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.

One outcome is this post. I am grateful to have had the time to translate this in my mind. We all move on far too quickly and not have the time to sufficiently reflect, and that can be a huge mistake.

The day before the main 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.

The time spent in the Siemens Technology Centre was far too short, in getting the depth I would have liked. Yet, by accessing and re-evaluating all the presentations I have collected over the recent years, I pieced much of it together, into the clarity that I wanted, well actually needed, to understand where Siemens was positioned and going.

I came away from my visit to Siemens greatly impressed, now I am busy filling in the blanks.

The opportunity to attend has certainly triggered a lot of thoughts and connected me into Siemens, a whole lot more. They do seem to be are on a good path for growth but to make it a path that fully connects to exploit all the opportunities it can explore, Siemens needs to exploit Ecosystem thinking a little more. Surprisingly, I felt they lacked a more holistic view of innovation; a clear innovation process, and dedicated focus on this, which is required. Innovation is certainly central to the future of Siemens but it seems to me not to have the core positioning it should have.

Often you come across a dominating mindset of engineers, scientists, and technicians, who often have the mistaken view that they know innovation well when actually they only have a terrific depth on a given subject, not the breadth or broader comprehension. This comprehension is often missing by not having both fully connected and available, or consciously built in.

If you take a more holistic view of the all the connected parts of innovation that value-add, built on customer need, etc., that a structured innovation process offers, the result is more than likely to be more radical in final design and end-result. More radical in design and outcome The whole process to capture and translate options, to minimise the gaps, and leverage the critical opportunities, fully needs a system, a connected system.

I can’t prove it, in a very limited visit I had at Siemens but I certainly felt, they might value a more detailed review of their innovation environment, some more, it might potentially benefit them.

It is partly due to this classic missing “T” you expectations often fail to recognize a growing imbalance.  To build a more comprehensive innovation capability and capacity, does take for a very dedicated focus, especially when it is based on building digital solutions. Innovation expertise is as specialized as engineering for example.

Also if the innovation is not well-defined at the starting point, conceived often in a lab or internal group but not knowing fully the customer ‘real’ needs, you can lose an awful lot of energy and opportunity. We often confuse invent and innovate, each has its place. For me discovery and execution need a lot more robust “working through” the innovation process, to drive a diversity of ideas and concepts through to a final value point. How does Siemens balance invent and innovation?

If you don’t reflect on the holistic contribution of a connected innovation, a complete connecting up of the innovation process, you can limit your potential. Time to market suffers for one. Knowing customer need can be another. Silo’s of knowledge can lock out new potential opportunities. The Innovation process needs to be robust in structure, commitment, and resource. My limited time trying to appreciate where innovation fully fits within Siemens was clearly constrained, but from my past experience using the word “innovation” is different than its application. I felt innovation needed a more holistic focus.

Siemens is in some highly competitive industrial markets. Like many others, they are embracing digital as strongly as a number of other companies I have been following. I did get some disconnects still at the event and this post is attempting to elaborate on these. Among these was that I was not able to understand the differences (if any) in their platform offering, Mindsphere. I had to come away and dig a little deeper myself and subsequently produced this post “There is a growing force in Siemens Mindsphere as it gears up for 2018″

Although Mindsphere was featured prominently as the core of all their digital work, surprisingly, it was little featured specifically, in the Siemens technology center presentations and on the following day at the Innovation Event.  I think there are some reasons for that. Partly they, Siemens, have lived with Mindsphere for some time and it has become embedded in where they are going, perhaps forgetting the significant lag the outside world has to this, to relate or appreciate or make those essential comparisons. Customers are working through a variety of choices in selecting the platforms they require, there is unease with the risk of “lock-in” for all involved, to understand and evaluate “gain and return”.

Equally, they are perhaps still lagging others in what they can provide as application solutions. I would reckon 2018 will be a huge ramp up for them. One outcome from my research has been a dedicated a post on Siemens MindSphere, over on my dedicated Ecosystem and Platform site, as they have one or two edges that, if exploited well, will give them some ‘competitive edge’

The one ‘thing’ that really caught my attention, is still under the mainstream of Siemens corporate attention I would speculate. It was showcased in the technology center, it is going to be vital for the connected innovation I see as our future.  This deserves a dedicated post on what I consequently learned, not through Siemens, but in a conference that Siemens presented at, in November 2017. These insights set me off on a new train of thought and the presentation gave me what I needed on (Industrial) Knowledge Graphs for the initial depth of understanding I was seeking.

Thankfully the trigger was on my visit to their technology center. This was not even mentioned at the Siemens Innovation Event the next day, on recall, so it is presently flying under the Corporate radar, still to be integrated into the offering of Siemens. It is seemingly ready but I feel is yet another 2018 rollout, or should be. Still, a dedicated post on Knowledge Graphs follows this one in the next few days. This excited me in its potential.

Let’s explore this a little more, partly on my tweets of the day and after, in reflecting

Getting Physical and Digital as the twins we all need. The need to go way beyond.

The Siemens Innovation Event was digital all the way. Of course, the CTO, Roland Bosch hosted the event and it was all about the value of connecting the digital into the physical but the over-emphasis prompted me to tweet some different thoughts not just simply reactions:

Yet the one biggest feeling on listening at this Siemens Innovation Event I tweeted.

“What is going to give @Siemens the edge in the future? Digital dominates #InnoDay but the #UnlockThePotential is the quality of their combined results of product + service + design + digital as the bundled solutions. Let’s remember it is the potential #Innovation power of ALL”

Getting the balance right is crucial for all Industrial digitally aware organizations.

Siemen’s I don’t think has the present emphasis wrong but perhaps the balance needs to be recalibrated, going forward. They have the digital momentum clearly, they are mastering the physical and digital domains in the “twin enablement,” to generate greater innovation and growth options. Yet I just felt they are not aligning these two ‘worlds’ as comprehensively as they could or should be.

Then in my reflecting post event, I came across a recently published article from BCG

With Martin Reeves as one of the authors. I always pay attention to his thinking; I draw a lot of understanding and connection from these. The article was “Getting Physical: The Rise of Hybrid Ecosystems”. This made many of the connections I was thinking about that Siemens needed. It bridged a gap in my thinking, perhaps it also would in their thinking, if they see what I see (or don’t see- ask!).

The articulation of the Siemens Ecosystem was missing at the Innovation Event, it was lacking as a central aspect, in its recognition of the value potential (in my mind) and the real connecting value it offers. The platform and digital solutions tend to dominate the (present) thinking at that Siemens Innovation Event. Siemens needs to think deeper about the Siemens Innovation Ecosystem and its management, in my opinion. That can truly #UnlockThePotential!

Let me explain why Ecosystems need a greater focal point that Siemens ‘seems’ to presently lack.

Siemens is like many other Industrial Organizations, in first love with digital. They look at the valuations and impact that Amazon, Facebook, Apple, Microsoft, and Google as platforms and this does have some “digital envy.” Industrial applications are very different from the Social application. You can never lose sight of where the sustaining value comes from, in physical products that need to perform consistently, over a product lifetime, and need to be deeply trusted and well-supported.

Of course, the phenomenon of digital and the platforms that we have all embraced and embedded in our daily lives, has been stunning, in what I see emerging from Siemens and many others. It has transformed all of what we refer too, in recognizing connections and interactions and give us a potential for a whole new ‘connected’ value. Technology offers us a whole different way of managing our daily lives. Yet the industrial world feed off this even more, in distinctive ways of need?

Certainly, Siemens can and I expect do, constantly look to build on what they have, with digital driving the impetus of change, it can force transformation and growth. We need to determine that Industrial platforms are different, significantly different, in how they create and connect value in radically different ways for their customer’s needs. As an end result, it has to be far more sustaining and not built on ‘instant’ reaction or gratification.

Real value can come from scoping your Ecosystem holistically

Digital is central to create value, it is the primary focal point in most organizations at present. Industrial organizations are perhaps drawing more from the ‘pure’ digital organization, they are only getting comfortable in their own “digital skin”. Certainly, the external consulting advice is constantly relating to the ‘best practices’ to these purely digital orchestrators of their digital ecosystems, but this can be equally, damaging and constraining. If you focus too much on other parties practices, they are not your own “emerging practices”, they are someone else’s practice, operating in a totally different business contexts and market conditions. We must never lose sight of this.

I am not saying Siemens is lacking, in recognizing differences; of course, they are consistently working at finding those. They have a growing perspective from a digital view of the value-add, presently digital is 10% of their total business, nicely growing at 20% plus a year. The question here, though, is this: are there are real differences that, perhaps, they have not yet recognized or studied, clarified or validated, as robustly as they could? They need too, earlier than later.

Clients and markets quickly define differentiation and prospective value, that always needs shaping and influencing. I’m not sure Siemens is defining what they do as clearly as they can; do they want to be a leader or fast follower of others, or contain themselves to specific domains of expertise, others will find hard to mimic? What will be the cost or even better, the “Value@Stake” analysis, a real measure of opportunity identification? Taking a further step back on where ‘true value’ lies might yield different results, some years into this digital initiative by having a more focused innovation perspective might. More an ‘open question’ here in my mind.

Why I related to the BCG article is it attempts to break out the difference in operating need.

For Siemens or any other industrial producer of physical goods, the physical is the bedrock. Digital is a different building value proposition to take the product forward, offering new and highly innovative ways, if the innovation system is structured and fully in place.

In the BCG articles, they suggest digital ecosystems to dominate, have three factors that make the competitive environment dynamic:

1. Zero marginal cost and positive network effects are increasingly coming at virtually no extra cost. I can recall the CEO of Siemens, Joe Kaeser, commenting on that in a past article.
2. Unprecedented Data Accumulation and Analysis gives the “data flywheel effect,” as this grows, you accumulate more data that fuels improvement on services if you can manage this. This has a “reinforcing effect”, strengthening the virtuous circle even more
3. Seamless and Comprehensive Digital Experience happens at scale, that over time progressively satisfies multiple needs on a single platform if it recognizes, evolves and adjusts, to this demand.

These are the foundation rules in a winner-takes-all dynamic of competition. How do the Industrial Platforms and their Ecosystems relate to these? This requires a comprehensive ecosystem management. I think this is still understated in Industrial solutions. It is capturing and engaging but it needs a greater attention, time and value correlation

Digital ecosystems have seen physical assets as only complementary. It is the need of the Industrial players aspiring to “orchestrate and lead” to work the physical and digital in different, much tighter ways, the “twining” becomes vital.The physical channels are well-established in their need and outcomes in performance, reliability, and consistency of result, so as to be well understood and drawn into the ecosystem in more comprehensive ways to capitalize on different value-creating options.

We need to shift from a focus on digital as our central “play” into the intersections of the digital and physical in new ways. Siemens is well on their way but it is this thinking more deeply, about combining the two even further, in new ways that connect the whole innovation experience, to explore different outcomes that clients are looking for or transform their understanding, can result in this more ‘holistic’ approach.

BCG suggests AI is a wedge but this is where having a more robust Knowledge and Innovation structure sitting alongside, and managing the physical and digital world as twins, becomes a clear value making point. Translating knowledge into innovation is key.

Suggested by BCG are three factors that need to be managed,  with my added thoughts:

1. New Frontiers and Unexploited Opportunities will be greater battles in the physical realm, using the combination of technology, digital, knowledge, and innovation as the combinations.
2. More connected hardware is allowing for encroachment from new sources of competition. It is the power of the ecosystem management that will orchestrate the competitive edges, constantly evolving and needed in dynamic market conditions, based on a highly flexible platform
3. More Real-World Data is enabling the physical world to catch up on the digital-only world.

This, as BCG suggests, is giving birth to the hybrid digital-physical ecosystems.

These require deep domain knowledge and dense business relationships and a growing comprehension of the ecosystem effects.

The unique combinations of forming an Ecosystem need understanding

This will revolve around exploiting 1. Technology and Relationships, extending 2.Depth and Breadth of Knowledge, 3 Forming and 4 Extending Connections, 5. Experimenting and exploring  6. Creating and participating in New Ecosystems and 7. Paying attention to the Rejuvenation of the Old, as a constant need.

That consistency of evolving, challenging and reshaping even faster, with this Ecosystem perspective, will drive the ’emerging’ digital solutions of the Industrial Organizations, for the likes of Siemens, as well as a number of other Industrial companies, looking to lead and continue to grow.

Of course, Ecosystem development and management are a whole lot more than just and seeing complex systems that can be built out from the ones briefly outlined.It is fully appreciating the dynamics within the system, always adapting, emerging and resolving at different rates, to explore and exploit innovation as the central concept of Ecosystems, that needs greater attention.

For me, it is the management within the broader Ecosystem that will leverage fresh knowledge. Being able to apply context and domain expertise, worked through digital platforms and applied technology, that focuses on co-creation, so as to deliver on the potential of new insights, that are leading to innovation with a real difference. This collaborative “orchestration” will determine those that will emerge as winners within the Industrial environment as they form really unique competitive edges based on reliance, trust, and deepening partnerships.

Closing thoughts

The need is for the application of Ecosystems in multiple contexts and forms. Just equally consider the Interconnected Parts of an Ecosystem. Are we truly clear on what is reshaping entire industries? This requires not just a robust understanding of Ecosystems but also of the power and strength of your platforms, in its adaptability and flexibility to respond, not just to simply build but to shape. An ecosystem that is highly collaborative and that shifts the relationship dynamics, will require a deepening of the co-evolution.

This co-evolution is a significant difference between ‘pure’ digital ecosystems, where the winner is constantly looking to take all, and the emerging Industrial Ecosystem, one that forms and deepens relationships around trust, reliability, and performance, with its clients and partners over time, in final product solutions.

Industrial Ecosystems will be designed differently, they need ‘infusing’ differently than just building these internally only. Those that learn the new dynamics of Industrial Ecosystems first will actually win the long-term race, not just in having a “me-too” platform, but in its unique design and adaptability. One they can articulate and others can see the clear value in joining. An Ecosystem that evolves and is constantly connecting in radically new ways, of thinking and proposition.

We are entering this new era of innovation, where connected highly collaborative relationships, form the ecosystem to build unique value propositions, that deliver the new competitive edge. I wrote a series of posts on this “new innovation era, one can become the opening reference point to reflect on this “Advancing my Applied Innovation Thinking” as it helps in developing a more, open, fluid design of innovation.

Moving along an evolutionary path

We are getting somewhere along an evolutionary path in what I saw and heard at Siemens, but it needs a more radical blueprint of ecosystem design. Who will pioneer this new game, where management skills will be based on ecosystem understanding, building and managing needs, to be thought through and fully connected?

As we presently focus on the digital platform, let’s not forget, it is the strength of connecting and understanding the ecosystem that will actually be the difference between winning and losing, and that’s relationship-based, as much as technology-based.

Ecosystem management needs far more articulation, well beyond what I heard at the Siemens Innovation Event.  I thoroughly enjoyed the event but it has prompted my necessary reflection here, as I saw opportunity gaps that would accelerate innovation more effectively. In the next twelve months, Industrial Ecosystems will evolve and it will become highly critical to be mastered and articulated, in ways that show their connected innovation value.

Going beyond present thinking to build Industrial Ecosystems by design

This post only “points the way” to separate out the present thinking, to extend it in structured ways, on what needs to be thought through to build Industrial Ecosystems. Industrial Ecosystems that combine not just the digital with the physical, but also fully leverage the connected relationships that prosper by co-evolving and striving to deliver innovation in new exciting ways, those that advance collaborations and seeks out constant growth.

• Updated on 28th December to link to other posts of relevance or extend a point or two, as well as correct one or two errors.

The post Creating the Industrial Ecosystem first appeared on Building Your Innovation & Ecosystem Intelligence.