We've now taken a rough view into Time, Reality, Style Types and dived into the three Strutcures…
Now I'll challenge the quality of the most critical type: Matter.
What does the innovation want?
How does it get it and what does it need?
An innovation needs energy to deal with problems of different complexity. There are three levels of problems: object building, object interaction and object positioning problems. From an external point of view the innovation can solve, but also create problems - internal problems need to be solved.
Big wins often come from creating problems. Innovations that make a big change often create a problem before they solve it (think of fashion…).
The struggle to get objects doing new things in new ways is the "heart and soul" of an innovation. Focusing on that struggle may make the differential advantage.
So, the first job an innovator has to do is to specify what the innovation wants, how it is going to meet the requirements and what it needs.
Matter Types
divided into
Purpose Types - what the innovation is for, what does it drive. Innovations may want to improve global external values, or extends, enrich (computational) domains knowledge for sectors...or enable better problem solving on various levels in various fields...
In the stone age of computing, we spoke of general or special purpose systems, assuming that there's a trade off between coverage and automation depth.
But this is not longer valid. Cascades of methodologies and technologies enable systems that are broad and deep…automated solutions and development systems in one. This motivated me to take another view into the matter types.
The macro point of view - the expressional forms of societies
Education, R&D, Economy, Social Affairs, Culture, Health, Ecology, Security, Defense, Finance, International Affairs…
The meso point of view - domains, fields, sectors and their main functions
Sciences, Engineering, Public, Supply, Information, Communication, Entertainment…doing funding, design, making, logistics, admin, marketing…related to usability, quality, risk, attraction…
The micro point of view - the basics for getting better results
Presentation, analytics, prediction, control, simulation, optimization
Thinking in tasks, innovations may support society tasks, performance tasks, action tasks, presentation tasks…and there are some internal aspects like worldview (changes the innovation wants to convey at a global level), status (changes at a target group level).
Status? At UnRisk, we have decided Arming Davids - in a market of Goliath able to justify huge spend on enterprise (risk) management how are the numerous small capital management houses to meet regulatory pressures whilst remaining cost effective.
Making big systems for the small follows a kind of Reverse Innovation (put enormous effort into making something for those who cannot pay much and sell the innovation to the big later). Big systems mean: identical functionality, but moderately massive data and consequently resource usage.
To make Reverse Innovation possible, you need the most advanced methodologies and technologies to start at.
Realization Types - how the innovation works and atop what systems it is built.
Characterize the architecture, designs, models, methods, critical implementations of your innovation and what technologies it utilizes.
Problem solving consists of using generic or ad hoc methods from mathematics, engineering, computer science, artificial intelligence…psychology…
An example of a critical decisions: taking a mathematical (abstract and re-concrete) or an engineering view (define and refine)….
In quantitative fields techniques are found in symbolic and numerical computation, stochastic methods, logic methods, optimization, machine learning, data and algorithms, control theory...cellular automata…or more general, the theory of complex systems
Quantitative problem solving is often characterized by reading (preprocessing) - computing (processing) - writing (post processing) cycles. This is typical for mathematical problem solving where we transform a textual description into a model - make the model of good nature for computation - calculate - interpret the results.
Consequently, it will be helpful (indispensable) using platforms and tools that provide such problem solving techniques (and work flows). In my experience, platforms are beneficial, if they support explorative learning as well as problem solving - they're fit for innovations the bottom-up fashion.
Some problems are difficult, when not fiendish…Inverse problems are ill-posed. In general, creating data from models is easier, than extracting models from data. Think of Computer graphics vs image processing.
There are so many aspects that it needs aggregation and typology. It took me many years to roughly understand this and what the key points are.
In my evaluation work for the EC I was quite often confronted with a principle misunderstanding: this is a great innovation! But what's it for? For this and that! But how will it be exploited? (I find Graphene a fantastic innovation. But after a time of enthusiasm...innovation analysts are asking: "What's for?").
We can't master the full complexity of the innovation assessment (even when focusing on quantitative problems) without trying to map it into a mesh of types.
How they're built, what they're for, how they effect work, how users experience them, how real they are and how they synchronize to real world...real life behavior?
Next up…more about conventions and techniques realizing innovations that are fit for purpose and optimize market risk. Will we be possible to write this all down in "one" page?
