In a recent report, jointly from the World Economic Forum and McKinsey called the “The Fourth Industrial Revolution and the factories of the future” they made a number of observations

“After a decade of flat productivity, the arrival of the Fourth Industrial Revolution (4IR) is expected to create up to $3.7 trillion in value to global manufacturing. A few years back, experts noted that the changes associated with the 4IR would come at an unprecedented rate yielding incredible results for those who truly embraced them.

Still, the hockey stick of benefits has not kicked in yet – while all companies are making efforts to adopt technology, most of the production industry (~70%) remains in pilot purgatory (where technology pilots last for extended periods of time, and companies do not take the final step of scaling up viable technologies). Less than 30% of manufacturing companies are actively rolling out Fourth Industrial Revolution technologies at scale”

No wonder we presently have trouble attracting many businesses onto platforms when they are still very much behind in deciding or deploying a strategically thought-through IIoT digital design, that is connecting everything up.

It is equally holding a new form of innovation back, one that is highly collaborative where partners come together to work on more complex problems. Collaborators can achieve solutions only by being “fully” connected up, comfortable with their data, understanding and contribution, both within their knowledge and insights.

The power of multiple-connected ecosystems gives innovation a completely different momentum but it needs this 4th industrial revolution to be fully operative, for a digitally connected world in manufacturing and beyond.

According to a recent Deloitte report: Most manufacturing lines still look a lot the same way they did 10, 20 or even 30 years ago. Operators clock in, have a brief conversation with their crew and shift supervisor and then operate a machine or tool for 8-12 hours before heading home. Depending on the day, the machine may break and need maintenance or an adjustment, the line settings may need to be modified for a specific product or run, or the operator may need to step away to resupply the line or be trained on a new procedure. In 4IR transformed manufacturing sites this is no longer the case. So why are we adopting digital in our manufacturing plants slowly and very cautiously?

The biggest reason is dealing with the fear of the unknown – a lesson in 4IR change management.

This fear of the unknown, that constant blocker of “not been tested in our environment” has greatly inhibited adoption. Equally the nature of much of production has been designed “on the fly” and the equally tough job of connecting the whole process up in a new integrated, fully connected system meets huge resistance. Legacy systems plague the ability to break out of our present traps of silo’s and we are still struggling to solve these issues, machine by machine, a process by process, incompatible software and hardware, piece by piece.

Arguably Industry is trapped by complexity. The system complexity, understanding and investment delays are often huge and difficult to unwind. Many organizations are constantly meeting resistance to change, building some very mixed views of transformation and its lasting benefits. Thirdly the redesign to save space, and to determine more up-stream flexibility needs embracing digital solutions and reliance on partners and levels of collaboration not truly established to date. Any revolution is difficult, costly and requires time to work through a change in “order” to adapt to the new.

The Underpinning drive to connect-up is getting closer to that “crossing over” point

Increasingly, there is this recognition that the competitors seem to be moving faster towards becoming more digital, so it then becomes a race to catch up, as this “digital advantage” is increasingly eroding competitive advantage for the digital laggard, even if he is today the market leader. There also continues today that industry disruption is increasing, by those spotting both opportunity and weakness in present market players. Those that are digital laggards can’t achieve the same flexibility, the speed of manufacturing change, productivity and response as the new investor, building more on “greenfield” principles. The established player has processes that are fixed far more on past trading conditions, where longer production runs produced (past) economies of scale that became over time, more rigid and not adaptive.

Supply chains have often remained reliant still on manual instructions with limited visibility to the whole supply change to manage, adjust and respond. Today, production is changing its philosophy where flexibility is “baked in”, for adapting further upstream and downstream so as to cater to a greater individual client and customer need. High levels of automation are generating the “digital factory” that comes as an offspring of this 4th industrial revolution.

The internal change is slow. There is a strategic resistance, there is a lack of organizational agility and still not the level of commitment this is required, driven from the top. Hence why we are stuck in so many “pilot purgatories”

Yet the threat of not changing is building pressures. this reluctance to embrace a new digitally connected world is feeding the “seeds of destruction”. Customers expect rapid response, reduced delays, tailored designs and greater engagement.  What is going to shift the needle on this dramatically to recognize we are in a real revolutionary period and caution is not your friend but your enemy?

There are increasing use cases being built and shown. The WEF wants to create a neutral, learning platform, which provides the best technical and organizational insights with respect to 4IR technologies in manufacturing to accelerate the delivery of the benefits, to give a sort of safe harbor. Such a learning platform will also facilitate collaborative action and the new partnership between companies and governments. Confidence grows through these learning exchanges This will partly help yet this will not be the real catalyst, that lies in addressing the old legacy problems, in bold, imaginative ways.

Innovation is one powerful catalyst ripe for leveraging in this 4th Industrial Revolution.

I wrote a post over on the Hype site “The Innovation World is Changing Due to the 4th Industrial Revolution“: We first need to recognize that there are twin forces at work, feeding off each other. We are facing greater disruption and an increasing innovation pace. These are constantly combining, relentlessly adding the new shape to our future. We are actually caught up in a very revolutionary period. The prospect of new innovation potential will eventually work through into the world of Industry 4.0 as a major game changer“. It is all ready for those accelerating and being fully committed to their 4IR journey.

We have to recognize the days of simple product innovation are dwindling. where technology, digital solutions, greater customer insights, where a new breed of designers, engineerings, scientists and software talent are combining built through a platform and new innovation ecosystems thinking, are all emerging. This new innovative approach is building greater complexity into our final innovation offerings, yet it is providing increased customer value or societal needs, on problems that require this combined thinking.

We need to recognize that we are increasingly becoming reliant on collaborative platforms, to realize the result: one of more radical innovations, that are capable of winning the hearts and minds of many consumers as they are more adaptable, adjustable to personalization, rapid shifts in trends or solving their complex problems with imaginative solutions, unseen at the time, in ways not possible without embracing and connecting up in the digital world.

Reality dawns, when the penny finally drops as I said in the Hype post “The customer is increasingly at the epicenter of the economy. The products and services are enhanced through the digital capabilities that boost their value and worth. New materials are making our assets more durable and resilient, and data and analytics provide the valuable feedback needed to build even better services and performance in the future. All this connecting and reacting is requiring new forms of collaboration, and we are seeing new types of organizations emerging. They are far more dependent on platforms and ecosystems. Innovation is the unlocking mechanism”

There is a real value in recognizing and delivering on the fourth industrial revolution, we can start to think outside our classic product innovation boxes. We can shift our often-linear thinking as the Industry 4.0 is the revolution that connects all the parts. We can loop, explore, connect, build scenarios, test assumptions, build digital twins to just the product and manufacturing impact.  Recognizing the transforming potential of having fully connected up digital factories will revolutionize how we manage innovation going forward. It shifts our thinking and the management of innovation dramatically.

The ability to have a more “rapid response, flexible manufacturing” can align more to market shifts earlier. We can begin to design products that are more “digitally connected” that can be updated in the consumer’s premise or hand. We can enhance “their” innovation by shortening the time-to-execution and extending the product life cycle, we can build new potential through greater innovation design, based on increasing insight and collaboration.

Growing the business in new ways, we are building revenue based on Industry 4.0.

Deloitte put out one report “Industry 4.0 and manufacturing ecosystems. Exploring the world of connected enterprises“ where it focuses on a few themes that point towards the benefits of IR4. One is growing the business through I4.0 and also integrating the digital and the physical to achieve business objectives

Growing the business comes through the transforming of engineering, research, design and achieving increased levels of customer engagement and interaction and achieving a greater co-design of the products themselves. The key is again through innovation to make already existing products smarter, offer more data relevancy from smart technology, the change to add completely new products and services in format or build alternatives in business model thinking.

We have the ability to apply a greater “intelligence” to marketing products, using analytics and predictive customer tools to draw closer to actual need. The opportunity to improve the aftermarket experiences, extend the life of the product and work towards maximizing uptime all have value. The use of sensors dramatically helps connect the product throughout its lifecycle to be constantly monitored, updated and managed in collaborative ways between the buyer and seller.

Application-based technology, based on connecting in new digital ways, can reduce the idea to market, be able to digitally link design to product intelligence and improve the overall effectiveness of having connected engineering. We can test, provide feedback on early design concepts, adjust them and redevelop their designs within our network of suppliers, customers, and the manufacturing.

Integrating the digital and the physical to achieve business objectives, the arrival of the digital twin.

Deloitte nicely put this as “It is the leap from digital back to physical—from connected, digital technologies to the creation of a physical object—that constitutes the essence of the Industry 4.0 concept”

We are at a point of combining information technology (IT) and operations technology (OT) to enable the creation and use of a digital twin for example and fuse our operating environment into one.

Deloitte points out “The value of a digital twin can allow companies to have a complete digital footprint of their products from design and development through the end of the product lifecycle. This, in turn, may enable them to understand not only the product as designed but also the system that built the product and how the product is used in the field. With the creation of the digital twin, companies may realize significant value in the areas of speed to market with a new product, improved operations, reduced defects, and emerging new business models to drive revenue.

The digital twin may enable companies to solve physical issues faster by detecting them sooner, predict outcomes to a much higher degree of accuracy, design and build better products, and, ultimately, better serve their customers. With this type of smart architecture design, companies may realize value and benefits iteratively and faster than ever before.

A digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behavior of a physical object or process that helps optimize business performance. The digital twin is based on massive, cumulative, real-time, real-world data measurements across an array of dimensions. These measurements can create an evolving profile of the object or process in the digital world that may provide important insights on system performance, leading to actions in the physical world such as a change in product design or manufacturing process”

“Indeed, the real power of a digital twin—and why it could matter so much—is that it can provide a near-real-time comprehensive linkage between the physical and digital worlds. It is likely because of this interactivity between the real and digital worlds of product or process that digital twins may promise richer models that yield more realistic and holistic measurements of unpredictability. And thanks to cheaper and more powerful computing capabilities, these interactive measurements can be analyzed with modern-day massive processing architectures and advanced algorithms for real-time predictive feedback and offline analysis. These can enable fundamental design and process changes that would almost certainly be unattainable through current methods”

The Deloitte report Industry 4.0 and the digital twin is a highly useful reference to read.

The present organizational inertia is stopping innovation to realize radically different benefits from Industry 4.0

There are so opportunities for innovation well beyond products and services. We require new approaches, new skills, different talent and design capability to realize and leverage the power of connecting coming from industry 4.0. It can offer us, for the first continuous learning, we can tap new a network of talent through ecosystems and platforms designed for greater collaboration and exchange. We can reach far more into underserved markets through greater customer connections and engagement, we can co-design with them.

We can, by embracing 4IR, have available far more predictive and evaluation tools to help improve processes, to reduce or test risk, to prototype and experiment, partly engaged with customers in learning and adjusting design and value. We can build and connect different supply chains that read and react, we can create new ecosystems where we share a common goal and value, we can enable more agile systems that can adapt and respond to changes in real time to reconfigure the ‘connected factory’.

Innovation emerges out of the Industrial 4.0 initiative in radically different ways to explore and exploit.

We are waiting for the fully connected up factory and process so we can apply a very different digital set of innovation tools. Until then we have innovation being managed the current linear, disconnected way.

We fail to have a seamless set of connections into our industrial process world, into our designers, engineers, research scientists and more importantly our customers. Until we all get the same “line of sight” innovation stumbles along its familiar “guessing game” derived of insight and underlying knowledge.

The quickly we connect up and build the fourth industrial revolution we can explore, build and redesign our whole innovation process. We can .look towards managing more radical innovation design in ecosystems and networks, that push existing boundaries, explore new technologies and materials, that all can combine into the Innovation Revolution that follows the Industrial Revolution.

The post Innovation cannot expand without the 4th Industrial Revolution first appeared on Building Your Innovation & Ecosystem Intelligence.

If you are not aware, it is worth reading about the Gordian Knot.

“For people the world over, the Gordian Knot represents the difficult, the intractable and often the insolvable problem.

Today’s systemic business problems are the modern-day equivalent of this seemingly impossible challenge, our Gordian Knots to untie or cut through.

According to Greek mythology, the huge, ball-like Turkish knot with no ends exposed was impossible to untie. An oracle had predicted that the first person to do so would become the ruler of all Asia. Thousands of people had tried, without success, to unlock its complex riddles. Alexander of Macedonia, son of King Philip II of Macedon, solved this puzzle simply and very creatively – by cutting it in half with his sword, exposing its ends and making it possible to untie. Alexander the Great went on to conquer all of Asia, just as the oracle predicted”.

So are Organizations Cutting their Gordian Knots?
So how can we cut the intractable knot inside organizations and thrive from it?
We all face increasing uncertainties and are having our trust battered. We live increasingly in a world of complexity. We are seemingly not rising to the challenges that seem to be growing in this complexity in the global, organizational and personal context.

For me it is attempting something new and different, can often undo these knots we find ourselves. We grab digital transformation as one of those solutions. Often that simply makes it even more complex unless we cut through the current impossible knots we have today.

Innovation has a vital role to play but it must be ‘treated’ differently.
Where do we start? Firstly we need to revisit any work we are thinking about changing more on an “adaptive enterprises and systems” way of thinking.
We need to find ways to deal with unpredictable, discontinuous change and make this more predictable for us to manage.

We need to be agile, fluid and adaptive in allowing design, systems, and processes to continually adjust, to reflect the constant challenge of change going on continuously.

We lack often a real coherence of purpose; we lurch from one opportunity to another, from one-quarter of financial results into another, reacting to that moment, often defensively, with often no longer-term consistency in our activities. Performance has been taken over by opportunistic behaviors. Innovation often takes the real brunt of this. It gets cut like it is the very knot that needs cutting!

Our intended strategy, moves suddenly, into more of a deliberate strategy, and then somehow this gets abandoned or unrealized. We keep responding and slowly we replace this with an emergent strategy that is rapidly become our realized strategy that we present to the world, as a response to ‘factors outside our control’ for our often indifferent performance. We are passing through different strategy responses without any coherence of purpose.

Who really talks about the EXACT results achieved from a specific innovation for instance? We might get financial numbers, we will get lots ot “hype” but we never get the recognition, that transforming validation of this success, maybe in rare cases. Why is that?All we hear is just a general reference to it, to cloak innovation often in the ineffectiveness that lies within. We bundle up our innovation activity, we cloak it in mystery, we fail to draw it out and give it the credit it is often due to our growth strategy. We rarely encourage it and give it the real recognition it deserves but we constantly “simply” talk about our innovation success.

I think innovation should be central to any events involving results and predicted future. Knowing the innovation capability is knowing the changes to a better future and shareholders are denied that. Do we have any clarity on innovation processes, are they sustaining or one-off, do we know the investment being made in building individuals innovation value. Do we break out innovation investments in capability, capacity or competence management. We should, as innovation grows a company, it is a major indicator of future success. It needs more “cutting open” and revealing, as “our” innovation management process.

For innovation and many things going on within organizations they often lack a real coherence, they ‘signal’ inconsistent behaviors, especially within the organization. These bad signals simply build up all the tensions and dysfunctional aspects that middle order management and the organization, in general, has to mop up and cope with.

How can innovation thrive in this sort of chaotic mess? No wonder it never takes hold, it just has nothing ‘permanent’ to attach too and grow. Management needs a radical overhaul so innovation can be the vehicle for what is articulated but often poorly delivered on a consistent basis.

Context & Coordination needs designing in purposefully.
Again, where do we start? We start with ‘disconnect’ that organizations often have. As we “cut through” the problems we need to take the different strands and piece them together. To make fresh knots to make a different strength. The understanding of the underlying purpose of the organization going beyond just making money and keeping the shareholders happy is that it does need a deeper primary function, it needs re-articulating, it needs constructing a new entity. Some organizations do a reasonable job of this but many simply don’t.

We need to restate our primary function and purpose far more in its societal contribution. We need to express the bounds as a governing set of principles that are known, reinforced and measured against. We then need to set about building in a consistent way, the capabilities that are able to produce the outcomes. Those new outcomes need innovative thinking to be central to them.

We need to build in the accountability into the roles that reflect that need to combine innovation, effectiveness, and efficiency, that is needed so they can be managed equally side by side. We need to teach the ability to be adaptable and recognize the differences so as to actively manage the ‘creative’ conflicts and tensions.

Innovation can help in cutting through existing “knotty” problems by searching for new strands that give fresh opportunity to be tied together and build strength beyond the existing. We need to cut through old knots but, yes, make new ones that form around some of the old and plenty of the new, “weaved” into them, to give them a greater strength of purpose and ability to take on new “knotty” problems, in different innovative ways.

The post The legend of the Gordian Knot and today’s organizations knotty problems first appeared on Building Your Innovation & Ecosystem Intelligence.

Let me provide a short narrative to give this a meaning and why it is becoming so important

Today we deliver content- It has become far too easy. We are drowning in it on a daily basis. We all suffer a massive deluge of digital input. Content can’t stand alone.

• What we need is context to anchor content and give it the clear meaning to understand………..that’s our necessary starting point.
  

Context shifts everything, it gives it shape, a structure to draw (deeper) meaning from. We learn to know, to integrate, to remember, to understand and to act.

Of course, context is going to be fluid as it builds out its related content.

Having content and context is complementary, interconnected, and interdependent, they interrelate to one another.

As we gain more insights we can potentially build a greater understanding. We simply improve our knowledge.

It is going to be needed to be adaptive as we learn but it will be placed in ‘certain bounds’. If we start from a much clearer starting base then the learning and discovery allow people to want to find solutions as they gain increased knowledge and provide fresh inputs, they begin to strongly relate. We pass through memory understanding as we learn.

“ Context provides the innovation marching orders” in my world

• We achieve a higher sensitivity to needs and understanding, we begin to relate
• We engage more, we can dialogue more, we ‘unite’ more in relating
• It adds more value to our thinking, it triggers additional ideas and contribution
• Functionality and Capability then follow from this- we build it differently

Context gives us certain guard rails or borders to help gain a more united focus. Where we go after that, by pushing those borders will depend on those involved, by us collectively seeing a greater value in an evolving context of mutual understanding.

• The ‘setting it in context’ allows for dialogue, adding more value to the thinking.

Context needs to also become, increasingly the arbitrator of success. Around this, you form your community that seeks out the possible solutions to this. Context gives knowledge into the hands of others to go and work on finding possible solutions.

• Knowing the context gives us something highly relevant to judge eventual outcomes on.

Q: How can we absorb and learn differently?

• We are overwhelmed by “content stock”
• We need to understand (increasingly) what is relevant to us
• How should we engage? What can make it easier?
• We need to build content creation for a context-specific world
• This needs to be increasingly tailored to our needs with help increasingly from (AI + Machine Learning)

Introducing the notion of Knowledge Graphs (KG) – its value and purpose

Here I wanted to give a deeper explanation of knowledge graphs- a ‘potted’ history and future view.

I wrote about Knowledge Graphs “The Arrival and Potential of Knowledge Graphs Into Our World” to give them a little depth of understanding.

So, my attempt here in this post 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. They help us relate faster.

KG will equally become a significant contributor to the industrial application as well, as increasingly it will build knowledge visually and be part of our everyday working life in related searches

I recently heard Knowledge Graphs will become as topical as “Artifical Intelligence” as a prediction for use in an industrial application.

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 much within AI

It is once we try to 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……….we become more creative, informed and innovative….so let’s look at these a little more:

Knowledge Graphs captures the relationship between people, processes, applications, data- the things and their relationships

KG makes the connections for “contextual information”

• We need to stop stringing together to build a picture of things, piece by piece
• We need a way to bring together contextual knowledge in greater collective forms

Connecting knowledge is one of our biggest challenges today

The more you “see” within the relationships the better the insight, to influence decisions, to set actions and directions.

This leads to “cognitive solutions”

Let take an Example from  Google Search

Google pioneered or advanced Knowledge Graphs- “the information box on the right-hand side”

Examples “Google”:  “Bertrand Russell” or “the “Mona Lisa” or as shown here “Leonardo da Vinci”

• We are linking data and semantics (well-defined, related), easy to query and explore
• We are linking relevant information- (threads) which we can build and add too, in time (AI) smart. We are building dependencies of understanding (content and context)
• It integrates (over time) with machine learning
• Knowledge = Structured = Connected Relationships = Entities

Outcomes are represented in knowledge graphs (visuals)

This gives us data visualization, logical reasoning, it represents and presents knowledge

So why KG? It is becoming essential for Enterprises to link Knowledge and Intelligence

Today it is estimated 90% of enterprise data is unstructured, it is hidden in textual form (documents and files). What if?

• It Organizes and Distributes Structured Knowledge
• It has Openness, Transparency, and Accessibility
• It is Collaborative, a Sharing and Exchanging ‘mechanism’
• It is Enriching and Contextualized
• It ‘bridges’ Enterprise Knowledge needs with Open Knowledge Learning

The technical know-how lies within Siemens today and this has been my initial catalyst to understanding its value.

The outcomes are full of helping us get organized:

Ontologies, Common Language builder, Builds Schema, Common Descriptors, Vocabularies

Turning A Data Aware to a Knowledge Ecosystem, linking data, information, into knowledge and building on this

It is full of Entities and Relationships, it is dynamic and grows by you making the connections to constantly improve.

If organized differently:  it can create a better customer experience, greater efficiencies, increased insights for more connected products and services and understanding across the whole enterprise value chain, internally informed, externally communicated.

It can build upon those “keepers of specialized knowledge” driven by domain experts and business users that use their (growing) insights to increase connections and subsequent value generation.

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.

It is this lack of connecting all of this knowledge up that is really placing demands on organizations that manage knowledge flow today, As we know it is often very inefficient to know where the knowledge actually lies.

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 with the use of Knowledge Graphs. It can have the incredible value of being driven by the domain experts and actual business users, as they make and use their knowledge to make the increased value of all the connections they have, increasingly in a growing network of relationships, constantly building a deeper domain knowledge.

It is a Learning Memory Builder

It gives us: What we know or need to know how we integrate, remember, to act and understand

• What is our challenge? (the need)
• The Suggested Solutions (the insights)
• Value Generation (empowered, equipped and enabled, to decide, to improve upon)

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 greater these become, and then the better you become, in being “informed” to act in more knowledgeable ways.

The post So the value we can derive from using Knowledge Graphs 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.

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?

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