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From Information to Knowledge, Wisdom


🎭 Summary & Indices Elucidation 👁 Foreword Vitae 🎭
👐 C-Steer C-Serve C-Shape 👁 I-C6isr I-Jabes I-Know👐
👐 r-steer r-serve r-shape 👁 r-c6isr r-jabes r-know👐
🔰 Contents Mesh ABCs Control ALC-V* Polestar 🔰
  
🚧  Variety Act on Cyber Change ALC-V* knowtc 🚧
  
🎯 Algol Interact Tenets Change Volatile North 🎯

C-1 Processes & Organisations


C-1.1 Contents

⚙ C-1.1.1 Global content
Mindmap start design bpm design math devops meta design data design meta The most technical topics, not ready in their basic nature for operations, are slowly made ready for getting used at operations.

Visions as futurists from the business leaders are not always sensible approaches. Mostly they are stressing hypes.

🔰 Too fast .. previous.
feel_order_02 escher
🎭 C-1.1.2 Guide reading this page
No silver bullet
There is a desire for simple fast busyness solutions for the incomprehensible challenges needing a solution. Instead of analysing and understanding the issues the easy way is to mention new hyping technologies although they are not proven or researched.
The content has changed from just technology into understanding that and how to apply it in wisdom. This is not easy and will be hard work.
The circular walk through in this site by topics is broken at the edges. Diagonal connections replaces the normal vertical lines. When hitting this page (r-math) from C-Steer that path has been followed.
Understanding the mindset
It all started with an idea for a product, Jabes. It changed into trying to understand why it is that difficult for realisation.
The Jabes framework and products is about:
6w 1 how
Required to read this page
There is some jargon on his page. That jargon part is not really relevant, only helful in understanding context.
The switch in defining context for a theoretical story into context for a detailed practical realisation is important. Wisdom is not from the unproven theory but from proven realisations.
The mindset, philosophy to understand:
⚙ C-1.1.3 Local content
Reference Squad Abbrevation
C-1 Processes & Organisations
C-1.1 Contents contents Contents
C-1.1.1 Global content
C-1.1.2 Guide reading this page
C-1.1.3 Local content
C-1.1.4 Progress
C-1.2 Communication - Interactions knowhr_02 Mesh
C-1.2.1 Communicating Information
C-1.2.2 The technical ICT world
C-1.2.3 Reuse of validated and/or prepared information
C-1.3 Historical evolvements knowhr_03 ABCs
C-1.3.1 New hyping technologies
C-1.3.2 Programming languages
C-1.3.3 Deming principles, out of the crisis
C-1.4 Processing flows VSM - assembly lines knowhr_04 Control
C-1.4.1 Break-up: Logic, Concept, Context
C-1.4.2 Design abstractions, John A. Zachman
C-1.4.3 Design abstractions, understanding Chris L
C-1.5 Processes Building Blocks Basics knowhr_05 ALC-V*
C-1.5.1 Data information Knowledge Wisdom
C-1.5.2 DIKW alternative lines
C-1.5.3 DIKW into Jabes process views
C-1.6 Organisation & Business knowledge knowhr_06 Polestar
C-1.6.1 Cultural differences, interpretation
C-1.6.2 Cultural differences, behavior
C-1.6.3 Interpreting cultural impact
C-2 How, build technology
C-2.1 Challenges 6w1h site knowtc_01 Variety
C-2.1.1 Technology understanding Html
C-2.1.2 Technology Responsive Web html
C-2.1.3 Technology Web html Notes
C-2.2 Communication - Interactions knowtc_02 Act on
C-2.2.1 Guide reading these pages, site
C-2.2.2 Presentation Windows Sizing - Positioning
C-2.2.3 Presentation logical setup - Positioning
C-2.3 Historical evolvement ICT knowtc_03 Cyber
C-2.3.1 Probability distributions
C-2.3.2 Computer Technology - decisions algorithms
C-2.3.3 Decisions algorithms - statistical science
C-2.3.4 Fraud detection & technology abuse
C-2.4 Processing flows VSM - Change knowtc_04 Change
C-2.4.1 Retrospective 6w1H
C-2.4.2 Zachman Overhauling Ideate
C-2.4.3 Zachman Overhauled objectives
C-2.5 Technical understanding processes knowtc_05 ALC-V*
C-2.5.1 Learning from examples, Zachman overhauled
C-2.5.2 PDCA in another context, SABSA
C-2.5.3 Design process details
C-2.5.4 Basic building blocks categorisation
C-2.6 Organisation & Business Understanding knowtc_06 knowtc
C-2.6.1 Retrospective Using 6w1h
C-2.6.2 Who does product management?
C-2.6.3 Classic product management, guided by principles
C-2.6.4 Product lines next level
C-3 Realisations by Wisdom
C-3.1 Miscellaneous Practical Knowledge knowdo_01 Algol
C-3.1.1 Presentation Windows Sizing - Positioning
C-3.1.2 Zachman Overhauling Ideate
C-3.1.3 What can we learn, 6*1 - 2*3?
C-3.2 Communication - Interactions knowdo_02 Interact
C-3.2.1 Learning from examples, Zachman overhauled
C-3.2.2 PDCA in another context, SABSA
C-3.2.3 Goals feeding decisisons
C-3.3 Historical Practical Evolvement knowdo_03 Tenets
C-3.3.1 Exploding number of frameworkd & tools
C-3.3.2 Change: Culture Leadership
C-3.3.3 Visions future
C-3.4 Processing flows VSM - Control knowdo_04 Change
C-3.4.1 JABSA reused principles
C-3.4.2 JABSA Designing, building the organisation
C-3.4.3 JABSA applied to information processing
C-3.5 Processes Building Blocks - Control knowdo_05 Volatile
C-3.5.1 JABSA for Data-Information, data governace
C-3.5.2 JABSA for services, processes transformational flows
C-3.5.3 JABSA aligning the organisation to product flows
C-3.6 Controlling Organisation & Business knowdo_06 North
C-3.6.1 The Five Organizational Systems
C-3.6.2 Scale or flow a system
C-3.6.3 Jabes - Jabsa mindsets
C-3.6.4 Ongoing to do it
⚒ C-1.1.4 Progress
done and currently working on:
Planning to do:
Ai missing ML

C-1.2 Communication - Interactions

Working with machines that process information, is a relative new topic of science. Human communications and interaction is classic.
The concept of the "information" container is not that clear and simple.
Information is an abstract concept that refers to that which has the power to inform. At the most fundamental level, information pertains to the interpretation (perhaps formally) of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any medium can be said to convey some amount of information.
🎭 C-1.2.1 Communicating Information
ICT is using a own language that makes it difficult to communicate. There is an intro for understanding common terms choosing options: it-terms Information technology is the study and use of computer systems to store, access, process, and share information. IT professionals design, build, install, and maintain organizations’ hardware and software systems. Whether you’re preparing to earn an IT certification or interview for a new role, studying these technology terms and definitions can help you feel more confident and ready.
🎭 C-1.2.2 The technical ICT world
Data representation.
Information is translated and transformed in many phases. What the intention and meaning is, is needing a common ground. In the world of using computers everything is digitized, assuming just two values 0 and 1 (binary) The size of any measurement is having limits. In the technical approach understanding those is necessary.

nls_eunl1
Characters, National Language support
Morse and the first realisations the 8-bit single byte as basic unit allowing for 256 (0-255) characters. Ebcdic and Ascii two major competitors for the translation of what number to what symbol is intended.
The number of possible symbols was far too low to satisfy everybody. One solution for that is using different code pages. That is the same number can have a different meaning when used in an other location. Transcoding between codepages is loosing information by not having the all of the same symbols.

Base64 is a ay to convert all 256 binary values into 64 common used characters. The goal is avoiding unwanted effects while doing an exact binary copy.
This single byte representation still is often used.

unicode technical Unicode is a different approach, it has abandoned that single byte approach. There are five ways of how to code and there are different versions of the standard. Those different versions is known with the emoticons being added as new ones. Transcoding, converting those numbers to have the correct meaning, is a hidden problem cause. Sometimes the replacement char is shown.
UTF-8, UTF-16, UTF-32 & BOM Basic Unicode tables: A new issue to deal with: not every binary combination is valid!
Data information has to be relasised with the appropiate characterset in use.
nls_afst1
Text, National Language support
It doesn´t stop having the right characters, there are much more conventions that are having language differences. One of those the ordering left to right or right to left.
another important one is the decimal point, instead of a point a comma is often used. Other measures and units also exist.
nls_eugr1 The byte-order mark (BOM) in HTML (w3.org) You can see that the order of the two bytes that represent a single character is reversed for big endian vs. little endian storage. The byte-order mark indicates which order is used, so that applications can immediately decode the content.
nls_chmk1
Dates time, National Language support
iso8601
When dates are represented with numbers they can be interpreted in different ways. For example, 01/05/12 could mean January 5, 2012, or May 1, 2012. On an individual level this uncertainty can be very frustrating, in a business context it can be very expensive.
Organizing meetings and deliveries, writing contracts and buying airplane tickets can be very difficult when the date is unclear. ISO 8601 tackles this uncertainty by setting out an internationally agreed way to represent dates

rekenlineaal
Number Data elements.
Real world numbers are floating numbers, analog with uncertainties. Impossible to implement those in a digital representation only some aspects covered.
An old type, packed decimal, stores two numbers in a single byte. Using the first 10 out of 16 hex values.
⚠ License numbers (cars) and bank account nummbers are NOT number types. They contain other symbols and are not used to calculate a sum or mean.
The 8-bit single byte as basic unit. Unsigned and Signed in two´s complement. EB explanation.
Working with real life numbers is the technical floating representation. It is hardware dependent, not bound to a programming language. IEEE_754 (wikipedia)
⚠ note the limit of 15 (12 are save) decimal digits.
⚠ loss of precision is a cause for surprising not valid results.
🎭 C-1.2.3 Reuse of validated and/or prepared information
In information processing there are several stages for vital product deliveries:
A product can be build from many prepared components. With the scope on assembling manufacturing the product there is communication with alignment needed for verified materials and prepared components. There a two moments for an exchange, reuse in the flows, these are:
  1. used verified raw materials
  2. used defined components
Mixing these two will create a chaotic unclear situation for flows.
Simple exchange one product one product line in a figure: Process artifactshare1
Whether the process in the information is a simple recipe, classic instructions, or diffuse complex (algorithm analytics) one, doesn't really matter. Advanced exchange multiple products one several product lines in a figure: Process artifactshare2
😉 Operations: Reusing verified material & prepared components is not common practice.
😉 Analytics: Reusing verified material & prepared components is practiced but having many issues. There are at least three types of analytical information processing. These are:
  1. Managing closed loops in the operational plane.
    Optimizing flows in real time. They are not easy visible.
  2. Using for adjustments by decisions on the information flows over the days.
    Presented by regular dashboards and reports. Product managers are decision makers.
  3. Using for adjustments by decisions on the prodcut and flows over months.
    Presented by oneoff dashboards and reports. Executives are decision makers.
rethink what has happened TN

C-1.3 Historical evolvements

Learning from examples is the start how it should be done.
Practising experimenting is the next stage.
Goal: innovating extending improving how it should be done.
The good message: there is a prospect for improvement.
The bad message: it will not be easy.
Descriptive understanding:
Information is often processed iteratively: Data available at one step are processed into information to be interpreted and processed at the next step.
For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information relevant to the phrase it is part of, each phrase conveys information relevant to the sentence it is part of, and so on until at the final step information is interpreted and becomes knowledge in a given domain.
📚 C-1.3.1 New hyping technologies
Supercomputers
supercomputer_38 The fastest greatest EU industry ministers have signed off on a eur 1 billion project to build the world´s fastest computer by 2023 in an effort to compete with China, the US, and Japan. According to the commision, the new initiative is "crucial for the EU´s competitiveness and independence in the data economy."
The article is from 2018, a 5 year plan. It didn´t materialize, replaced by new hypes.
New world-class European supercomputer inaugurated in Spain (12-2023)
Blockchain
blockchain_38 Blockchain is one of the fastest-growing technologies in today´s digital world that have come into the limelight in the last few years. Undoubtedly, we have heard that this technology has revolutionized finance, real estate, and many other sectors. Depending on who you ask, blockchain technology is poised to revolutionize the world — from creating a universal currency to building a free and truly private internet. Or, the new technology, built with a combination of encryption and transparency, is a solution in search of a problem.
... The reality likely falls somewhere in between. While a growing number of startups and researchers are devoting themselves to exploring blockchain´s full potential, experts caution that a healthy dose of skepticism is needed to fully evaluate the technology and its eventual place in society. ... Simply put, a blockchain is a ledger. But unlike an old-time hotel register gathering dust on a counter, a blockchain ledger is held electronically in multiple locations across the internet. It is visible to any member of the community participating in that particular blockchain.
🤔 The Princeton article is from dec 2018. It didn´t got a real follow up. There is bitcoin hype, but some scandal already have happened.
🤔 Blockchain was the promise solving anything in data governance in a way like bitcoin. Maybe it is more useful when using the axioma´s in an other way solving the data governance issues.
Sam Bankman-Fried Strange enough this resulted not in a breakdown like the Enron scandal. To put in simple I don´t believe in a generated computer number has a financial value by his own. Having alls history in a chain ia a valauble concept in a classic ledger.
Mini boards
Raspberry_38 The RaspberryPi is a series of small single-board computers developed in the United Kingdom by the Raspberry Pi Foundation to promote teaching of basic computer science in schools and in developing countries.
Started as education (2010) it has become real valuable industrial (2024) Rack mount Pi Revolution Pi is an open, modular and inexpensive industrial PC based on the well-known Raspberry Pi. Housed in a slim DIN-rail housing, the three available base modules can be seamlessly expanded by a variety of suitable I/O modules and fieldbus gateways. The miniaturisation enables a lot more for devices.
Drones Robots
Quadcopter_38 Quadcopter_38 In the last few decades, small-scale unmanned aerial vehicles have been used for many applications. The need for aircraft with greater maneuverability and hovering ability has led to a rise in quadcopter research. The four-rotor design allows quadcopters to be relatively simple in design yet highly reliable and maneuverable.
These are getting much attention because becoming profitable in many areas including war.
LLM AI Bots
OPen Ai openai "Safely aligning powerful AI systems is one of the most important unsolved problems for our mission. Techniques like learning from human feedback are helping us get closer, and we are actively researching new techniques to help us fill the gaps."
Altough bots and language were getting much attention there was not a profitable reliable generic solution. This one change the game it is offered for generic usage in the Edge browser.
Implementing bots for replacing human helpdeks is a more differnt difficult topic. Trainging of te models is done at cloud offerings at increasing cost.
Storage
hadoopparquet_38
We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem. Parquet is built from the ground up with complex nested data structures in mind, and uses the record shredding and assembly algorithm described in the Dremel paper. We believe this approach is superior to simple flattening of nested name spaces.
feel_geniaal_02 lab
🎭 C-1.3.2 Programming languages
Algol Cobol Fortran Rexx JCL (3gl)
In the anciënt days when computers were far more expensive than the cost of labour to program them, you had to think very well do desktop testing before being allowed to run something.
The language mandatory for use: The most important part: the business logic.

😱 Once documentation what the intention and meaning of the process of the logic is was highly valuated. The believe that intentions and meaning could be dropped with the argument of cost saving resulted in not understandable not understood process flows.
Reengineering what was not documented well not understood well, is hard costly work.
C, C++ SQL Java HTML bash (3gl)
Most modern programming languages - C, C++, VB, Delphi, Java, Python, and even Perl - use syntax and concepts that originated with the AlgolLanguage. So they´re called AlgolFamily languages.
In an Algol-like language, with semicolons as statement separators, the semicolon is often used as a synchronizing word.
A move has made to focus on the language itself as the solution for solving business problems. That is weird.
R Python julia SPSS Watson matlab SAS Powershell (4gl)
These are new and older languages. With these it is about all those packages (modules) you can use and run. Smart construct for object attributes with defined properties are less or more included. The programming effort is decreased but new dependies are introduced .
low code approaches. New names are Mendix and Outsystems. Additionally, some CIOs have expressed concern that adopting low-code development platforms internally could lead to an increase in unsupported applications built by shadow IT. For shadow IT:
Some people consider shadow IT an important source of innovation, and such systems may become prototypes for future approved IT solutions. On the other hand, shadow IT solutions are not very often in line with organizational requirements for control, documentation, security, reliability, etc.
Spreadsheets (Excel), powerBI are popular for shadow ICT.
5 GL
With 5 GL approache the programming languages themself are getting hidden by more logic high level constructs. It is the prmise wiht AI hype this will be more and more being used decreasing the programing efforts.
📚 C-1.3.3 Deming principles, out of the crisis
94 system / 6 other
Systems thinking The System Of Profound Knowledge (SoPK) is the culmination of W. Edwards Deming’s work on management. The four areas of the system are: ..
Taking a systems approach results in management viewing the organization in terms of many internal and external interrelated connections and interactions, as opposed to discrete and independent departments or processes governed by various chains of command. When all the connections and interactions are working together to accomplish a shared aim, a business can achieve tremendous results—from improving the quality of its products and services, to raising the entire esprit de corps of a company.
A system view of the organization views the flow of the processes to create products and services. The interactions between various processes is respected.
A system view helps create a long term focus. Rather than seeing incidents as isolated (and often looking for the person to blame for a bad result) a system view allows managers to focus on the systemic drivers of results. For Dr. Deming the purpose of an organization was to create a system that provides benefits to all stakeholders, page 51 of the New Economics:
➡ The aim proposed here for any organization is for everybody to gain – stockholders, employees, suppliers, customers, community, the environment – over the long term.
What are the experiments about?
With references to operational research, very mathematical approaches trusting numbers as assumption, there is something unexpected.
Dr. Deming continually increased the percentage of problems attributable to the system instead of to special causes (outside of the system) such as blaming a person for a mistake. Obviously that doesn’t mean those problems are inevitable, it just means that the most effective way to improve and avoid those issues in the future is to improve the system. Page 315:
➡ I should estimate that in my experience most troubles and most possibilities for improvement add up to the proportions something like this:
The idea is that the data seen is based on just a stochastic probability event.
💣 Blaming the worker on outcomes by probabilities is bad, very bad.
💣 Trying to modify a probabilistic outcome on just an event will result in worse results.
In short: "a bad system will beat a good person every time."
under construction
What happened causing this unexpected approach?
When this was not bore his involvement at Toyoata in Japan there must be influence what has heppened there causing this. The culture is quite different and he worked a logere period achieving his succes there. Culture is hard to underestimate in resulting effects.
SIAR cycle

C-1.4 Processing flows VSM - assembly lines

A swarm organisation, self organisation, are networked structures without leaderships. Using some shared goal.
⚠ Challenges: have a shared goal, have a good shared goal.
The organisation structure is a hierarchical line of command. Formation in groups using leaders is human nature.
⚠ Challenges: avoiding leaderships going into micro details.
Expected is authority and acountability for a product in place.
😱 Administrative/cyber setting: seems to have got lost.
🎭 C-1.4.1 Break-up: Logic, Concept, Context
The words Logic, Concept, Context are from the Zachman framework.
In the logic middle there is a switch in understanding for approaches. The same topics are translated into another world.
More easy to understand for functional requests: More easy for technical realisations: The oldest figure I found for the zachman framework: Zachman 6W-s no W for which technology
🤔 The five levels are having a complication they are grouped in two categories.
For a simple realisation the details are the enablement. When it is about just an intermedatiate analyses, new context is the result.
⚖ C-1.4.2 Design abstractions, John A. Zachman
"Enterprise Architecture Defined: Architecture Abstractions" Business Rules Journal, Vol. 22 Enterprise Architecture Defined: Architecture Abstractions
You can classify the set of descriptive representations of anything (buildings, airplanes, locomotives, battleships, computers, etc.) in a two-dimensional classification structure, a "schema." One dimension of the classification I call "Abstractions" …
I chose to call this dimension of the classification Abstractions because you can abstract out, or separate out, or factor out a set of six single, independent variables or focal points of descriptions that are common to every architected object. Zachman_tech The architectural descriptions of anything:
  1. Bills of Material,
  2. Functional Specs,
  3. Drawings (or Geometry),
  4. Operating Instructions,
  5. Timing Diagrams,
  6. Design Objectives.
It is not mysterious why the people who build buildings, airplanes, battleships, locomotives, computers, all the Industrial Age products that are sufficiently complex to warrant Architecture came up with that set of description representations.

🤔 These six categories are for assembling a product. The design approach is another level, this is filling these descriptions. The ordering of classification is far better to understand then the orirginal 5W1H columns.
They are answering the six primitive interrogatives that constitute the total set of questions that have to be answered to have a complete description of anything: What, How, Where, Who, When, and Why. 🤔 These six categories are more generic, same order, but failing in associations in specified situations.

This goes back about 7,000 years to the origins of language … and by the way, I did not invent this classification. It has been well-exercised by humanity for thousands of years. If you don't answer all six primitive interrogatives it means that your description is incomplete.
Any of the six primitive interrogatives that is not explicitly answered relative to the descriptions of any subject or object (or if any one is not completely answered — Enterprise-wide at excruciating level of detail) simply means that the answers, the descriptive representations, are incomplete. The unanswered interrogative or portion of interrogative was never made explicit … therefore, it is implicit … which means that assumptions are being made. Those assumptions might be right … and they might be wrong. Erroneous assumptions in tangible, Industrial Age products are the sources of defects. Erroneous assumptions in intangible subjects like Enterprises are the source of miscommunication and misinterpretations.

But, clearly, there is one and only thing expressed in each of the 'Abstractions'. This is really important for engineering work. You are trying to "normalize" each characteristic. There is an engineering cliche, something like "an elegance in simplicity." You want to minimize redundancy except where explicitly controlled because redundancy increases complexity which affects manufacturing, operations, maintenance, performance, costs … the entire spectrum of the existence of the product. The only way to "normalize" the contents of any one Cell of the Framework is to see the total set of occurrences for any one 'abstraction' in the context of the entire object.
🤔 I would not reduce redundancy when redundancy is a result of simplicity. Adding complexity because of dogma is a wrong assumption.
There is another important observation to be made about this classification of descriptive representations of Industrial products:
In contrast, for manufacturing, you need an entirely different kind of description. For any one part, you need to know its structural relationship with any other parts, the functionality of the part, the geometry of the part, the operational responsibilities for the part, the cyclical (timing) characteristics of the part, and the part's design objectives. In short, for manufacturing, it would be optimal to make explicit every one of the Abstraction characteristics deriving from the six primitive interrogatives for any one individual part. Any characteristic that is not explicit is implicit, and therefore assumptions have to be made … subject to error.
Furthermore, for manufacturing, it is useful to decompose the end product into smaller parts. The smaller the part, the faster and more cheaply it can be made. A one-dimensional classification (a hierarchy or taxonomy) is very useful for manufacturing. However, in a one-dimensional classification, the same content can be classified in more than one category as evidenced in the biological taxonomy. There is a multiple inheritance problem. The categories are 'de-normalized'.
🤔 A well chosen definition unique key set for categories should solve the de-normalized issue.
🤔 The segreagation of manufacturing from design does not make sense. During design the question of how it is manufactured is common question to solve.

About that time, Ted Codd was floating around with his 'relational model', a TWO-dimensional schema … normalization! ... .... which was the automation of the Codd relational model. It later became what we know now as DB2. The product almost never saw the light of day! The performance was dismal, and therefore, management thought no customer would ever buy it. But, they imported the three Chrises from the Hursley Lab.
Chris L used my Framework in the book to argue the point that if you want to build a system with acceptable performance (Column 5), you have to understand the locations of the Enterprise at Rows 1 & 2 before you decide where to put the data (Column 1), the processing (Column 2), and the screen formatting (Column 4).
The performance (Column 5) is determined by the integration of Column 1 (data), Column 2 (processing), Column 4 (Screen, i.e., "Work Product" formatting) with Column 3 (location).
If you wait until you are writing the code at Row 5 (Physical) to decide these Location characteristics, you will produce a system implementation that is a 'dog', a performance nightmare … I elaborated Chris' point after 20 or 25 more years of experience using the Framework Ontology … and I have not yet completed the definition of the Framework structure for you nor have I discussed the subject of Ontologies. ... Location is the determining factor for "performance" as manifest in response times. The same concept is as applicable internal to a machine (computer) as it is external to the computer in the Enterprise.
⚙ C-1.4.3 Design abstractions, understanding Chris L
Trying to translate the notes on Chris L because there are ohter words with dedciated meanings. A too high generalisation will loose information to understand what is going on.
Zachman basic engineering Context situation: ICT platform
What Bills of Material ⇄ Data
How Functional Specs ⇄ Information transformations
Where Drawings (or Geometry) ⇄ Location technology
Who Operating Instructions ⇄ Screen, user interactions
When Timing Diagrams ⇄ Performance
Why Design Objectives ⇄ Platform functionality

🤔 The performance remark: don't copy data unnecessary, avoid overhead.
elephant-blind-men

C-1.5 Processes Building Blocks Basics

The term elephant test refers to situations in which an idea or thing, "is hard to describe, but instantly recognizable when spotted"
A process life cycle building block, ALC life cycle, is very generic en simplistic. There are only three possible approaches.
To solve:
😱 PM: project management is ❌ NOT product management.
😱 ALC life cycles are made complicated by blame games.
🎭 C-1.5.1 Data information Knowledge Wisdom
The DIKW model is often quoted, or used implicitly, in definitions of data, information and knowledge in the information management, information systems and knowledge management literatures, but there has been limited direct discussion of the hierarchy. A discussion adding context for understanding: Understanding ; Data, Knowledge, Information & Wisdom
Ever heard of the DIKW pyramid? It stands for the Data / Information / Knowledge / Wisdom pyramid. Sometimes it is also referenced as “DIKW Hierarchy”, “Wisdom Hierarchy”, “Knowledge Hierarchy”, “Information Hierarchy” or “Knowledge Pyramid”.
dikw vaes
Although it is uncertain when and by whom those relationships were first presented, the ubiquity of the notion of a hierarchy is embedded in the use of the acronym DIKW as a shorthand representation for the data-to-information-to-knowledge-to-wisdom transformation.

dikuw_vaes
Data is conceived of as symbols or signs, representing stimuli or signals.
Information is defined as data that are endowed with meaning and purpose.
Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations it often becomes embedded not only in documents and repositories but also in organizational routines, processes, practices and norms. Wisdom is the ability to increase effectiveness.
Wisdom adds value, which requires the mental function that we call judgment. The ethical and aesthetic values that this implies are inherent to the actor and are unique and personal.
🤔 There are several interesting ideas in this. These are:
🎭 C-1.5.2 DIKW alternative lines
Applying the extended DIKW line wiht understanding and measurements into six columns of Zachman, applying dedicated meaning. This is a possible innovative approach for categorising and engineering data, information processing.
Zachman basic engineering Functional Processing Operations
What Bills of Material ⇄ Data Measurements Data storage
How Functional Specs ⇄ Information Data Data integration
Where Drawings (or Geometry) ⇄ Knowledge Information Reference data management
Who Operating Instructions ⇄ Insight Knowledge Data quality
When Timing Diagrams ⇄ Wisdom Understanding Data modeling
Why Design Objectives ⇄ Impact ❶ Application Goal
❷ decision support 		❸ Data governance
❹ Archive History

🎭 C-1.5.3 DIKW into Jabes process views
JAbes proces views
There are several perspectives for processes There is a reduction from six columns into three areas by this classification.
Jabes Dikw why
Adding additional attributes to: Data, Information, Knowledge, Wisdom.
New: Request / Desire, - , Activities, Results, Evaluate. When put in the Jabes cycle:
Jabes_process_why
Jabes Dikw how
When the focus is on how to do technical data processing:
Jabes_process_how
Jabes Dikw what
Organising the processes:
Jabes_process_what
Data monetizing journey

C-1.6 Organisation & Business knowledge

Once Dorothy and her colleagues made the journey to OZ, they quickly found out that there was no there, there. The Wizard simply told her what she really should have known all along.
Dorothy and her companions just had to figure out how to reframe their own perceived shortcomings and recast them as strengths to achieve real transformation.
⚙ C-1.6.1 Cultural differences, interpretation
Japanese culture
I made a lot of references tot lean, agile. The most important cultural origin for that is Japan. I used a German source to understand what is lean about. What is to avoid with this are associations to: emotions with political culture, history, believes & viewpoints.
The same is to avoid when reviewing historical evolvements. Prejudice is ethical bad.
The_Great_Wave_off_Kanagawa
The Great Wave off Kanagawa has been described as "possibly the most reproduced image in the history of all art" as well as being a contender for the "most famous artwork in Japanese history". ... The print, though simple in appearance to the viewer, is the result of a lengthy process of methodical reflection.
The Great Wave off Kanagawa
The Japanese interpret The Great Wave off Kanagawa from right to left, emphasising the danger posed by the enormous wave. This is traditional for Japanese paintings, as Japanese script is also read from right to left.
Note the difference in directions of the interpretation. The story being told by that fom the figure is interpreted different although the figure is exactly the same.
⚙ C-1.6.2 Cultural differences, behavior
Just a start wiht a assumption the ordering is showing what is felt important. In many ways, Japanese culture is rich, and attempting to shed light on some of its concepts in this article will not do justice. However, I feel compelled to share nuggets of its pearls of wisdom, especially since the world is becoming more interconnected. 10 japanese concepts (Hairej Younes)
Omoiyari
Means caring and showing sincere consideration for others. Practicing omoiyari is said to help build compassion and empathy toward others.
Ikigai
Ikigai is the Japanese term for the state of well-being induced by devotion to enjoyable activities, which leads to a sense of fulfillment, according to Japanese psychologist Michiko Kumano. Your ikigai is what gets you up every morning and keeps you going.
Wabi-sabi
A concept that encourages us to embrace our imperfections and accept the natural cycle of life.
Everything in life, including us, is in a state of flux. Change is the only constant, everything is transient, and nothing is ever complete. By practicing wabi-sabi, we are taught to be grateful and accepting and strive for excellence rather than perfection.
Mottainai
Means respecting the resources we have, not wasting them, and using them with a sense of gratitude. The concept invites us to be grateful and intentional about our actions and think of ways to help make this world more sustainable.
Shin-Gi-Tai
Mind, technique, and body are the three elements for maximum performance used in martial arts. The concepts can be applied to any domain. Take, for example, chess. Your performance is not solely dependent on your chess skills at the board. Winning also requires a mindset that can cope with stress and setbacks during hours of uninterrupted concentration.
A healthy body and a sound mind are the foundation for developing and refining any skills.
Shu-Ha-Ri
It is a way of thinking about how to learn and master a technique. There are 3 stages to acquiring knowledge: “When the student is ready the teacher will appear. When the student is truly ready… The teacher will Disappear.” ― Tao Te Ching
Kaizen
A method of continuous improvement based on the theory that small, ongoing positive changes can be significant. Reminds us to let go of assumptions and perfectionism. It teaches us to take an iterative, progressive approach to change.
Mono no aware
This concept describes having empathy towards things and their inevitable passing. This concept reminds us that nothing in life is permanent. We should willingly and gracefully let go of our attachments to transient things.
Omotenashi
Is about offering the best service without expecting a reward.
Ho-Ren-So
“report, inform, and consult.”
The Japanese argument is that the Ho-Ren-So, through collaboration and communication, strengthens subordinate employee relationships and provides a platform for the subordinate to learn from their superior. It is good practice to encourage everyone to report issues and problems immediately. Even if a solution is not found, the cost of a problem that is not reported can be high.
⟲ C-1.6.3 Interpreting cultural impact
⚙ Lean PDCA
The PDCA (Plan-Do-Check-Act) has a long history but at some moment when going to japanese culture came back with changed concepts, context. 25 Years after W. Edwards Deming
Allaboutlean deming pdca
in a figure:
See right side
japan japan Context Deming
Plan ⇄ Yotei pre- act of decide or define. Make - refine - schedule/plan - execute design
Do ⇄ Suru versatile: "to do", "to perform", also "add" (pull-push)produce
Check ⇄ chekku examine in order to determine its accuracy, quality, or condition sell
Act ⇄ akushon the fact or process of doing something, typically to achieve an aim redesign
⚖ Interpreting the figure
What is there to see:
🤔 "check" instead of "sell" telling: closed loop,a service mindset (Omoiyari) instead of earning.
🤔 this figure is in a cultural conflict: 🤔 It is unclear how to align the pulll and push. Following the invisible flow right to left the push is: do-check and the pull act-plan. Starting with the letter P in that case is a cultural result. Starting with the A APDC would make more sense.
⚒ Applying the figure
Defining a process in four phases is no classic culture. The Five Maps of Flow Engineering
SIAR process cycle focus simple Future State Mapping happens in four stages:
Context
Act ⇄ IV Review the target outcome and findings from previous maps.
Plan ⇄ III Identify targets for improvement.
Do ⇄ I Redesign the stream.
Check ⇄ II Measure the future state.
The acceptance to do that is steep because when visualised the starting & end point are not intuitive when the flow is leading.

There are five stages to Outcome Mapping:
  1. Defining the Target Outcome: What goal(s) do we want to achieve?
  2. Defining Obstacles: What could get in the way?
  3. Defining Next Steps: How are we going to proceed?
  5. Defining Benefits: Why does this outcome matter?
  6. Outcome Discovery: What is our target?
😉 I changed the order and added a mapping. By this it is a fit with the six zachman columns. The missing column is the one with who roles in the organisation, operating instructions.
🔰 Contents Mesh ABCs Control ALC-V* Polestar 🔰
  
🚧  Variety Act on Cyber Change ALC-V* knowtc 🚧
  
🎯 Algol Interact Tenets Change Volatile North 🎯

C-2 How, build technology


meandering path

C-2.1 Challenges 6w1h site

The technology using computers has several lines of evolvements. The hardware has become faster, better, cheaper. Application software has a few basic fundaments in logic by flows and constructs.
The problem to solve has moved from a pure technical aspect how to run machines into how to process information in a technical way. Practical questions & answers on technology for this webpage.
⚙ C-2.1.1 Technology understanding Html
Website building.
> I am trying to build this site by coding just plain html and css. There are tools helping in coding the html other are generating code using templates.
There are many frameworks like WordPress Magento and interfaces with advanced packages. They all claim to make the work easier.
The choice not going for the most usual and easy approach looks weird, it hurts. The issues are:
cloud link
doing the basics
Disadvantage are: Advantages:
Understanding why HTML is combined with CSS
A big conceptual change is done. html css intro
HTML was NEVER intended to contain tags for formatting a web page! HTML was created to describe the content of a web page, like: To solve this problem, the World Wide Web Consortium (W3C) created CSS. CSS removed the style formatting from the HTML page!
This results into:
Chapters
After some trials, I was happy to have three chapters on each page with four separations blocks. Three chapters is aligned with the nine plane. Those three chapters are extended to six. This is a fit for the Zachman philosphy. Surprising also the nine plane in two constellations.
This is a fit for the intended content.
Paragraphs
For human reading paragraphs are shown in a limited width.
A paragaph is filled with content up to a maximum length.
Using six paragraphs at each chapters allows dynamic reordering in different layouts: Each paragraph is having divisions. Ordering the divisions over paragraphs is challenging.
Disadvantage: there is no standard canvas, no standard layout. Hard work to do it in basics.
dual
⚙ C-2.1.2 Technology Responsive Web html
Hardware abstraction.
Any application should have as less as possible dependicies on the hardware used. Using a browswer (HTML CSS) the knowledge on viewport is the most important for how to present what there is. Old hardware was limited in the sizing and the performance. Those limitations are changed.

RWD Responsive Web Design
What is Responsive Web Design?

Different type of endpoints, PC, tablet, smartphone
Some links on the technical coding: What was missing once was: Heavy usage of java-script could get more avoided using this.
dual
Presentation, technical CSS
The abstraction in the presentation html - css has resulted in a requirement for understanding and building the css code. Having a goal of dynamic behavior showing and processing information the css presentation is causing challenges.

Setting text divisions disable flow (css)
The content of min-width is deliverd by the browser. In a wide screen width 3 columns over 2 rows the definition for css attribute clear is:
@media only screen and (min-width: 1290px) and ( max-width: 2760px ) { :root { --divtxt-clr1: both ; --divtxt-clr2: none ; --divtxt-clr3: none ; --divtxt-clr4: both ; --divtxt-clr5: none ; --divtxt-clr6: none ; } ; }
Also six block class definitions with in the order of the six divisions.
div.tx-p%{ clear: var(--divtxt-clr%); }
Viewport, calaculations (css)
A responsive desing calculating the size of elements:
:root { --divtxt-width: calc(100vw /2 - 24px) ;
Fixed and dynamic sizings
Images can be fixed when used to help understand text in a small inline elaboration. When they are over the 240px size they should resize wiht the section and viewport size.
All what has done in recent years will have to be rebuild to get aligned with this.
Analysing a CSS to generate new HTML with inline style code is a bad practice although giving quick results. A new problem is arising. When defining all those CSS classes in a specific in house style, how to align the dynamic generated html code using those classes?
Paragraph breaking up in segments (html)
Just wanting a clear breakup within a division ( clear:both ), then a css attribute must be used, not html. An easy approach:
<hr class="clear">
Adding this in non floated block elements will solve the height for the borders when used with floated sub elements.
Issues, mistakes, not well understood technology
That is work to do, just need a plan when there is time doing that.
cloud link
⚙ C-2.1.3 Technology Web html Notes
Floating elements and borders
A dynamic layout and dynamic positioning rises questions in the order of processing the lay out. When borders are added ore removed after the positioning of floating elements the positioning itself will change. This is an unsolvable situation. Better is avoiding that ordering question.
Issue overlappng borders A common problem with float-based layouts is that the floats´ container doesn´t want to stretch up to accomodate the floats. If you want to add, say, a border around all floats (ie. a border around the container) you´ll have to command the browsers somehow to stretch up the container all the way.
Syntax & spacing
First, the required and resulting spacing is not always clear. CSS: Calculating a number and doing a substract - 32px and -32px would both look correct. The second one is not doing the calculation, no error/warning.
Dynamic layout effect
Complexity in several ways.
dual
Presentation logical set up.
Every application has his own set of requirements. Just documenting ordering like written book is very different to an interactive approach using one ore more backends.
Information for reading should not be to have a great width. Scrolling vertical is the most logical seeing some text having read and new text coming in.
Imagemaps
I have used that For choosing in a map representing the site structure. The figure self is big (480 reference).
The coords attribute on the imagemap html tag does not support percentages. Percentages would be the way allowing dynamic adjusted content. Approaches are solving it scripted. Someone is mentioning CSS. fora
Video MP4 autoplay
Html-5 is supporting the videa tag, seems easy to implement. Having wondered why it did not autostart on the mobile and it did on the desktop. Than noticed it didn´t start when it is the entry page, only when coming from another page of my onwn pages it start. Uhhh, what is going up? Web pages should not know what has happened before (stateless).
Emoticons not as expected
dual
Frictions nine plane laysout.
A web page (html) is technical a Latin-1 basic communication protocol. Using extended characters is needing encoding.
Encoding: Using basic representing for other characters
When using multiple programming languages mixed up in a single document, quoting escape sequences are needed.
Html evolution
The language has been extended with many new tags going from version 3 to 5, others tags are depecreated. Some recurring work in redesign en maintenance is required. The biggest change seggregation presentation from static content.
Notes from relocations
Building using an editor the non printables are a challence when hit
Ai missing ML

C-2.2 Communication - Interactions

Working with machines that process information, is a relative new topic of science. Human communications and interaction is classic.
How to understand the physical world and virtual one in similar concept and practical implementations is a big challenge by human understanding human communication.
Understanding and communicating about:
Zachman 6W-s no W for which technology
🎭 C-2.2.1 Guide reading these pages, site
Break-up: Logic, Concept, Context
The words Details, Physical, Logic, Concept, Context are from the Zachman framework.
In the logic middle there is a switch in understanding for approaches. The same topics are translated into another world.

When the why for context is clear and de details in the physical realisation are clear it is an easy understandable classification. The challenge is that the why for context neither the realisation are clear understandable. The what (logic) could be a better start. The result can be a reduced complexity context or a physical realisation.
Replacing the why in the horizontal for which is more applicable in most situations.
More easy to understand for design: More easy to understand for realisations:
Break-up: paragraphs 6w1h
Only three levels are grouped in this, one group for design plan and another for realisations devops. The split in the middle is going up (designg) or going down (devops).
The six paragraphs for each of three chapters are aligned for 5w1h questions and the content for the why. The first three paragraphs are having a different orientation as to the last three.
2,3,4,5 Natural whole numbers
Reading a full wide screen is not feeling very natural. Splitting up in columns is more user friendly. The disadvantage is having those columns longer, needing some scrolling. For ease of dividing in whole numbers the following are practical layouts evenly spread. Options are: The clock is based on 12 (24) hours and subdived in 60 easing all those split ups.
For the number of columns 3 chapters each using 6 paragrpahs is chosen.

🎭 C-2.2.2 Presentation logical setup - Positioning
7 w-s Lawrence Corr
Processing Information.
When an business process is set up, the major question is the goal. It starts at a high level (context, conceptual), into achieving a presentation for interactions.
A top down design starts with the questions about information and will search for the best way in presentation(s).
A bottom up design will start with how something is presented and is trying to invent what and how the information should be processed.
Beam data storming Using the 7 w´s for data modeling putting the informations questions from business perspective central has this source of modelstorming. The technical logical and physical ideas is classic dwh.
Aside Data Types (Cn, DTn, DN, Nn.n Tn B) there are Key types, Dimensions Attribute types and Fact types. Asking with 7W´s how the data should look like for an informational result and asking with 7W´s how the processes should work to achieve goals should work in a generic way.
Conceptual Information (Back end)
A conceptual schema is a high-level description of informational needs underlying the design of a database. It typically includes only the main concepts and the main relationships among them.
Logical Information (Back end)
A logical data model or logical schema is a data model of a specific problem domain expressed independently of a particular database management product or storage technology (physical data model) but in terms of data structures such as relational tables and columns, object-oriented classes, or XML tags. This is as opposed to a conceptual data model, which describes the semantics of an organization without reference to technology.
Physical Information (Back end)
A logical data model or logical schema is a data model of a specific problem domain expressed independently of a particular database management product or storage technology (physical data model) but in terms of data structures such as relational tables and columns, object-oriented classes, or XML tags. This is as opposed to a conceptual data model, which describes the semantics of an organization without reference to technology.
Dynamic Information (front end)
Online transaction processing systems increasingly require support for transactions that span a network and may include more than one company. For this reason, modern online transaction processing software uses client or server processing and brokering software that allows transactions to run on different computer platforms in a network.
Static & Meta Information
The descriptive information for the dynamic data is not required to build up over and over again. It can have an static layout. It will go along with the life cycle of programs - code.
Presentation (front end)
How the the Static & Meta Information and Dynamic Information is implemented in a Physical way is the delivery act of an information system.
🎭 C-2.2.3 Human interactions on what to do, LCM
Stand up - collaboration
The daily stand-up
Managing people is an important factor for organisations. The implementation of a daily stand-up can be brought in mandatory. Getting aligned how to practice coordination alignment of people at organizations to get things done is the daily way of life.
Knowing what the colleagues are doing is important with a shared goal.
There is something strange going on:
Context Operational or Analytical systems
❶ Operational plane: Operational systems can contain analytical processes for automatic closed loops optimizing flows. The goal is delivering the products of missions in agreed quantity and quality.
❷ Analytical plane: Should not have operational content in avoiding compliancy issues. It is in support of closed about about the operational flows. The goal is helping decision makers for reliable and timely decisions.
❸ There are several layers with components in the life cycle management process. These are:
  1. Infrastructure including middleware and platforms.
  2. The information flow product line having
    ❶ the physical data representation of information and
    ❷ the code of business rules.
  3. Analytics processing for closed loops, support in decisions.
Each of these has their own life cycle that is resulting in technical and funtional gaps by misalignments.
Maintaining operational plane systems
The operational plane is classic with best practices in LCM. Reliability, predictability in consistent processing flows are major concerns.
When AI/ML is used the concern is about training data lift consistency and impact.
In a figure: LCM Process operations
Maintaining analytical plane systems
The anlytical plane is suffering by a lot of misunderstandings, quick solutions caused by misunderstanding and bad alignmet in communications. What is different: Reliable timely signals about processing flows are major concerns.
In a figure: LCM Process analytics
😉 Knowing the challenges and differences the next step is solving this in alignment by communication.
rethink what has happened TN

C-2.3 Historical evolvement ICT

Learning from examples is the start how it should be done.
Practising experimenting is the next stage.
Goal: innovating extending improving how it should be done.
The good message: there is a prospect for improvement.
The bad message: it will not be easy.
Predictive understanding:
📚 C-2.3.1 Probability distributions
The importance of probability distributions
➡ Doing the right things.
Analytics: The complete minicourse (Cassie Kozyrkov 2020) Analytics is like the writing profession: the basics are easy to get started with and it's an art, so there are few barriers to entry and anyone can be a "writer." There's no guarantee of quality in the title. However, being barely-literate hardly makes you Toni Morrison or Leo Tolstoy - the best writers are light-years away from beginners and they change the world. That's how analytics is too. The variance in the profession is massive. On the other hand, barriers to entry for, say, statistics, are higher, so the minimum level folks are more impressive than minimum level analysts, but there's also a narrower range of virtuosity. I'm often far more impressed by expert analysts than by statisticians and ML engineers.
➡ Doing the things right.
How to spot a data charlatan (Cassie Kozyrkov 2020) Advice for leaders: Leaders, refuse to take any data “insights” seriously until they’ve been tested on new data. Don’t feel like putting in the effort? Stick with analytics, but don’t lean on those insights — they’re flimsy and haven’t been checked for trustworthiness. Additionally, when your organization has data in abundance, there is no downside to making splitting a core part of your data science culture and even enforcing it at the infrastructure level by controlling access to test data earmarked for statistics. It’s a great way to nip snake oil sales attempts in the bud!
The infamous pareto principle
Pareto In 1941, management consultant Joseph M. Juran developed the concept in the context of quality control and improvement after reading the works of Italian sociologist and economist Vilfredo Pareto, who wrote in 1906 about the 80/20 connection while teaching at the University of Lausanne.
Mathematically, the 80/20 rule is roughly described by a power law distribution (also known as a Pareto distribution) for a particular set of parameters. Many natural phenomena are distributed according to power law statistics. It is an adage of business management that "80% of sales come from 20% of clients."
The 80/20 rule is one being abused by biased assumptions for:
Greece explore
Gini unequalness - uneveness
Gini The Gini coefficient was developed by the Italian statistician Corrado Gini and published in his 1912 paper. Building on the work of American economist Max Lorenz, Gini proposed that the difference between the hypothetical straight line depicting perfect equality, and the actual line depicting people's incomes, be used as a measure of inequality. Normal distributions are not normal in many real life situations, there a tremendous diffrences between groups.
The Gini coefficient measures the inequality among the values of a frequency distribution, such as levels of income. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values, a situation where a single individual has all the income while all others have none.
Greece explore
Lean staffing ➡❌ Lean
This is NOT Lean: Lean Staffing claims to be lean: lean staffing. It is NOT lean! It is an abomination. It is pretty much the opposite of what, in my opinion, lean stands for. It is a complete lack of respect for humanity. Let me explain you what it is, why it is terrible, and how to prevent this. Note that this post may include a rant here and there. ...
Judging people on what is in fact a probablistic outcome is bad in many ways.
There are multiple anecdotal examples where the entire staff just walked out, and the business had to close temporarily or permanently. The word gets around, too, and staffing may become more difficult. There is an internal memo at Amazon where they worry “If we continue business as usual, Amazon will deplete the available labor supply in the US network by 2024.” In other words, they may run out of people to hire by 2024 due to their eye-watering 150% annual turnover rate.
📚 C-2.3.2 Computer Technology - decisions algorithms
Pythagoras
The first well known algorithms still referred by this name. It cannot be the oldest way to calculate a surface. Egyptians did that long before these writing and likely others also.
Pythagoras of Samos Pythagoras ho Samios "Pythagoras the Samian", b. about 570 d. about 495 BC was an Ionian Greek philosopher, mathematician, and founder of the religious movement called Pythagoreanism. Most of the information about Pythagoras was written down centuries after he lived, so very little reliable information is known about him.
decision by hand
Decisions by leaders in ancient history
What was challenged in those time did not need any proof. The judgement was done by the majority being empowered. When the emperor leader as person did not wanted not being personal involved the decision went on other input. The case of Socrates is well described.
 
As easy it was to get condemned just by public opinion sometimes it was evenly easy to get away from anything. At least the logic and paradox questions started. liar paradox
⚠ The mistake made by Thomas Fowler (and many other people) above is to think that the negation of "all Cretans are liars" is "all Cretans are honest"e (a paradox) when in fact the negation is "there exists a Cretan who is honest", or "not all Cretans are liars".
📚 C-2.3.3 Decisions algorithms - statistical science
limited list of technical statistics.
The statistical algorithms are in place for a longer period. They should be well known although inventing them by yourself or understanding their proof of correctness is not that easy. The usage of all this gets hidden in automated tools doing analyses. The easy mistake is by using them incorrectly in wrong assumptions. There are several topics for stats.
Probability theory (wikipedia)
Describing information, dicrimination in distribution type.
Nominal_data
mean
median
the normal (or Gaussian)
Poisson
uniform
Skewness
Kurtosis
chi-squared distribution
F-test
Chebyshev
Regression (wikipedia)
Interpretation of the information description by drawing a line.
Ordinary_least_squares
Weighted_least_squares
Two-stage_least_squares
Nonlinear_least_squares
Logistic_regression
Generalized_linear_models
Least_absolute_deviation
Stepwise_regression
Quantile_regression
Probit_model
Cox_regression
Poisson_regression
Multiple_linear_regression
Anova correlation clustering
Finding artifacts of the same group, using group differences.
MANOVA
General_linear_model
Mixed_model
Post-hoc_analysis
Latin_squares
Time series
Using time -temporal- as the main dimension with something else.
ARIMA
GARCH
Unit_root_test
Cointegration_test
Vector_autoregression
Granger causality
⚠ What is done with BI (Business Intelligence) is handing over mainly time series oriented information descriptions to decision makers. Ignored are all the options in a process were time as an axis component is not that important.
📚 C-2.3.4 Fraud detection & technology abuse
Benford distribution of numbers
With te conditions of real measures the numbers itself are not random.
Benford´s_law
Benford´s law, also called the Newcomb Benford law, the law of anomalous numbers, or the first-digit law, is an observation about the frequency distribution of leading digits in many real-life sets of numerical data. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small.
At retail supermakets there is reason why the 9 is in acces for the announced prices.
How a Simple Misconception can Trip up a Fraudster and How a Savvy CFE Can Spot It (acfe)  
Devil feeling
Dark sides in leader KPI´s
Fame, Wealthiness are tempting when in a position to manipulate the prestation figures. Rogue traders
A rogue trader is an employee authorized to make trades on behalf of their employer (subject to certain conditions) who makes unauthorized trades. It is most often applied to financial trading, when rogue professional traders make unapproved financial transactions.
Other examples:
Using knowledge predictable outcome
Manipulting outcomes having an option to ask a question for a smart decision. Knights and Knaves 100% certaintity is a remarkable lift form only 50% guess. The important question is: "What question should be asked?".
Choosing and playing random, or not being random.
Rock-paper-scissors
Rock paper scissors is often used as a fair choosing method between two people, similar to coin flipping, drawing straws, or throwing dice in order to settle a dispute or make an unbiased group decision. Unlike truly random selection methods, however, rock paper scissors can be played with a degree of skill by recognizing and exploiting non-random behavior in opponents.
Optimising an outcome, counterintuitive.
Three door problem (wiki) The situation is a brain teaser for the specific conditions into change of chances and proceeding with decisions.
Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what's behind the doors, opens another door, say No. 3, which has a goat. He then says to you, "Do you want to pick door No. 2?" Is it to your advantage to switch your choice?
The given probabilities depend on specific assumptions about how the host and contestant choose their doors. A key insight is that, under these standard conditions, there is more information about doors 2 and 3 than was available at the beginning of the game when door 1 was chosen by the player: the host's deliberate action adds value to the door he did not choose to eliminate, but not to the one chosen by the contestant originally. Another insight is that switching doors is a different action than choosing between the two remaining doors at random, as the first action uses the previous information and the latter does not.
 
felling mislead The problem is a paradox of the veridical type, because the correct choice (that one should switch doors) is so counterintuitive it can seem absurd, but is nevertheless demonstrably true. The Monty Hall problem is mathematically closely related to the earlier Three Prisoners problem and to the much older Bertrand´s box paradox.
The "paradox" is in the probability, after choosing a box at random and withdrawing one coin at random, if that happens to be a gold coin, of the next coin drawn from the same box also being a gold coin. ... Their solution illustrates some basic principles, including the Kolmogorov axioms.
Good old Monty Hall! Or, All Probability Is Conditional (wmbriggs)
The Monty Hall Problem: A Study (Michael Mitzenmacher) Research Science Institute 1986
Architec role

C-2.4 Processing flows VSM - Change

A swarm organisation, self organisation, are networked structures without leaderships, a shared goal.
⚠ Challenges by a shared goal: coordinating architecting - engineering: 👁 Focus on the why for the business organisation.
⚒ C-2.4.1 Retrospective 6w1H
6w 1 how
Categorizign using 6w1H
During analysing to solve a problem the question arose between roles tasks and responsibilities. The why of the problem was triggered by organisational interest, the coordination of which and where. Missing the correct knowing what and how caused not undertandabel crashes. In technology there are many specialisations.
The classifications reduces six categories for three parties. When the Which is given to Serve en Where to Steer, two parties.
Categorizign using 5w1H
About the zachman framework
Now that we are in the Information Age, it is the Enterprise that is increasing in complexity and the Enterprise that is changing. It is my opinion that Enterprise Architecture is the determinant of survival in the Information Age.
Wat are criticisms to Zachman framework?
  1. The hierarchical orientation does not match the challenges when working engineering for flows, value streams. Engineering is considering several abstractions levels. Architecting is considering several abstraction levels. There is context switch at "logical" connecting fucntionality to technology.
  2. Although change by learning from experiences is a leading key factor in engineering, the framework has become a fixed theoretical monolith with certifications.
  3. Complexity: the 6x6 matrix is overwhelming and complex to use
  4. Lack of practical guidance: the framework is too theoretical, lacks concrete implementation guidance
People process technology
⚖ C-2.4.2 Zachman Overhauling Ideate
Open mindset for a transformation
People will often find it comfortable being in their own shells, trying not to venture out. ... The first one is adapting to the market conditions and accepting that things could be done differently. The second is to adopt new ways of working. ...
Many of us are used to thinking or working in a certain way for a long time. We are normally uncomfortable with change. But a change in mindset is required for something like an digital transformation as we have to constantly unlearn and learn new culture, processes and skills. ...
The process will follow a typical Plan-Do-Check-Act cycle. ...

Mind set change 6 stages transformation
Processes are set to build a cadence and discipline in how we develop the product. If there are gaps in our processes, they trickle down into the product too. ...
In the long run save on costs by building, testing, and releasing a product that will actually be usable and increase adoption. Also there is a need to introspect about the activities and practices of the team to understand where the gaps lie and what can be done to fix them so that the quality of the processes and services is consistently high. ... Futurespectives are also effective in issue prevention by orienting people’s thoughts and actions towards looking ahead and preparing to do the right thing at the right time. Once the gaps are identified, and preventive actions agreed upon; teams have to spend time and effort to implement the actions. It is upon the team and leaders to create opportunities for such activities
Six necessary conditions for a successful digital transformation (Srinivas Murty, Sudarshan Gorur 2021-12 thoughtworks). An experiment to align those six categories in a Zachman frame by those agile attention points for transformations. Put those six categories into a zachman categorization:
5w1h Engineering Agility
What Bills of Material ⇄ Communication
How Functional Specs ⇄ Shift in mindset
Where Drawings (or Geometry) ⇄ Choice of tech & tools
Who Operating Instructions ⇄ Unlearn relearn
When Timing Diagrams ⇄ Adoptions & adaptation Timing
Which Design Objectives ⇄ Process improvements

The experiment is successful.
👁 It should be possible to engineer "design of designs".
SIAR process cycle focus simple
Jabes rotated plane, flow, pdca
◎ PDCA in a full cycle arround information (data).
◎ Fucntional tasks Steer, Server, Serve instead of organisation names.
◎ Flow left to right.
◎ The customer being part of the cycle (act redesign), not an external artifact. Similar to Deming added that to Shewhart.
Notes:
◎ Repositioning planes (9plane) so that is does match with a flow left to right, pdca, tasks. For the enterprise organisation, methodology, in the horizontal centre.
The figure, See below:
dtap layers application
⚖ C-2.4.3 Zachman Overhauled objectives
Abstractions into columns
The ordering of columsn has changen in time in the zachmans framework. Starting with design objectives is more sensible than starting wiht a bill of materials.
❶ To align with the Jabes plane orientation supporting flow, PDCA DMAIC and more the following ordering is preferred:
Which, When, Who, Where, How, What.
◎ When designing the answer on, "Why" is the cel content. Which, which choice, is better when there is a list. Start with Why (Simon Sinek, 2009) Sinek says people are inspired by a sense of purpose (or "Why"), and that this should come first when communicating, before "How" and "What"
◎ Every cell has at least three perspectives, those are: technical, intended process, methodology - business (mission vision goal). Confusing: there is a why how what seen in this cel content.
❷ Allow changes in the classification details, learning from feed back in experiences.
❸ Reduce complexity by understanding and explaining relationships and expectations.
❹ For any defined engineering context an understandable practical guidance.
Classifications into rows
identification Zachman Standard Process information
Serve
  Push 		What
Bills of Material 		⇄ Material Data Data storage
Serve
  Push 		How
Functional Specs 		⇄ Process Information transformations Data integration
Steer
  Pull&Push		 Where
Drawings (or Geometry) 		⇄ Geometry Network
Location technology 		Reference data management
Steer
  Pull&Push		 Who
Operating Instructions 		⇄ Instructions People
Screen, user interactions 		Data quality
Shape
  Pull 		When
Timing Diagrams 		⇄ Timing Time
Performance 		Data modeling
Shape
  Pull 		Which
Design Objectives 		⇄ Objectives Motivation Data governance

🤔 The role of Pull and push is for expected activities as an fucntional requirement or technical outcome.
Abstractions into columns
Abstraction is by perspective imagination until it is convinced to able into realisation. The architect and engineer role are about in the "how" to solve the why delivering the "what". There are two additional "why, how, what" cycles around them.
🤔 The architect trying to define how processes can be translated into ideated machines.
🤔 The engineer how ideated machines for processes can be realised into working machines.
identification Zachman Hierachy Hierachical Activities Functionals
People
  Design 		Context
Scope 		⇄ Executive planner Scope Hoshin Kanri
Process
  Design 		Concept
Concepts 		⇄ Business Owner Enterprise Requirements
Machines
  Design 		Logic
Logic 		⇄ Architect Designer System Architecture
Process
  Devops 		Logic
Physics 		⇄ Engineer Builder
Programmer		 Technology Engineering
Machines
  Devops 		Physical
Technology		 ⇄ Technician Implementer
Programmer		 Detailed Technology tools components
People
  Devops 		Details
Product 		⇄ Enterprise User
Operator 		Functioning Enterprise operating instances

🤔 When analysing it is hard to start at the Context. To work at something that is unclear even unknown is very difficult. Starting at the design logic with assumptions (bottom up) and than verifying in top-down is a more workable approach.
elephant-blind-men

C-2.5 Processes Building Blocks Realisations

The term elephant test refers to situations in which an idea or thing, "is hard to describe, but instantly recognizable when spotted"
A process life cycle building block, ALC life cycle, is very generic en simplistic, however there are seeral confusing layers.
To solve:
😱 Understanding goals, getting values is hampered by culture.
😱 Being Technology driven is a fail for product values streams.
🎭 C-2.5.1 Learning from examples, Zachman overhauled
There are no good examples found how to apply a Zachman approach for architecture and engineering.
There are no generic design/build patterns for: There is an exception for nice information usable as an example. SABSA is set up refering Zachman in the classic setting. Sabsa is very abstract but usuable.
Just looking arround for more having those six engineering categorisations and Siem is an applicable one.
🎭 C-2.5.2 PDCA in another context, SABSA
SABSA cycle
PDCA SABSA
The Journey to Enterprise Security Architecture
Architected security is business-driven, aligned, integrated, monitored, and continuously improved to enable the enterprise to pursue its goals and objectives reliably. The SABSA Enterprise Security Architecture (ESA) Framework and Methodology delivers mature, vital, business-enabling ESA. SABSA provides the guidebook for the ESA journey that begins with real business requirements and traverses a complex landscape of shifting threats and opportunities. ESA’s destination is living, adaptive, continuously improving and up-to-date enterprise security. SABSA’s four-phase life cycle begins with Strategy & Planning, and moves through Design, Implementation, and Manage & Measure to iterate through the lifecycle as the organisation evolves.
The PDCA is obvious
System thinking SABSA
Architecting a Multi-Tiered Control Strategy
The purpose of security is of course to avoid business disruption and ensure there is a robust, fit-for-purpose, business enabling and end-to-end solution.
Being compliant is not the main goal and should not be the top priority. Also, because most of the standards are considered best-practice but are not tailored to the organisation or are incomplete. ...
Engineers use their knowledge of science, mathematics, logic, economics, and appropriate experience or tactical knowledge to find suitable solutions to a problem. In Information Security, this is no different. We use our knowledge of logic, economics, politics, appropriate experience, business strategy and existing solutions to find and build the suitable security solutions to the security problems to support and enable the business. SABSA Strategy
Security Principles
Seeing the SABSA system thinking it is very generic, no real difference to the physical world. The well known security standaard are having physical components, a hybrid approach.h In the physical world there is simple dogma:
🎭 C-2.5.3 Design process details
Sabsa 5w1h details
Wat kind of controls are in scope? It might be surprising technical security controls are not at "Serve". A safe environment is not the responsiblity or accountablity of technology (serve) altough the technology guys can advise and help.
SABA Controls
Siem Controls
Siem details
Security information and event management, siem is getting a follow up. SIEM vs SOAR: What’s The Difference?
SOAR. Security Orchestration, Automation and Response is a technology that improves cybersecurity by safeguarding networks and devices against cyber threats, attacks and unauthorized access.
Gartner: Incident analysis and triage can be performed by leveraging a combination of human and machine power — help define, prioritize and drive standardized incident response activities. SOAR tools allow an organization to define incident analysis and response procedures in a digital workflow format.
Putting the Siem structure in a ovherhauled Zachman classification:
Zachman engineering Security information Event Management
What Bills of Material ⇄ Collect data: Servers, Network devices, platforms
How Functional Specs ⇄ Aggregates Data landing, staging, semantic
Where Drawings (or Geometry) ⇄ Analyses normalized data for: anomalies, threats, trends
Who Operating Instructions ⇄ Investigate alerts identifies breaches, respond action
When Timing Diagrams ⇄ Remediates discoveries: act on vulnerabilities, breaches, cve alerts
Which Design Objectives ⇄ Reports on risk and compliance

🤔 This series is the most easy understandable column "Physical Technology".
In the shown matrix there is a lot to be reused. The black row with words management is a good fit for the column "Concepts".
⚙ C-2.5.4 Basic building blocks categorisation
Hoshin Kanri - jabes meta
Changing developing maintaining an organisation is not technically different than Changing developing maintaining an information technology application. The names of roles are different. Some questions and answers: There is a lot room for misunderstanding confusing in the chain of involved people. A comic: Leadership for a Digital World
Communication understanding challenge
In large organizations, it can be difficult for Developers to get access to end users. We can’t just make many of these middlemen go away since many of them were hired for being exactly that, but we can educate them on why it’s important to include Developers when talking to end users.
If Developers talk directly with end users regularly, they’ll get a much better understanding of the end users’ needs, work processes and preferences than if there are several proxies in-between.
Building up this empathy and awareness of end users will, eventually, lead to Developers being able to challenge the scope and take better product decisions because they simply just know more about the context to which they’re delivering.
If the end users need some convincing buy in on having to spend time with the Developers (they’re usually more than happy to do it), just promise them that it’ll help increase the awesomeness of the product and, thus, their everyday lives.
🤔 The overhauled zachman matrix
The goal of the overhauled zachman classification is understanding, defining, enabling Jabes metadata, so applications can be methodological: architected engineered, build, supported, operated, reviewed. It is an innovative mindset. The hardest will be acceptance needing a lot to Hesomething unclear even unknown is very difficult.
Data monetizing journey

C-2.6 Organisation & Business Understanding

Once Dorothy and her colleagues made the journey to OZ, they quickly found out that there was no there, there. The Wizard simply told her what she really should have known all along.
Dorothy and her companions just had to figure out how to reframe their own perceived shortcomings and recast them as strengths to achieve real transformation.
🎭 C-2.6.1 Retrospective Using 6w1h
6w 1 how
Why is Jabes interesting?
Everybody is looking for a solution to mange the challenges with information processing.
As far I know there is nothing on the market for solving those challenges holistic. There are many tools for detailed topics, but no one covering all the interactions.
The zachman framework connection
Building up and analysing some why-s I accendentiality got into this structure. It is hard work for reducing complicated issue into more understandable ones. An overhauled version is proposed to avoid loopholes in reasoning categorising. I don't see any more simple alternative.
Jabes is needing a well defined metadata structures to reuse methodlogy patterns patterns for feature profiles.
Defining a generic metadata structure that is weel practical usable will not be easy.
The 5w1h +1w approach:
goldrush
🎭 C-2.6.2 Who does product management?
🕳 Categorizing content, start with the why
During the 1980s and 1990s, the Generalization approach to product management was prevalent, especially in Silicon Valley. In this model, the product manager took on a wide range of responsibilities, effectively acting as the CEO of the Product. This approach emphasized a holistic view of product management, where a single individual oversaw everything from strategy and development to marketing and sales1. It’s interesting to note that this approach has evolved over time, and today’s product management practices are more specialized and collaborative.

😱 Expecting accountability for a product product line. Surprising, by culture it is not reality.

Silicon Valley, known primarily for its technology innovation and startup culture, has been at the forefront of management innovation. The region’s top companies not only start well but also continue to evolve, finding new ways to combine and apply technologies. They move beyond their initial success by creating new organizational forms and adopting unconventional management practices. For instance, the “H-P Way” emerged as a hallmark of Silicon Valley, emphasizing decentralized corporate structures, teamwork, shared responsibility, and entrepreneurship.

product managers encounter several challenges in their roles. Here are some common ones:
  1. Balancing Priorities: Product managers must juggle competing priorities—user needs, business goals, technical constraints, and market trends. Finding the right balance is crucial.
  2. Unclear Roadmaps: Creating a clear product roadmap can be challenging. Prioritizing features, aligning with company strategy, and communicating the vision effectively are ongoing tasks.
  3. Stakeholder Alignment: Product managers work with cross-functional teams, executives, and external partners. Aligning everyone’s expectations and goals can be tricky.
  4. Scope Creep: Managing scope changes during development is tough. Avoiding feature bloat and staying focused on the core value proposition is essential.
  5. User Feedback Interpretation: Gathering user feedback is vital, but interpreting it correctly can be complex. Distinguishing between vocal minority requests and broader trends is a skill.
  6. Market Research and Trends: Keeping up with industry trends, competitor products, and emerging technologies requires continuous learning.
  7. Resource Constraints: Limited time, budget, and engineering capacity impact product delivery. Prioritization becomes critical.
  8. Decision Fatigue: Product managers make countless decisions daily. Decision fatigue can affect judgment and lead to suboptimal choices.
  9. Managing Expectations: Setting realistic expectations with stakeholders, especially when timelines or outcomes change, is challenging.
  10. Handling Failure: Not all product launches succeed. Learning from failures and iterating is part of the job.
  11. Remember, each challenge presents an opportunity for growth and learning!
 In book: Leadership for a Digital World (pp.25-28)
🎭 C-2.6.3 Classic product management, guided by principles.
🕳 Categorizing content, start with the why
Schools thought product management The four approaches are Generalization, Technology, Business, and Methodology.
CEO of the Product
The Generalization approach to product management views product management as multidisciplinary, multifaceted, and responsible for nearly anything and everything related to the product. The primary consideration that product management is a generalization, not a specialization, leads to monikers such as CEO of the Product to describe the supposedly encompassing nature and broad profile of product management practitioners. The Generalization approach describes product management as diverse responsibilities that may include tasks offloaded from other corporate functions. The Generalization approach also describes product management as the backbone, connective tissue, or glue that holds together all aspects of a product project. Constant deliberations on the scope of ownership, range of responsibilities, desired skills, and how to lead without authority are prevalent.
Business of the Product
The Business approach to product management is heavily focused on the product's business aspects with a broad emphasis on all monetary issues. Consequently, this approach resembles a scaled-down executive management function. Relative to the product, this approach deals with decision-making, process application, interdepartmental coordination, project management, team management, revenue management, metrics costing, pricing, etc. Phrases such as "owning the strategy", "driving execution", and "profit and loss accountability" are frequently used in conjunction with this approach.
Discussions on how product management should deal with business strategies, business models, and any of the latest business or market trends are considered very relevant because the thinking is primarily geared toward making money. Expectedly, the Business approach to product management is fixated on any trending innovation, such as big data, machine learning, artificial intelligence, automation, analytics, virtual reality, robotics, intelligent applications, and just about any new technology of the day that perhaps could be leveraged to make more money.
Part of Engineering
The Technology approach to product management considers product management as an extension of product development and, at times, even subservient to product development. The Technology approach exhibits a mindset that contemplates all product-related issues, roles, responsibilities, processes, and tasks from a technological or developmental viewpoint. With the Technology approach, product management practitioners are expected to be technically astute.
Indeed, many product managers at companies that implement the technology approach, are former engineers who perform various activities that support product development and occasionally sales. These practitioners' main job focus is to determine product functionality and features and communicate these to product development.
With the Technology approach, a product manager’s deep understanding of technology and product knowledge is highly valued. Conversely, market research is regarded as a low-priority activity. At software development companies that adopt Agile/Scrum, a lightweight software development method, it is considered legitimate for a product manager to assume the responsibilities of a Scrum product owner. Product management practitioners subject to the Technology approach to product management are forced to contemplate their professional identity and place in the hierarchy and ponder how they should adapt to changes in the product development sphere.
Guided by Principles
The Methodology approach to product management views product management as a professional domain governed by a set of foundation rules supported by cogent rationales and solid arguments.
The Methodology approach regulates everything in product management, such as terminology, definitions, roles, tasks, responsibilities, teams, models, processes, interfaces to other departments, etc., according to a methodological foundation and fosters a more strategic, systematic, and disciplined way into how the company deals with all product-related issues.
The Methodology approach to product management treats product management as a highly strategic function, not a managerial, operational, tactical, or technical function.

🎭 C-2.6.4 Product lines next level
Product line engineering
embeddedcomputing At first glance, product development may seem like the most complex component of the product life cycle. However, everything developed from the silicon, circuit boards, and software have features, options, and derivatives that need to be identified, productized, and maintained. Managing the myriad of variations within a product line is often called product line engineering (PLE). In order to be competitive in today’s environment, companies must deliver a product line, not just a single product with a mentality that “one size fits all.” What is product line engineering?
In general, product line engineering (PLE) refers to the practice of creating an underlying architecture (both hardware and software) that describes the base platform. The architecture describes the base commonality across the product line as well as planned variations. PLE focuses on the process of engineering new products so it is possible to reuse product assets and flexibly apply a subset of variations for a specific product with minimal cost and time spent. ...
ecomp-biglever-1.jpg
“Think of an analogy of a factory,” ... “Assembling and producing requirements and specifications is one dimension. Think about the things coming into the factory: the traditional artifacts like technical specifications, subsystem designed, bills of material, software, user documentation, calibration data, test cases, certifications… The list is long. We want to get really good at reusing these artifacts.”
Reusing Product lines engineering
The feature descriptions go into the PLE process and are used to engineer the feature profiles that cause the products to come out. You’ll notice in the diagram the comprehensive nature of the feature catalog as it feeds into both the development engineering and testing phases of the V-model. In order to create a specific product, we need a way to filter all these assets. How do we know what to do within the configurator?
This is where the bill of features specifications come in. If the feature list includes a feature, the configurator will include that in the assets, based on the bill of features. ...
“Agile processes still have assets [definition of done, user stories, sprint reviews], and these assets can be handled in the same way within the factory view of PLE,” ... “The development of each feature may be coming from a user story, but the asset result is the same.”

😲 Reuse of assets simplifying infrastructure is seen high valuable. Amazingly: agility mindset, absence of product line accountability are impediments.
🔰 Contents Mesh ABCs Control ALC-V* Polestar 🔰
  
🚧  Variety Act on Cyber Change ALC-V* knowtc 🚧
  
🎯 Algol Interact Tenets Change Volatile North 🎯

C-3 Realisations by Wisdom


meandering path

C-3.1 Miscellaneous Practical Knowledge

There a lot of pitfalls in the challenge of understanding how to do architecting engineering realisations. The path is a labyrinth spread over several areas.
Even with good intentions it is possible to end at the wrong side. Continous evaluation is needed and continous adjustments are expected.
😉 Structuring the paths is the first challenge to solve for understanding by simplifying what is overwhelming in complexity. This structuring is not a free lunch, it is a challenge on his own.
🎭 C-3.1.1 Presentation - Windows Sizing, Positioning
The question: how to structure information for an understandable interpretation technically (web).
The content is presented in floating blocks on a page. The layout is done using css only with well chosen options. Character size & type, borders margins and colours in a fixed size where others can resize. To my surprise the hardware viewport with small devices is 400px or even smaller. The 25% setting of that size is not much, leaving very little room for text.
The choice:
❶ small devices, 6*1 lay outs small width
For smartphones the best approach. Will work in windowed versions on other devices
❷ medium screen - tablet, 3*2 lay out medium width
On small computers tablets a better presentation.
❸ pc laptop desktop screen, 2*3 lay out wide width
❹ Extreme wide, 1*6 lay outs There is no example given.
The goal is seeing whether the paragraphs are having evenness in content.
🎭 C-3.1.2 Culture: interpretation, frictions
The question: how to structure information for an understandable interpretation functionally. The question in this one is how frictions are when between several areas of interest when the people within a roles for mentioned task are cooperating. Everybody in a role has interests and they are not automatically aligned with the involved parties.
When there are six levels a split in: 1,2,3 or 6 are possible for activities and tasks. With 1 and 6 there are no alignment challenges other than a central point of control or all on their own.
The classic one is orgnsiational questions in a 9 plane: Dumping your core business out of the organisation doesn´t make any sense. Core business tasks (operations) and business (organisation) are two columns at the left.
Commercial technology and common knowledge are the two at the right.
Advice what to improve how to improve what to manage is in the middle.
❶ 2*2 plane compared 3*3 plane vertical
mindmap_9vlak_2crkl.jpg mindmap_9vlak_3crkl.jpg
2 levels
+ Internal contacts for issues.
- Frictions at external tech support.
+ Better long term continuity.
- Possible missing tech developments.
3 levels
- Every issue is an external order.
+ No frictions external tech support.
- Missing Long term strategy.
-/+ tech development alignment.
Frictions positioning - choices
Frictions are growing at point where the flows in relationships voor communication meet each another in a contrary direction. Having big circles wiht al lot of stations in the relationships problems caused by misundertanding confusion and diifferent targets. With a growing number of involved people the size of circle needs adjustments. In pictures where the small circle loops in the horizontals are not drawn:
❷ 2*2 plane compared 3*3 plane horizontal
mindmap_9vlak_2crkv.jpg mindmap_9vlak_3crkv.jpg
2 levels
+ Less frictions but a bigger circle.
- Every friction is more difficult.
+ Hierarchical distance is small.
- more direct involvmentare required.
3 levels
- More frictions in smaller circles.
+ Every friction is less difficult.
- Hierarchical distance increases.
-/+ Direct involments can get avoided..
❷,❸,❹,❺,.. 2*2, 3*3 combinations There are several more combinations possible in the 6*6 detailed reducing to simplicity in an organisations matrix but increasing in frictions. All depends in the involved sizing up to some size of scale. Above some unknown limit the scale itself will increase complexity and frictions.
🎭 C-3.1.3 What can we learn, 6*1 - 2*3?
Theoretical - Technical
The theory of many frameworks in a 2*2 3*3 and 6*6 setting took a lot of time. The functional presentation is heavily impacted by cultural mindsets. Only a nice figure without explanations doesn't give the path to insights.
Theoretical - Orchestration
Getting the many different theories overhauled into a combined structure was, is and will be the challenge. Resistance to change is futile. Better to direct change in an understandable structured positive way than letting things happen as they happen.
Theoretical - Functional
❶ Mind mapping starting from a single topic by to defined unknown split-ups well known.
❷ Mind mapping starting from a problem area to several, four, directions is not well known.
❸ Mind mapping using several ordered axes, multiple dimensions, is an unknown challenge.
Practical - Functional
A gap for jabes is how to do categorisations documentations. A 6*6 structure looks to be a very good candidate.
💡 Changing the mindset that a tool for the 6*6 categorisation, structuring, mind mapping is not a nice to have but more a must have.
The 6*6*(#n) content presentation tool should:
Practical - Orchestration
Even when seeing a direction it is very hard to get orchestration understandable. A surprise to learn by experience is how strong cultural mindsets in figures are. Unlearning and leering is difficult.
Practical - Technical
The technical layout was a question to solve a way to present structured content. Accidently a fit for the 6*6 categorisation structuring. Learning the technology is hard work not a persistent type of capabilities.
Ai missing ML

C-3.2 Communication - Interactions

Working with machines that process information, is a relative new topic of science. Human communications and interaction is classic.
How tot translate the physical world experiences into practical implementations in the cyber world is a big challenge by human understanding human communication.
Understanding and communicating about:
⚖ C-3.2.1 Multiple service topic lines in process chains
Processes
The concept of the Process models (USM Jan van Bon). This is changing the classic process flow block into a new struture. The mindset switch is that is not about coding, programming but about processes.
Using the word processes is a litte bit confusing being used in several contexts:
  1. USM-Process The term process is frequently misused in service management contexts.
    Defined as "a series of activities that are meaningful for the customer, with a specific goal", a process should be limited to activities.
  2. The representation of an activity unit. "Process model" or "Process quantum".
    I prefer "Process quantum" because it is a fit with "data quantum used in data mesh and avoiding the word model. The word model is often used in coding, programming, AI machine learning.
  3. Within "Process quantum" several processes are defined.

Requirements for processes, axioms:
To define a process architecture, we need more than just this simple definition. USM specifies 10 requirements that should be fulfilled for a process, before we can indeed call it a process and handle it as such:
  1. A process describes what has to happen successively, not the who or how.
  2. A process can be interpreted with a verb.
  3. A process can be counted.
  4. Processes are not depending upon practical conditions (◊).
  5. Processes have a customer-relevant and unique purpose.
  6. A process can be divided into sub-processes, but that does not change the process.
  7. A process model organizes the processes.
  8. An integral process model includes all service management activities.
  9. In an integrated process model, each activity occurs only once.
  10. All activities are steered using process control.

The process quantum, data quantum:
If we test the 'processes' from all current frameworks, standards, and reference architectures against these 10 requirements, it can be demonstrated that they all describe combinations of people, process and technology: the what, the who and the how. Therefore, they all fail the very first of the 10 requirements. They actually describe practices, and practices are not processes: practices are derived from processes by adding the who and the how to the what.

In that process model we find only five processes that are independent of the line of business of the organization. Four of these processes are reactive: they are triggered externally, by the customer.
USM-Process-model
In a figure see right side:
These reactive processes are used to respond to the customer's demand. One of the five processes is proactive: it is triggered internally, by the service provider's organization. his proactive process aims at structural improvements in the delivered services, managing all threats and innovations for service delivery.
These four demand processes are basic concepts: A generic "process quantum" being a combination with the "data quantum" of data-mesh.
In a figure see below: USM-Basic-process-layout
⚙ C-3.2.2 Process concecpts by change management
The abbreviations ETL ELT are used in data analytics. In the context of processes and services there is another meaning. It is associated using information straight way or mediated.
ELT Extract Load Transform, straight (rules)
Administrative processes are based on rules dictated by human decision makers: 👐 Rule processing with defined rules by humans are easily accepted but are hard in traceability and explainability, some examples:
👁 Defining the VAT (value added tax) and VAT rate for products.
👁 Defining priorities who is getting for what level in social support.
ETL Extract Transform Load, mediated (AI/ML)
There are a lot of technical machines working creating complicated new types of information by knowing causal interactions.
👐 AI/ML is not well understood, some examples:
👁 A numberplate recognition converting an image to a defined combination of letters and digits is an example for this kind of processes. Validation of the result by humans is easy, understanding the complex algorithms behind that is almost impossible.
👁 Knowing your position and planning routes to a destination is made very simple using smartphones GPS and route apps. Understanding how GPS works is very hard. How route planners are working achieving their results is for also very hards. Using those GPS locators and trusting route planners has become common wihtout many complaints on the the technical complexity.
USM Increasing number of catgories.
The process service, plan, build prepare, test, implement, in USM is not in elementary components. A proposal for split up: Dimensions, aside building an organisation ❶ enabling to fulfil product deliveries using that organisation ❷, there are more dimensions:
👐 These processing categories ❸.
👁 Time horizons ❹ from paste to future .
👁 Management control, hierarchy and social interactions ❺.
👁 Product management life cycle management and control ❻.
The number of dimensions are far too many for easy understanding (six mentioned).
⚒ C-3.2.3 Standard process patterns
artifacts in process patterns
The visible materialized data, information representations:
  1. Extract and load materials into a Landing area
  2. Validate the material at landing placing them into Staging
  3. Prepare Staging for transformation processing at Semantic
  4. Deliver transformations results into Databank
Between those data materialisations there are processing activities.
Information flow, using closed loops
Monitoring what is going on, closed loops on operational flows should be in place. When there is a high vital product data an additional flow evaluating new options before changing process flows is needed. In a figure: Process informationflow
Process change control by four lines
Changing process flows is done by changing orchestrated four dependent process activities in the standard pattern. In a figure: Process processlcm
rethink what has happened TN

C-3.3 Historical Practical Evolvement

Learning from examples is the start how it should be done.
Practising experimenting is the next stage.
Goal: innovating extending improving how it should be done.
The good message: there is a prospect for improvement.
The bad message: it will not be easy.
Prescriptive for actionable advice:
⚙ C-3.3.1 Exploding number of frameworkd & tools
Number of security frameworks
24 Great Cybersecurity Frameworks (Andrey Prozorov, Cybersecurity and Privacy Expert)
The list (12-2023): 2023 Matt Turck mad2023_mattturck
  1. ISO 27001 (ISMS)
  2. ISO 27002 (IS Controls)
  3. Standard of Good Practice for Information Security (ISF SoGP)
  4. NIST Cybersecurity Framework (CSF)
  5. NIST SP 800-53 (Security and Privacy Controls)
  6. CIS Critical Security Controls
  7. PCI DSS
  8. Katakri (Information Security Audit Tool for Authorities)
  9. COBIT Focus Area: Information Security
  10. Information Security Manual (ISM)
  11. New Zealand Information Security Manual (NZISM)
  12. Essential Cybersecurity Controls (ECC)
  13. SAMA Cyber Security Framework
  14. Cyber Essentials (UK)
  15. IT-Grundschutz
  16. CSA Cloud Controls Matrix (CCM)
  17. State of the art (TeleTrusT)
  18. Cybersecurity Capability Maturity Model (C2M2)
  19. CyberFundamentals Framework
  20. ETSI Cybersecurity Standards
  21. HITRUST CSF
  22. Open Information Security Management Maturity Model (O-ISM3)
  23. Secure Controls Framework (SCF)
  24. IEC 62443-2-1 (IACS Security Program)
  25. The Cyber Security Body Of Knowledge (CyBOK)
See also:
Number of big data tools
The 2023 MAD Landscape (Machine Learning, Artificial Intelligence & Data) Matt Turck
It has been less than 18 months since we published our last MAD landscape, and it has been full of drama.
When we left, the data world was booming in the wake of the gigantic Snowflake IPO, with a whole ecosystem of startups organizing around it.
Since then, of course, public markets crashed, a recessionary economy appeared and VC funding dried up. A whole generation of data/AI startups has had to adapt to a new reality.
Meanwhile, the last few months saw the unmistakable, exponential acceleration of Generative AI, with arguably the formation of a new mini-bubble. Beyond technological progress, it feels that AI has gone mainstream, with a broad group of non-technical people around the world now getting to experience its power firsthand.
The rise of data, ML and AI is one of the most fundamental trends in our generation. Its importance goes well beyond the purely technical, with a deep impact on society, politics, geopolitics and ethics.
Yet it is a complicated, technical, and rapidly evolving world that is often confusing even for practitioners in the space. There’s a jungle of acronyms, technologies, products and companies out there that are hard to keep track of, let alone master
 2023 Matt Turck mad2023_mattturck
Wat we can learn from this: 🤔 For maturity is expected that:
⚙ C-3.3.2 Change: Culture Leadership
P.O.17344_Assembly_Chalmers.gif
Association to physical manufacturing
The Evolution of Automotive Assembly Line—Part 1
The first cars were assembled long before the moving assembly line was invented. The car was assembled on one spot by a team of builders. Later, this was sometimes called “static assembly,” in contrast with the newer moving assembly lines.
Such static assemblies were usually not very efficient. The workers needed to know many different assembly operations, and hence had quite a learning curve before they could assemble cars well… resulting in many cars that were assembled NOT well (i.e., had quality flaws). Management was complex, as logistics needed to know which part had to go to which car and when. Often, the operators walked away from the car to get the needed parts, wasting time.
🤔 Wat we can learn from this:
End of the dream of a leadership - quichot.
The end of classic leadership
Don Quixote is a leader in a sad setting although his intentions were ethical clear. Cervantes life an interesting story of conflicts in the world. When first published, Don Quixote was usually interpreted as a comic novel.
After the French Revolution, it was better known for its central ethic that individuals can be right while society is quite wrong and seen as disenchanting.
In the 19th century, it was seen as a social commentary, but no one could easily tell "whose side Cervantes was on". Many critics came to view the work as a tragedy in which Don Quixote's idealism and nobility are viewed by the post-chivalric world as insane, and are defeated and rendered useless by common reality.
By the 20th century, the novel had come to occupy a canonical space as one of the foundations of modern literature.
🤔 Wat we can learn from this:
⚙ C-3.3.3 Visions future
Data Mesh concepts
This is a new hype wiht some interesting concepts. From: "Data Mesh Delivering Data-Driven Value at Scale" (book), the data (product) quantum.
Data Mesh - opertional plane, analytical plane, infratructure The environment for products in dimensions:
Functional
layer A - Operational plane
layer B - Analytical plane
layer C - Federated
computational governance

The Data quantum in dimensions:
Technical
layer i - Data, Metadata
layer ii - Code
layer iii - Infrastructure

data monolith to mesh
It´s an accepted convention that the monolithic data platform hosts and owns the data that logically belong to different domains.
Data mesh- data product
The idea:
🎯 Add a control connection at transformation process. (control: speed, safety)
The Data quantum in process flows:
Technical
input - flow features
output - flow results
control - closed loop, input
discover,
observe 		- closed loop, result

A figure, see right side

Still to do:
Push Pull system - Situation Input Action Result
Processing information using ICT is assembling parts of information into new information products. Where the data quantum is for transforming information the SIAR approach is more abstracted, generalised.
Situation Input Actions Results
A figure, see right side.
The production orders are (push): Product flow: left to right (clockwise, top).

The customer demand (pull): Demand flow: right to left (clockwise, bottom).
Requirements to set, document & validate
Information processing culture is lacking the culture for structured processes by layers and is lacking culture for sufficient explanations on these. The jabes proposal is about structuring processes. The documental architecting engineering and operating has to cover multiple attention points.
😉 Information on information processing: Jabes process Assurance
In a figure:
See left side
SIAR cycle

C-3.4 Processing flows VSM - Control

The SABSA "Sherwood Applied Business Security Architecture" developed independently from the Zachman Framework, but has a similar structure.
JABSA "Jabes Applied Business Systems Architecture": 😱 Every step for understanding needs elaboration.
⚖ C-3.4.1 JABSA reused principles
Combining Zachman and Sabsa, overhauling
Inventing from scratch is hard an prone to errors. Reusing existing knowledge to get a more mature approach is a better option. Detailed knowledge is recombined, reused:
JABSA generic
A 6*6 matrix set up: Using: Perspectives by 2*2 3*3 should be included in understanding.
The chosen words are not ideal in this first attempt, improvements are to be expected.
👁 Building products in flows in a figure: BPM use organisation
The diagonals are the big steps going from goal to result. Walking those diagonal lines is impossible, only going by smaller steps horizontal - vertical are possible.
To take notice of
This visualisation is:
😉 Needs attention for evaluations by cycle to start in the right bottom corner.
🤔 Hard to use to complete the horizontal flow lines.
The culture of a blocking mindset wanting to see segregations by organisational verticals.
🤔 Shows change project constraints in the generic added content for the diagonals.
PM triple constraint 👐 Constraints: "Project management triangle" (called also the triple constraint, iron triangle and project triangle) is a model of the constraints of project management. ...
In practice, however, trading between constraints is not always possible. For example, throwing money (and people) at a fully staffed project can slow it down. Moreover, in poorly run projects it is often impossible to improve budget, schedule or scope without adversely affecting quality. ...
The four:
  1. Modeling Scope, (functional diagonal)
  2. Flexibility profit (results diagonal)
  3. Quality budget, (functional diagonal)
  4. Integration time, (results diagonal)
are the constraints in the horizontals& verticals to manage.
⚒ C-3.4.2 JABSA Designing, building the organisation
engineering, reverse engineering
The showed 6*6 figures are a result of reverse engineering. After that trying to construct the parts into something new. Reverse engineering
Reverse engineering (also known as backwards engineering or back engineering) is a process or method through which one attempts to understand through deductive reasoning how a previously made device, process, system, or piece of software accomplishes a task with very little (if any) insight into exactly how it does so. Depending on the system under consideration and the technologies employed, the knowledge gained during reverse engineering can help with repurposing obsolete objects, doing security analysis, or learning how something works.
The hierarchical organisation is better to understand. This one is better (see SABSA) for usage how to change the activities.
There are now two representations of the same but with a different goal.
  1. From left to right: what is needed to enable the processing in a value stream for products.
  2. From left to right: How should organisations organised for processing delivering products.
    This one is a more common approach. There is big gap between context and details.
👁 Building the organisation in a figure:
BPM build organisation
🎭 In the mirror action axis from the SIAR cycle are not mirrored. Pull/push and steer shape serve are connected in other constraints. The order in the cycle is different: A healthy organisation is continuously changing between these tow approaches when either of the gets the hegemony there is no future.
Porter 5 forces
External forces
The diagonals represents what is in the circle of influence internally. Concerns triggered by external interactions are missing, applicable is: Porter’s Five Forces (2016 business-to-you)
is a framework that helps analyzing the level of competition within a certain industry. It is especially useful when starting a new business or when entering a new industry sector.
The figure is also a good fit in the flown orientation suppliers at the left, buyers at the right. The "rivalry" among existing competitors was meant externally but here also has a meaning internally, functional activities under pressure.
⚙ C-3.4.3 JABSA applied to information processing
Going for more standard cells in the 6*6 plane in the setting of information processing. An organisational structure is difficult to create and change, it must be adaptable for change. At the abstracted theoretical level there are standard focus area's.
What is left specific: 👁 Building the organisation standard abstraction in a figure:
BPM build organisation cit
👁 The diagonal mirror operating on a product for the standard abstraction in a figure: BPM use organisation cit
🎭 What is getting visible: Note: Interactions, closed loops, are only possible vertical and horizontal lines
elephant-blind-men

C-3.5 Processes Building Blocks - Control

The duality between information (states) and processes (transformations) is an complication at first sight. In reality it is a simplifications of administrative cyber processes.
reduced to:
💡 Intended information states.
💡 To avoid ones by safety, Deter Detect Defence.
💡 Processsing information into intended results (impact).
💡 To avoid ones by impact analyses descriptive & predictive
⚙ C-3.5.1 JABSA for Data-Information, data governance
information strategy
An information strategy is an indispensable capability. What is data governance and why does it matter? (techtarget 2024 )
Data governance is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal standards and policies that also control data usage.
Applying materialized Information usage
The materialized Information state has two perspectives: Explainability with traceability for both are becoming mandatory requirements at auditing.
👁 Operating information materialised products in a figure: mindmap BPMuse information
👁 Safety, security, for information materialised products in a figure: mindmap BPMuse safeinformation
🎭 The duality of the expected normal flow is what can go wrong. A healthy organisation implements safety closed loops that are independent of the normal expected value stream flow.
Building the organisation materialized information, delivery -safety
The build mirrors visualisations to enlarge:
👓 Mindmap BPMbuild information, states
Focus on how an organisation enables information processing 		mindmap BPMbuild safeinformation
👓 Mindmap BPMbuild safeinformation, process
Focus on how an organisation enables safe, secure, information processing 		mindmap BPMbuild information

⚙ C-3.5.2 JABSA for services, processes transformational flows
information strategy
An information strategy is an indispensable capability. What to with processed data and information on the processing is part of information strategy. data retention policy (techtarget 2024 )
A data retention policy is part of an organization's overall data management strategy.
The goal of data retention for these businesses is to allocate enough time to extract needed value from data while keeping data privacy and security considerations in mind. While businesses can draft their own requirements for data retention, there are also legal considerations as well that depend on factors such as geography. Other reasons a business would prioritize data retention could include the need for future data analyses.
Applying information transformation usage
The transformations of Information, processing, has two perspectives: Explainability with traceability for both are becoming mandatory requirements at auditing.
👁 Operating Building information transformations processes (algorithms) in a figure: mindmap BPMuse process
👁 Impact safety, explainability for transformation states in a figure: mindmap BPMuse safeinformation
🎭 The duality of the expected normal flow is what can go wrong. A healthy organisation implements safety closed loops that are independent of the normal expected value stream flow.
Building the organisation executing transformations, impact - explainability
The build mirrors visualisations to enlarge.
👓 Mindmap BPMbuild information, states
How organisations enables information processing, transformations 		mindmap BPMbuild process
👓 Mindmap BPMbuild safeinformation, process
How organisations enables impact concerns information processing states 		mindmap BPMbuild impactprocess

⚙ C-3.5.3 JABSA aligning the organisation to product flows
Aligning the organisation to product flow
The flow for a product is reviewed for the Jabes proposal.
What kind of gaps:
Using the organisation capabilities going into market operations.
What whe have now: Remember: there is not intention to define a hierarchical organisation with this.
For the operational structure: adding external relations, external processes to the previous internal 6*6 ones. The previous internal structure is collapsed into backend and a frontend is added. In a figure: BPMuse operations
The build mirrors visualisations to enlarge.
👓 Mindmap BPMbuild operations
How organisations enables an operational structure 		mindmap BPMbuild operations

Teasing evaluations of the opertional strcuture.
The diagonals are the lines of interest. The only allowed interactions are vertical and horizontal. Interest conflicts:
Data monetizing journey

C-3.6 Controlling Organisation & Business

Once Dorothy and her colleagues made the journey to OZ, they quickly found out that there was no there, there. The Wizard simply told her what she really should have known all along.
Dorothy and her companions just had to figure out how to reframe their own perceived shortcomings and recast them as strengths to achieve real transformation.
ndma_gears jpg
C-3.6.1 The Five Organizational Systems
Organizational transformation: a matter of "reprogramming" signals.
the components of an organizational operating model
Signals come from five organizational systems: These are all attributes of the organization (not the people currently in it, nor the work they do).
What's not on the list and why: people and strategies.
... Many leadership frameworks include people and business strategies. While these are very important, not every important issue is an aspect of organizational design.
Organizational systems have three qualities: Talents and skills are certainly a leadership challenge, but not an organizational design issue. People live within the organizational ecosystem, and their work is guided by its signals. But people are not something that leaders can "program." And they take their talents with them when they leave; they're not an attribute of the organizational system that lives on without them.
Strategies are also not an attribute of the organization. Customers' strategies are an input; and the organization's strategies are an output. But neither meaning of the word "strategy" is an aspect of the design of the organization.
In fact, well-designed organization continually align themselves with customers' strategies, and continually define and execute their own strategies.
This framework of five organizational systems refers to the organizational ecosystem, not the people who live within it or its inputs (e.g., customers' strategies) and outputs (including its own strategies).
🕳 Reporting understanding signals
informs ()Operations Research & Analytics)
Operations research (O.R.) is defined as the scientific process of transforming data into insights to making better decisions.
Analytics is the application of scientific & mathematical methods to the study & analysis of problems involving complex systems. There are three distinct types of analytics: ...
Though similar in definition, and there are instances of overlap, Analytics and Operations Research are actually two unique but related fields. Analytics helps realize business objectives by analyzing data to create predictive models for forecasting and optimizing business processes for enhanced performance.
O.R. employs highly developed methods using advanced tools and techniques to provide analytical power. This is something that no ordinary software or spreadsheet can deliver out of the box. O.R. draws upon the latest analytical technologies including simulation, optimization, probability, and statistics.
C-3.6.2 Scale or flow a system
why do we believe in economy of scale (John Seddon 2010) This paper evaluates some experiences where the beliefs in scale caused countereffects in results for efficiency.
Variety
At the heart of the problem is the issue of variety. A key difference between service and manufacturing is the nature of demand. Studying customer demands into service organisations always reveals high variety. It is variety that stymies management’s attempts to control the work and deliver high quality service. The problem is amplified by the division of work between a front and a back office.
Volume (Scale)
As in other mass-production systems, managers believe that by breaking down and standardising tasks they will gain economies of scale. They take as given that the work will arrive in the right places, be done in the standard times and returned within the service levels. Careful study of the work shows that this rarely happens. The fragmentation of work creates waste in the back office and failure demand in the front office.
Velocity
We can say that: These principles enable the system to absorb variety; hence we can predict that service will improve as costs fall.
Managing the development systems
"Managing the development of large software systems" is a paper (Winston W. Royce 1970) often refered to but hardly ever being read. Some notes from that paper.
Starting simple: ... 👁 The real monetary value of good documentation begins downstream in the development process during the testing phase and continues through operations and redesign. ...
👁 Involve the customer.
For some reason what a software design is going to do is subject to wide interpretation even after previous agreement. It is important to involve the customer in a formal way so that he has committed himself at earlier points before final delivery. To give the contractor free rein between requirement definition and operation is inviting trouble. ...
These are the principles of lean, agile for a prudent systems, even the documentation is belonging to that. Good documentation failed miserably, now it is becoming mandatory by regulations. You need to be able to explain:
C-3.6.3 Jabes - Jabsa mindsets
PDCA - DMAIC - SIAR , lean
The PDCA, plan do check act, DMAIC define measure analyse improve and SIAR situation input activity/analyse, result request I have often mentioned as strong related to each other. The abstraction of those cycles is better visualised with in mind acting on the value stream using a pull-push. Missing link: siar pdca dmaic

Mindmap usage with four related areas is not that uncommon.
Other mindmap models with a similar approaches:
Model Situation South Source East Activity North Result East
- Control Governance Ideation Creation Operations Execution Validation Evaluation
ANesbit Innovation Influence Insight Impact
Porter Product Substation Suppliers Product competitors Buyers
Murty &
Gorur 		Process Improvements Unlearn & learn
Mindset shift		 Adoption
Adaption 		Communication
Tech-Tools

Model Improve SE Analyze SW Measure NW Define / Detect NE
IMAR EL Inspire Appraise Mobilize Reflect


in a figure see right side.
😉 JABSA is internal backend and JABES the internal frontend.
External influence are at both at backend (Plan Analyse) and frontend (control improve act).
😲 The cycles PDCA and DMAIC cycles are on diagonals interacting between two involved parties. In this both the diagonals and cycles are giving the used letters. Seeing and recapping the diagonals in JABSA this is a sensible coincidence.
😲 The CIA letters have the meaining of: control improve act, this is at theoretical Steer. Another meaning is confidentiality integrity availablity, this at the floor for safety. This is a sensible coincidence.
💡 Breaking the vicious circle by locked in involved staff.
The vicious circle in which information processing and information security is stuck can be broken with cultural change. The best option is by a not involved source. An option for this is motivatian by HR for lean. It offers the prospect of more good changes.
The Anti-Lean Movement (2024 B Emiliani) The opportunity to create a step change is yours if you want it. Do you want it?
New innovations, changes
A mind map approach, Jabes is about this and this site setup is conforming that mind map.
👉🏾 The horizontal columns:
5w1h Engineering Product innovations
What Bills of Material bpm
Steer 		⇄
⇆		 What the organisation is needing, wanting to achieve.
Impediment: missing notion for what kind of tools are needed.
Impediment: missing accountability for information, safety included
How Functional Specs sdlc
Serve 		⇄
⇆		 Tools technology supporting the organisation goals.
Impediment: missing notion LCM operational & analytical planes
Impediment: missing notion execution in information flows services
Where Drawings (or Geometry) bianl
Shape 		⇄
⇆		 Helping in choices tools and organisation to achieve their goals.
Impediment: The missing "functional product management" (PM). Notion is focussed on project management (PM).
Who Operating Instructions data
Data 		⇄
⇆		 Locations hierarchy and the way of structuring (naming conventions) and using artifacts (history archive. Both are proposals.
An organisational structure for Jabes (timing: not valid now)
When Timing Diagrams meta
Jabes 		⇄
⇆		 A product and framework (operating instructions) proposal.
A 6*6 mind map and documentation tools is needed.
To solve: metadata content aligned to a goal.
Which Design Objectives math
Know 		⇄
⇆		 An impact of the product.
Background from examples into future goals to achieve.


The context ordering in the theoretical rows are reversed to what the hierarchical order is. Starting both at logic the subject, topic is identical only the interpretation of how it used (functional) and how it is technical working is differnt. Analysing the understanding of relationships starts with what is known, "logic".
👎🏾 The vertical columns:
Abstraction Why considerations
3 context Theoretical_Technical ⇄ What executives, decision makers are doing.
Missions by visions
2 concept Theoretical_Orchestration ⇄ Advisories consultancies
1 Logic Theoretical_Functional ⇄ What product persons have interpreted from missions
1 Logic Practical_Functional ⇄ What product persons are doing to fulfil missions
2 Physical Practical_Orchestration ⇄ Advisories consultancies
3 Details Practical_Technical ⇄ What the realisations are on the floor.
operations (Gemba)

Categorizing content, start with the why
🕳 Understanding information processing, the new buzzword AI.
There are at least 4 dimensions. these are:
  1. execution: people / methods, processes, technology
  2. policy: purpose/value, communication, data/objects
  3. time: immediate, short, long
  4. hierarchy; control, coordination, operation
No matter how you try to squash that, too much will be lost. In addition:
  1. there are: many variants of what an algorithm entails (technology), so a 5th.
    Everything is now framed as AI and a lot of AI is no longer seen as such.
That doesn't make it easier for understanding.
C-3.6.4 Ongoing to do it
Missing link devops meta design bpm design data design meta design math
These are practical data experiences.

technicals - math generic - previous
data, generic toolings 👓 next.



Others are: concepts requirements: 👓
Data Meta Math
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🚧  Variety Act on Cyber Change ALC-V* knowtc 🚧
  
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