way, the other way around, imposed upon individuals to bury their passions , guesses and hunches, the mistake we make today.

A top down process by determining only the knowledge they need to know stifles creativity, enquiry and innovation discovery. We need a ‘healthy’ mix and open minds to allow discovery.

Management certainly needs to lead. It needs to give the context so knowledge can flow. For instance leadership inspires and identifies the right targets, while management identifies the right practice and encourages dialogues and sharing.

We need to ‘codify’ as much as we can, hence why I argue, it needs to be built around context. This then allows people, time to explore, to exchange ideas and experiences so they all come together within the appropriate context or ‘spin out’ into something radical that leads to potential real breakthroughs.

Finding time and making connections, simply being allowed to explore is rich for innovation possibilities, set around some even loose context.

Having a more open network that seeks out diversity and through this, delves potentially deeper in this often latent tacit knowledge that resides in the individual does needs significant focus and commitment to allow it to happen.

To encourage the free flow requires extensive personal contact and trust. Do we provide this in today’s organisation? Seemingly we are going the other way and perhaps knowledge attainment might actually be suffering?

For this blog I drew on an exchange I had with someone within my network, Jim Burke, Manager, Futures, Forecasting & Change Mgt at TASC, Inc.  These were in his private capacity for the exchanges we had that covered a range of different subjects we both wanted to explore our thinking ‘out loud’. Also I drew upon different references that explore tacit knowledge to help me in making some of the points expressed here.

The post Tacit Knowledge- Rich in its Innovation Implications. first appeared on Building Your Innovation & Ecosystem Intelligence.