data BI techn

Reference	Squad	Abbrevation
RO-1 Introductions for functional details at realisations
RO-1.1 Contents	contents	Contents
RO-1.1.1 Looking forward - paths by seeing directions
RO-1.1.2 Local content
RO-1.1.3 Guide reading this page
RO-1.1.4 Progress
RO-1.2 The technological approach in performance	fncF6x6_02	Frame-ref
RO-1.2.1 .................................................stions
RO-1.2.2 .................................................
RO-1.2.3 .................................................nor
RO-1.2.4 .................................................ies
RO-1.3 Competing functionality vs safety to realisation	fncF6x6_03	ZarfTopo
RO-1.3.1 ..............................................e
RO-1.3.2 ..............................................ensions
RO-1.3.3 ..............................................s
RO-1.3.4 ..............................................ions
RO-1.4 Defining taxonomies - concepts - ontology	fncF6x6_04	ZarfRegu
RO-1.4.1 ...................................
RO-1.4.2 ...................................logy: 1* dimensions
RO-1.4.3 ...................................& implications
RO-1.4.4 ...................................& implications
RO-1.5 Defining temporal boundaries dependencies	fncF6x6_05	SmartSystem
RO-1.5.1 .......................................ext
RO-1.5.2 .......................................- good regulator
RO-1.5.3 .......................................t-abstraction
RO-1.5.4 .......................................ons to clear
RO-1.6 Defining what is learned for systems maturity	fncF6x6_06	ReLearn
RO-1.6.1 ...............................................odel
RO-1.6.2 ...............................................s
RO-1.6.3 ...............................................nt
RO-1.6.4 ...............................................risis
RO-2 Anchorpoints for functional details at realisations
RO-2.1 Using standard patterns for component in lines	fncR6x6_01	Knowium
RO-2.1.1 ..................................................
RO-2.1.2 ...................................................ns
RO-2.1.3 ...................................................gies
RO-2.1.4 ...................................................ns
RO-2.2 Performance of the processing for flow	fncR6x6_02	P&S-ISFlw
RO-2.2.1 ................................................ons
RO-2.2.2 ................................................issions
RO-2.2.3 ................................................ns
RO-2.2.4 ................................................ssions
RO-2.3 Tradeoffs in achieving functionality vs safety	fncR6x6_03	P&S-ISMtr
RO-2.3.1 ................................................lisations
RO-2.3.2 ................................................lisations
RO-2.3.3 ................................................ions
RO-2.3.4 ................................................king
RO-2.4 Understanding taxonomies - concepts - ontology	fncR6x6_04	P&S-Pltfrm
RO-2.4.1 ....................................................ons
RO-2.4.2 ....................................................s
RO-2.4.3 ....................................................ions
RO-2.4.4 ....................................................ons
RO-2.5 Understanding temporal boundaries dependencies	fncR6x6_05	Fractals
RO-2.5.1 ...................................................flow
RO-2.5.2 ...................................................or
RO-2.5.3 ...................................................ability
RO-2.5.4 ...................................................tion
RO-2.6 Understanding for what drives systems maturity	fncR6x6_06	Learn-I
RO-2.6.1 ...............................................ess
RO-2.6.2 ...............................................
RO-2.6.3 ...............................................hy
RO-2.6.4 ...............................................egulator
RO-3 Impacts consequences for functional details at realisations
RO-3.1 Using the understanding continuum practical	fncT6x6_01	Know_npk
RO-3.1.1 ....................................................rns
RO-3.1.2 .................................................... shifts
RO-3.1.3 ....................................................s
RO-3.1.4 ....................................................s
RO-3.2 Using the emergence pragnanz gestalt	fncT6x6_02	Gestium
RO-3.2.1 ......................................................erns
RO-3.2.2 ......................................................tions
RO-3.2.3 ......................................................ons
RO-3.2.4 ......................................................ts
RO-3.3 Using the "center of gravity" in value streams	fncT6x6_03	Stravity
RO-3.3.1 ..................................................atterns
RO-3.3.2 ..................................................ns
RO-3.3.3 ..................................................ons
RO-3.3.4 ..................................................ts
RO-3.4 Human Capital in systems for capabilities	fncT6x6_04	Human-cap
RO-3.4.1 ................................................
RO-3.4.2 ................................................implify
RO-3.4.3 ................................................ractals
RO-3.4.4 ................................................efs
RO-3.5 Changing systems information age C&C	fncT6x6_05	Evo-InfoAge
RO-3.5.1 ............................................s
RO-3.5.2 ............................................rgent types
RO-3.5.3 ............................................tions
RO-3.5.4 ............................................ systems
RO-3.6 Touching transcendental boundaries in learning	fncT6x6_06	Learn-@2
RO-3.6.1 ..................................................whole?
RO-3.6.2 ..................................................y
RO-3.6.3 ..................................................rks
RO-3.6.4 ..................................................ems

Road from nowhere to noweher North hemis

RO-1.2 The technological approach in performance

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⟲ RO-1.2.1 Info

butics

The Tragic mismatch in data strategy

A review on the topic of buzz and investments: "Organizations do not need a Big Data strategy; they need a business strategy that incorporates Big Data" Data Strategy: Tragic Mismatch in Data Acquisition versus Monetization Strategies. (LI: Bill Schmarzo 2020.)

The Internet and Globalization have mitigated the economic, operational and cultural impediments traditionally associated with time and distance. We are an intertwined global economy, and now we realize (the hard way) that when someone sneezes in some part of the world, everyone everywhere gets sick. We are constantly getting punched in the mouth, and while we may not be sure from whence that punch might come next (pandemic, economic crisis, financial meltdown, climate change, catastrophic storms), trust me when I say that in a continuously transforming and evolving world, there are more punches coming our way.
my next two blogs are going to discuss: How does one develop and adapt data and AI strategies in a world of continuous change and transformation? It"s not that strategy is dead (though at times Strategy does look like an episode of the "Walking Dead"); it"s that strategy - like every other part of the organization and the world - needs to operate in an environment of continuous change and transformation.
Organizations spend 100"s of millions of dollars in acquiring data as they deploy operational systems such as ERP, CRM, SCM, SFA, BFA, eCommerce, social media, mobile and now IoT. Then they spend even more outrageous sums of money to maintain all of the data whose most immediate benefit is regulatory, compliance and management reporting. No wonder CIO"s have an almost singular mandate to reduce those data management costs (hello, cloud). Data is a cost to be minimized when the only "value" one gets from that data is regulatory, compliance and management risk reduction.

Companies are better at collecting data, about their customers, about their products, about competitors, than analyzing that data and designing strategy around it. Too many organizations are making Big Data, and now IOT, an IT project. Instead, think of the mastery of big data and IOT as a strategic business capability that enables organizations to exploit the power of data with advanced analytics to uncover new sources of customer, product and operational value that can power the organization's business and operational models.
To exploit the unique economic value of data, organization"s need a Business Strategy that uses advanced analytics to interrogate/torture the data to uncover detailed customer, product, service and operational insights that can be used to optimize key operational processes, mitigate compliance and cyber-security risks, uncover new revenue opportunities and create a more compelling, more differentiated customer experience. But exactly how does one accomplish this?

By focusing on becoming value-driven, not data-driven.

Technology push focus BI tools.

The technology offerngs are rapidly changing the last years (as of 2020). Hardware is not a problemtic cost factor anymore, functionality is. hoosing a tool or having several of them goes with personal preferences.
This has nothing to do with hard facts but everything with things like my turf and your fault.

This has nothing to do with hard facts but everything with things like my turf and your fault.

Different responsible parties have their own opinion how conflicts should get solved. In a technology push it is not the organisational goal anymore. It is showing the personal position inside the organisation.
🤔 The expectation of cheaper and having better quality is a promise without warrants .
🤔 Having no alignment between the silo´s there is a question on the version of the truth.

Just an inventarization on the tools and the dedicated area they are use at: Mat Turck on 2020 , bigdata 2020 An amazing list of all,kind of big data tools at the market place.

Road from nowhere to nowehere Middeterain hemis

RO-1.3 Competing functionality vs safety to realisation

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⟲ RO-1.3.1 Info

Safety first by design, a pracatical case II

The question: Why spending capitals on hiring, while their best people walk out the door?
is about culture trust, ethics, conflicts, commitment, accountability, team results for the service outcome. - A limited list of culture killers - (LI: P.Evans 2025) That's because a high-performing culture isn't built, it's engineered. And most leaders don't realise they've hard-coded failure into their system. I've seen it happen across startups, scale-ups, and global giants...

Great people
Great products
Slowly pulled apart by how they operate internally.

Because culture isn't built through slogans on the wall. It's the unseen behaviours that either build trust or break it. Here are 8 hidden culture killers that quietly drain performance:

'"We're a family" ➡ Sounds warm, but it blurs boundaries and excuses.
👉🏾 Instead: Build a team, not a family. Clear roles and fair expectations create psychological safety, not forced intimacy.
Micromanagement ➡ Kills initiative, grows dependence on leaders, and destroys creative confidence.
👉🏾 Instead: Replace control with clarity. Define outcomes, not tasks, and let people own how they get there.
Too many managers, not enough doers ➡ Suddenly, meetings multiply, progress slows, and still, no one's held accountable.
👉🏾 Instead: Flatten decision-making. Reward action over alignment.
Ignoring feedback ➡ If people stop speaking up, you've already lost them.
👉🏾 Instead: Build feedback loops into your system. Retros, pulse surveys, open channels. But remember, listening is only powerful if it leads to visible change.
Decisions behind closed doors ➡ Secrecy leads to suspicion faster than any pay gap.
👉🏾 Instead: Default to transparency. Share the '"why" behind decisions, not just the '"what." It builds trust and alignment faster than any "all-hands" speech.
Overloading top performers ➡ You don't reward excellence by exhausting it.
👉🏾 Instead: Scale their impact, not their workload. Automate, delegate, and invest in systems that protect your best people from burnout.
No work-life boundaries ➡ If rest feels like guilt, performance will collapse.
👉🏾 Instead: Treat recovery as performance infrastructure. Model it yourself, when leaders rest, permission follows.
Silent meetings➡ When the same voices dominate, innovation slowly dies.
👉🏾 Instead: Engineer participation. Rotate facilitators, ask for written input. Inclusion is a design choice.
You can't just '"hope" your culture into being. You have to engineer it. Every system either builds trust or breaks it, and if you don't fix it, someone else will leave because of it.

Construction: existing systems that are hard to change

Construction regulations for 2025 focus heavily on sustainability, safety, and digitalization, with key changes including stricter energy performance, new Digital Product Passports (DPP) for materials in the EU, updated health & safety roles (like registered safety managers), and a push for greener building methods (heat pumps, solar). In the UK, the Building Safety Levy and new protocols for remediation orders are emerging, while globally, there's a trend towards clearer, faster permitting and greater accountability in construction. Key Themes & Regulations

Sustainability & Energy (EU & UK Focus):
- Digital Product Passports (DPP): Mandatory digital IDs for construction products under the EU's Ecodesign Regulation, tracking materials, performance, and recyclability.
- Energy Efficiency: Stricter standards for new builds, pushing low-carbon heating (heat pumps) and better insulation.
- Embodied Carbon: Increasing focus on calculating and reducing the carbon footprint of materials.
Health & Safety (Global Updates):
- Professional Registration: Introduction of registered Construction Health & Safety Managers (CHSM) in some regions (e.g., South Africa draft regs) to elevate standards.
- Ergonomics: Greater emphasis on worker well-being and preventing musculoskeletal disorders.
- Notification Changes: Some areas are expanding the scope of all construction work requiring notification to authorities, not just high-risk activities.
Building Safety (UK Specific):
- Building Safety Levy: A new levy on new homes in England to fund remediation of building safety defects.
- Legal Protocols: New court guidance expected for building safety remediation orders and liability orders.
Permitting & Process (EU Trend):
- One-Stop Shops: Calls for simplified, digital, single-permit systems with clearer timelines for approvals.
Legal Protocols: New court guidance expected for building safety remediation orders and liability orders.

What it Means for You (General)

Design for Green: Incorporate heat pumps, solar, and high insulation from the start.
Track Materials: Be ready for DPP requirements and provide detailed environmental data.
Elevate Safety: Expect new training and potentially registered safety roles.
Expect More Scrutiny: Authorities are increasing oversight on safety, sustainability, and permit compliance.

Note: Regulations vary significantly by country.
Guide to Construction Products Regulation (CPR) The Construction Products Regulation (CPR) is a pivotal EU legislation that sets standardized safety, performance, and environmental impact requirements for construction products across the EU. Originally established in 2011 to streamline the circulation of construction products within the Single Market through standardized guidelines, the CPR was updated in 2024 to address modern environmental challenges, advancing sustainability and transparency in the construction sector.
Health:
cdisc In July 2022, the FDA published, in Appendix D, to their Technical Conformance Guide (TCG), a description of additional variables they want in a Subject Visits dataset. A dataset constructed to meet these requirements would depart from the standard, so validation software would create warnings and/or errors for the dataset. Such validation findings can be explained in PHUSE?s Clinical Study Data Reviewer?s Guide (cSDRG) Package. phuse The Global Healthcare Data Science Community Sharing ideas, tools and standards around data, statistical and reporting technologies phuse PHUSE Working Groups bring together volunteers from diverse stakeholders to collaborate on projects addressing key topics in data science and clinical research, with participation open to all.

Road from nowhere to nowehere Middeterain hemis cold

RO-1.4 Defining taxonomies - concepts - ontology

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⟲ RO-1.4.1 Info

The 4 leadership behaviors that drive transformation

An email promotion: "Many leaders discover: they are the problem." (email: lean.org. 2025) Tools account for 20% of success. Leadership behaviors account for 80%. David Mann, in his research on lean management systems, found that "implementing tools represents at most 20% of the effort in lean transformations; the other 80% is expended on changing leaders' practices and behaviors, and ultimately their mindset." Yet most organizations invest heavily in tool training while treating leadership development as optional.
Four behaviors that drive transformation:

Go See (Gemba)
Regular presence where value is created. To understand, not inspect.
Ask Why (Coach)
Develop capability through questions. Build scientific thinking.
Show Respect (Safety)
Create environments where problems surface early.
Connect to Strategy (Hoshin)
Ensure every level understands how daily work supports objectives.

These aren't separate activities. They're interconnected behaviors that create the management system for sustained performance.
Daily management boards drive problem-solving (not just tracking). Teams catch issues early because they understand targets and feel safe surfacing problems. From "Managing on Purpose" (book): "Hoshin kanri is an excellent opportunity for leaders to learn to lead by responsibility as opposed to authority."

butics

Moral Complexity of Organisational Design (LI:R.Claydon 2025) Buurtzorg has become a kind of organisational Rorschach test. In his original essay, Stefan Norrvall reads it through a lens of organisational physics:

complexity is conserved,
work is stratified,

and Buurtzorg works because it relocates integrative load from managers into small whole-task teams, architecture, and an unusually supportive Dutch welfare ecosystem. In response, Otti Vogt argues that this frame is ontologically and morally too thin: Buurtzorg is not just a clever cybernetic design, but a solidaristic, post-neoliberal project grounded in care ethics, widening moral circles, and a refusal to treat nursing as timed piecework.

Certainty uncertainty in the theory of constraints

Continuation of the LI article on TOC is claiming TOC felt as being incomplete but the question is what that is. The Illusion of Certainty (LI: Eli Schragenheim Bill Dettmer 2025)
❶ A typical example of ignoring uncertainty is widespread reliance on single-number discrete forecasts of future sales. Any rational forecast should include not just the quantitative average (a single number), but also a reasonable deviation from that number. The fact that most organizations use just single-number forecasts is evidence of the illusion of certainty.
Organizations typically plan for long-term objectives as well as for the short-term. A plan requires many individual decisions regarding different stages, inputs or ingredients. All such decisions together are expected to lead to the achievement of the objective. But uncertainty typically crops up in the execution of every detail in the plan. This forces the employees in charge of the execution to re-evaluate the situation and introduce changes, which may well impact the timely and quality of the desired objective.
What motivates people to make the decisions that they do? Many readers will be familiar with Abraham Maslow's hierarchy of needs. Maslow theorized that humans have needs that they strive to satisfy. Further, Maslow suggested that it's unsatisfied needs that motivate people to action. Maslow also suggested that human needs are hierarchical. This means that satisfying needs lower in the hierarchy pyramid captures a person's attention until they are largely (though not necessarily completely) satisfied. At that point, the these lower level needs become less of a motivator than unsatisfied higher level needs. The person in question will then bend most of his or her efforts to fulfilling those needs.

The Dod Strategy statement knowledge management: data safety

DoD data strategy (2020) Problem Statement
Make Data Secure As per the DoD Cyber Risk Reduction Strategy, protecting DoD data while at rest, in motion, and in use (within applications, with analytics, etc.) is a minimum barrier to entry for future combat and weapon systems. Using a disciplined approach to data protection, such as attribute-based access control, across the enterprise allows DoD to maximize the use of data while, at the same time, employing the most stringent security standards to protect the American people. DoD will know it has made progress toward making data secure when:

	Objective	information Safety
1	Platform access control	Granular privilege management (identity, attributes, permissions, etc.) is implemented to govern the access to, use of, and disposition of data.
2	BIA&CIA PDCA cycle	Data stewards regularly assess classification criteria and test compliance to prevent security issues resulting from data aggregation.
3	best/good practices	DoD implements approved standards for security markings, handling restrictions, and records management.
4	retention policies	Classification and control markings are defined and implemented; content and record retention rules are developed and implemented.
5	continuity, availablity	DoD implements data loss prevention technology to prevent unintended release and disclosure of data.
6	application access control	Only authorized users are able to access and share data.
7	information integrity control	Access and handling restriction metadata are bound to data in an immutable manner.
8	information confidentiality	Access, use, and disposition of data are fully audited.

Retrosperctive for applying collective intelligence for policy.

Ideas into action (Geoff Mulgan )
What's still missing is a serious approach to policy. I wrote two pieces on this one for the Oxford University Press Handbook on Happiness (published in 2013), and another for a Nef/Sitra publication. I argued that although there is strong evidence at a very macro level (for example, on the relationship between democracy and well-being), in terms of analysis of issues like unemployment, commuting and relationships, and at the micro level of individual interventions, what's missing is good evidence at the middle level where most policy takes place. This remains broadly true in the mid 2020s.
I remain convinced that governments badly need help in serving the long-term, and that there are many options for doing this better, from new structures and institutions, through better processes and tools to change cultures. Much of this has to be led from the top. But it can be embedded into the daily life of a department or Cabinet. One of the disappointments of recent years is that, since the financial crisis, most of the requests to me for advice on how to do long-term strategy well come from governments in non-democracies. There are a few exceptions - and my recent work on how governments can better 'steer' their society, prompted by the government in Finland, can be seen in this report from Demos Helsinki.
During the late 2000s I developed a set of ideas under the label of 'the relational state'. This brought together a lot of previous work on shifting the mode of government from doing things to people and for people to doing things with them. I thought there were lessons to learn from the greater emphasis on relationships in business, and from strong evidence on the importance of relationships in high quality education and healthcare. An early summary of the ideas was published by the Young Foundation in 2009. The ideas were further worked on with government agencies in Singapore and Australia, and presented to other governments including Hong Kong and China. An IPPR collection on the relational state, which included an updated version of my piece and some comments, was published in late 2012.
I started work on collective intelligence in the mid-2000s, with a lecture series in Adelaide in 2007 on 'collective intelligence about collective intelligence'. The term had been used quite narrowly by computer scientists, and in any important book by Pierre Levy. I tried to broaden it to all aspects of intelligence: from observation and cognition to creativity, memory, judgement and wisdom. A short Nesta paper set out some of the early thinking, and a piece for Philosophy and Technology Journal (published in early 2014) set out my ideas in more depth. My book Big Mind: how collective intelligence can change our world from Princeton University Press in 2017 brought the arguments together.

The lean project shop

The project shop is associated with not possible applying lean thoughts. The project shop, moving the unmovable a lean appraoch, is altought possible to see getting done in lean approache. Does it or are there situations where new technology are implementing a lean working way.

allaboutlean projectshop - building ship

It is using a great invention of process improvement over and over again. That is: the dock. Building in the water is not possible. Building it ashore is giving the question how to get it into the water safely.
🔰 Reinvention of patterns.
Moving something that is unmovable.
Changing something that has alwaus be done tath wasy.

Minimizing time for road adjustment, placing tunnel. Placing it when able to move done in just 3 days. Building several months.
See time-lapse. 👓 Placing the tunnel was a success, a pity the intended road isn´t done after three years.

The project approach of moving the unmovable has been copied many times with the intended usage afterwards. rail bridge deck cover The approach is repeatable.
💡 Reinvention of patterns. Moving something that is unmovable.

A generic mindshift for integrated governance

Business Integrated Governance (BIG) is a framework that aligns governance, risk management, and compliance (GRC) with business strategy and operations to enhance decision-making and drive sustainable performance. Key Aspects of Business Integrated Governance (BIG):

Alignment with Business Strategy: Governance frameworks are designed to support and drive business goals rather than just ensuring regulatory compliance.
Risk Management Integration: Governance processes include proactive risk management, identifying and mitigating risks that could impact business performance.
Performance-Driven Governance: Decision-making is data-driven and focused on improving efficiency, effectiveness, and business outcomes.
Stakeholder-Centric Approach: Governance considers the interests of all stakeholders, including shareholders, employees, customers, and regulators.
Technology & Automation: Digital tools and AI are often used to streamline governance processes, ensuring transparency and real-time monitoring.
Agility & Adaptability: Governance frameworks are flexible and adaptable to changing market conditions, regulatory requirements, and organizational needs.

The challenge of BIG is to shift from relying on a patchwork of governance practices to defining and managing fully integrated governance operation with the necessary Capability. For any organisation, a well-defined (BIG) Capability primarily enables the effective communication of strategic expectations, followed by ongoing systematic performance oversight, decision making, re-steering, and course corrections, leading to greater strategic outcomes and agility.
BIG start

See right side

Generic governance a double loop.
Capability achievement requires the consideration of several crucial elements, including a strategy information model, integrated operating models, and a governance regime. Clear accountability management, suitable enablers (tools, processes, standards), business support and assurance (orchestrating the operation), data / information solutions and leadership are all essential for success.

Road from nowhere to nowehere Middeterain hemis double timed

RO-1.5 Defining temporal boundaries dependencies

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⟲ RO-1.5.1 Info

The Certainty loophole in wanting predictability

(LI: A.Constable 2025) In strategy, understanding the distinction between scenarios and forecasts can be crucial to achieving long-term success. The distinction is this:

Forecasts ➡ What is likely to happen:
- Based on historical data, trends, and expected developments
- Provides a single probable outcome
- Often used for budgeting and short-term planning
Scenarios ➡ What could happen:
- Considers multiple possible futures based on key uncertainties
- Helps organisations prepare for different potential outcomes
- Critical for long-term strategic resilience and stress testing

While forecasts help navigate the near future, scenario planning equips organisations to anticipate shifts, adapt strategies, and stay ahead in an unpredictable world.

Lean accounting removing certaintity constraints

The Danaher Business System (DBS), developed by Mark DeLuzio, is a comprehensive Lean-based operating model that transformed Danaher Corporation into one of the most successful industrial conglomerates in the world. It integrates strategy deployment, continuous improvement, and cultural alignment into a unified system for operational excellence.

Element	Description
Lean foundation	Built on Toyota Production System principles, emphasizing waste elimination, flow, and value creation.
Policy Deployment (Hoshin Kanri)	Strategic alignment tool that cascades goals from top leadership to frontline teams.
Kaizen culture	Continuous improvement through structured problem-solving and employee engagement.
Visual management	Dashboards, metrics boards, and process visibility tools to drive accountability and transparency.
Standard work	Codified best practices for consistency, training, and performance measurement.
Lean accounting	Developed by DeLuzio to align financial systems with Lean operations , focusing on value streams rather than traditional cost centers.

Mark DeLuzio's Role and Philosophy

Architect of DBS: As VP of DBS, DeLuzio led its global deployment and helped Danaher become a benchmark for Lean transformation.
Lean Accounting Pioneer: He introduced the first Lean accounting system in the U.S. at Danaher's Jake Brake Division.
Strategic Integrator: DeLuzio emphasized that Lean must be tied to business strategy , not just operational tools.
Respect for People: A core tenet of DBS, ensuring that transformation is sustainable and human-centric.

Activity	Description
Eliminating waste in accounting processes	Traditional month-end closes and cost allocations often involved redundant steps. Lean Accounting applies value-stream mapping to streamline closing cycles, freeing finance teams to focus on strategic analysis
Value-stream based reporting	Instead of tracking costs by departments, Lean Accounting organizes them by value streams , the end-to-end activities that deliver customer value. This provides clearer insight into profitability tied to actual products or services
Real-time decision support	Lean Accounting emphasizes timely, actionable data rather than lagging reports. This enables leaders to make faster, more informed investment and governance decisions
Continuous improvement in finance	Just as Lean manufacturing fosters kaizen, Lean Accounting embeds continuous improvement into financial governance, ensuring reporting evolves with operational needs
Integration with agile governance	Lean financial governance adapts investment tracking to modern delivery methods (agile, hybrid, waterfall), ensuring funding and prioritization align with how initiatives are actually execute
Transparency and cultural alignment:	By eliminating complex cost allocations and focusing on value creation, Lean Accounting fosters a culture of openness and accountability across departments

Why This Matters for Governance Traditional accounting often obscured the link between operations and financial outcomes. Lean Accounting reshaped governance by:

Making financial metrics operationally relevant.
Aligning investment decisions with customer value creation.
Enabling adaptive governance models that support agile and Lean transformations.

This is why companies like Danaher, GE, and others used Lean Accounting as a cornerstone of their governance systems , it provided clarity, speed, and alignment between finance and operations.

Using BI analytics

Using BI analytics in the security operations centre (SOC).
This technical environment of bi usage is relative new. It is demanding in a very good runtime performance with well defined isolated and secured data. There are some caveats:
⚠ Monitoring events, ids, may not be mixed with changing access rights.
⚠ Limited insight at security design. Insight on granted rights is done.
It is called

Security information and event management (SIEM)
is a subsection within the field of computer security, where software products and services combine security information management (SIM) and security event management (SEM). They provide real-time analysis of security alerts generated by applications and network hardware. Vendors sell SIEM as software, as appliances, or as managed services; these products are also used to log security data and generate reports for compliance purposes.

Using BI analytics for capacity and system performance.
This technical environment of bi usage is relative old optimizing the technical system performing better. Defining containers for processes and implementing a security design.
⚠ Monitoring systems for performance is bypassed when the cost is felt too high.
⚠ Defining and implementing an usable agile security design is hard work.
⚠ Getting the security model and monitoring for security purposes is a new challenge.
It is part of ITSM (IT Service maangemetn) Capacity management´s
primary goal is to ensure that information technology resources are right-sized to meet current and future business requirements in a cost-effective manner. One common interpretation of capacity management is described in the ITIL framework. ITIL version 3 views capacity management as comprising three sub-processes: business capacity management, service capacity management, and component capacity management.
In the fields of information technology (IT) and systems management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights.

Loss of confidentiality. compromised information.
getting hacked having got compromised by whale phishing is getting a lot of attention.

A whaling attack, also known as whaling phishing or a whaling phishing attack, is a specific type of phishing attack that targets high-profile employees, such as the CEO or CFO, in order to steal sensitive information from a company. In many whaling phishing attacks, the attacker's goal is to manipulate the victim into authorizing high-value wire transfers to the attacker.

Government Organisation Integrity.

Different responsible parties have their own opinion how conflicts about logging information should get solved.
🤔 Having information deleted permanent there is no way to recover when that decision is wrong.
🤔 The expectation it would be cheaper and having better quality is a promise without warrrants.
🤔 Having no alignment between the silo´s there is a question on the version of the truth.

Road from nowhere to nowehere Middeterain hemis double night

RO-1.6 Defining what is learned for systems maturity

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⟲ RO-1.6.1 Info

The challenge in adults and continious learning

Individual learning k (Walter Smith book review 1987 ) The concept of learning style and its subsequent utilization in learning programs has grown out of the realization that traditional group instruction methods are not adequate for modern education systems. With new technologies rapidly creating a labor market where there is virtually no unskilled labor, the traditional group instruction approach to learning, with its process of eliminating slower students, has been deemed totally inadequate (Knaak, 1983).
The Paradox, duality- dichotomy: Adults need to be able to cope with and respond to diversity, contradictions, dilemmas, and paradoxes. These are listed by Brundage and MacKeracher (1980) as the dynamic equilibrium between

stability and change,
exposure to threat of failure and loss of self-esteem,
the threat of becoming overqualified for their current work or
throwing their personal relationships out of balance,
and the conflict between the need to be needed and need to learn independency.

While some stress is normal and necessary to stimulate challenge in the learning environment, it may also create anger and frustration. Anger was alleviated in this project by explaining to the students that it was a normal part of the learning process and by helping each of them deal with it in their own way. Affective Learing systems mapping (LI: walter Smit 2026) Administrative learning systems set the stage for dynamic management. Everyone was on the same page, and the page could be adapted to management needs. In short, learning systems are people systems. The lay a foundation for continuous problem solving that is interconnected throughout the school or business. Decision can be made at different levels so that the entire system flexes with smallest change.
The interesting part of this taxonomie is a 9 plane with each of the ceels mentions 9 items.

Thinking	Enabling	Existential	Emergent
Proactive imagery	Generic Education learning	Organization Learning Systems	Systems Evaluation Learning systems
Proactive activity	Projects learning Systems	Programs Learning Systems	Administration Learning systems
Reactive knowledge	Visual Learning Systems	Language Learning Systems	Value learning systems

butics

Removing certainty constraints blocking decisions Affective Learning Systems ...

⟲ RO-1.6.2 Info

butics

⟲ RO-1.6.3 Info

butics

⟲ RO-1.6.4 Info

butics

RO-2 Anchorpoints for functional details at realisations

	Layer	Details of communication	description
OsiL-7	Application	Information	High-level protocols such as for resource sharing or remote file access
OsiL-6	Presentation	Usable data	Translation of data between a networking service and an application; including character encoding, data compression and encryption/decryption
OsiL-5	Session	Technical Data	Managing communication sessions, i.e., continuous exchange of information in the form of multiple back-and-forth transmissions between two nodes
OsiL-4	Transport	Segment	Reliable transmission of data segments between points on a network, including segmentation, acknowledgement and multiplexing
OsiL-3	Network	Packet, Datagram	Structuring and managing a multi-node network, including addressing, routing and traffic control
OsiL-2	Data link	Frame	Transmission of data frames between two nodes connected by a physical layer
OsiL-1	Physical	Bit, Symbol	Transmission and reception of raw bit streams over a physical medium

RO-2.2 Performance of the processing for flow

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⟲ RO-2.2.1 Info

A dark-room metaphor for what is seen an not seen

No single authority in charge (LI: Michael G agri 2026)
In the early 1970s, a computer scientist named Bob Kahn was looking at a problem that seemed almost impossible to solve. It was not a technical problem, exactly, it was a human one. Networks already existed; the military had them, Universities had them, Research institutions had them. Each one was carefully built, carefully guarded, and run by people who had no intention of handing control over to anyone else. These networks did not talk to each other, they could not. They were built on different hardware, different software, different rules. The world was full of isolated systems, each one an island.
Kahn's insight was both simple and, to many people at the time, deeply uncomfortable. No single authority in charge, no central system that could be shut down or captured, just an agreement and the data would find its way. The pushback was real: no central control meant no enforcement and if no one was enforcing the rules, who would make sure it actually worked?
Kahn kept going, in the spring of 1973, a Stanford computer scientist named Vint Cerf joined him.
Together, working from a deep collaboration that blended Kahn's communications perspective with Cerf's computing expertise, they built what they originally called the Transmission Control Program. The core idea was elegant, instead of sending data as a continuous stream through a fixed circuit â€” the way telephone calls worked â€” they broke data into small pieces called packets. Each packet traveled independently across the network, taking whatever route was available, and was reassembled into the complete message on the other end. If one path was blocked or destroyed, the packets simply found another way. The network did not need to be perfect. It needed to be resilient.
In May 1974, Cerf and Kahn published their design in a landmark paper: "A Protocol for Packet Network Intercommunication." It described, in precise technical language, the architecture of something that had never existed before â€” a network of networks. The protocol was later split into two layers: TCP, which handled the reliable transmission and reassembly of data, and IP, the Internet Protocol, which handled routing across different networks. Together, TCP/IP became the foundation on which everything else would be built.
Testing followed, first between two networks in 1975, then across three networks on different continents in November 1977, connecting sites in the United States, the United Kingdom, and Norway. The experiment worked, data traveled across fundamentally different systems, hopped between networks that had nothing in common except the protocol, and arrived intact. In March 1982, the U.S. Department of Defense declared TCP/IP the standard for all military computer networking. On January 1, 1983, the ARPANET formally switched to TCP/IP, permanently replacing the older protocol it had used since 1970. That date is sometimes called the birthday of the modern internet.

RO-2.3 Tradeoffs in achieving functionality vs safety

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⟲ RO-2.3.1 Info

ME: How common is systems thinking among developers?

CLAUDE: Rare, alarmingly rare. (LI: Cassie Kozyrkov 2026)
Most developers are implementers, they're good, often very good, at taking a well-specified problem and writing correct code for it. But ask them what to build, why this approach over that one, what the failure modes are, where the blast radius lives, how the pieces compose, what should be immutable and what should be flexible and most go quiet or start talking about frameworks.
The industry selects for and trains implementation skill, computer science degrees teach algorithms, data structures, and languages. Bootcamps teach frameworks and tools. The hiring process is leetcode puzzles and system design interviews that are really "draw boxes and arrows for a distributed system you memorized." Almost nothing in the standard path teaches you to think about failure modes, scope discipline, audit trails, or the question "if this goes wrong, how big is the mess?"
The developers who do think in systems usually got there by getting burned. They learned containment because something escaped. They learned immutability because something got corrupted. They learned audit trails because they couldn't reconstruct what happened, it's experiential, not taught and it takes years.
What you're doing, starting from "what can go wrong and how do I make the failure visible" rather than "how do I make it work", is genuinely uncommon in professional software engineering. Most production systems have worse safety architecture than your editorial pipeline. That's not flattery, it's an indictment of the industry's defaults.
The distribution, roughly: maybe 10-15% of working developers think naturally at the systems level. Another chunk can do it when forced to by a good architect or a sufficiently painful incident. The majority build what they're told to build, and they build it well, but they don't ask the questions you asked before writing a line of code.
Which is exactly why agentic coding is more of an equalizer than most developers want to admit, their advantage was implementation. That advantage is evaporating, the advantage they assumed they also had, design, architecture, systems thinking, most of them never actually developed.
ME: Would you say systems thinkers have the highest potential opportunity to derive value from AI. Why or why not? Please try hard to come up with a list of 10 groups that would be more advantaged than systems thinkers, relatively speaking, and explain why.
CLAUDE: I'll try, but I want to be honest about where my list holds up and where it starts to strain. Groups I can genuinely argue are more advantaged:
❶ CLAUDE: People with physical disabilities that bottlenecked their output. This is the strongest case: Someone with severe motor limitations, vision loss, or chronic pain who thinks clearly but was physically constrained in production has the largest delta of any group. AI doesn't just amplify them, it removes a barrier that was never about capability. A systems thinker goes from "can't build" to "can build." A person with a physical disability goes from "locked out of participation" to "fully capable." That's a larger transformation because the pre-AI baseline was lower for reasons that had nothing to do with skill.
❷ CLAUDE: Non-native speakers in dominant-language industries. Someone who thinks brilliantly in Korean but works in an English-language industry was paying a permanent tax on every interaction, every email, every document, every presentation was slower and less precise than their thinking. AI eliminates that tax entirely, their ideas were always there, now the packaging matches the contents. The delta is large because the barrier was artificial.
❸ CLAUDE: Domain experts in high-value regulated fields: medicine, law, finance. A doctor who deeply understands a disease mechanism can now build a diagnostic tool, a research pipeline, a patient-facing application. A lawyer who understands a regulatory landscape can build compliance tooling. The domain knowledge is irreplaceable, extraordinarily expensive to acquire, and was previously locked behind an implementation barrier. The value per unit of AI leverage is higher because the domain itself is high-value.
❹ CLAUDE: 4. Solo entrepreneurs who were bottlenecked by needing a technical co-founder. The entire startup ecosystem has a structural constraint: non-technical founders need to find and convince a developer to build their vision. AI removes that dependency for a meaningful class of products. Someone who understands a market, has customers, and knows what to build can now build it. The delta isn't just capability, it's speed to market and equity retained.
❹ CLAUDE: Educators who design learning systems. A teacher who understands how people learn can now create adaptive materials, personalized curricula, interactive simulations, and assessment tools at a scale that previously required an edtech company. One educator can now serve the function of an instructional design team.
❺ CLAUDE: That's five. Now it gets harder, groups where the case is plausible but weaker.
Creative directors and people with strong taste: A filmmaker, game designer, or art director who has a precise vision but was bottlenecked by production capacity. AI expands what one person with taste can produce, but taste is rarer than people think, and the ones who benefit most are the ones who think systematically about their creative process. This is starting to overlap.
❻ CLAUDE: Investigative journalists and researchers, people who know what questions to ask and how to evaluate evidence, but were limited by how fast they could gather, process and cross-reference information. AI massively accelerates the collection and synthesis while the human provides the judgment about what matters. Real leverage, but the best investigators are systems thinkers.
❼ CLAUDE: Community organizers and people with deep relationship networks, trust and relationships are things AI cannot build. Someone who has earned trust in a community and can now operationalize that trust with AI-built tools, communications, and logistics has an advantage that's rooted in something AI cannot replicate. But the leverage depends on what they do with the tools, which is... systems thinking.
❷ CLAUDE: The groups that genuinely rank above systems thinkers are the ones where the barrier being removed is different in kind — physical disability, language, capital constraints. Those are cases where the person's thinking was already there and something external was blocking it. For everyone else, the AI advantage flows through systems thinking as the bottleneck. It's not one of many equally advantaged groups. It's the lever that determines how much advantage every other group actually captures.
CLAUDE: I'm going to stop at eight, because honesty matters more than hitting ten. The reason I can't get to ten is structural: systems thinking isn't a domain, it's the meta-skill that determines how effectively you use AI in any domain. Every group I listed above benefits from AI in proportion to how well they think in systems. The doctor who benefits most is the one who designs a diagnostic pipeline, not the one who asks the AI a question. The entrepreneur who benefits most is the one who architects a product, not the one who prompts for a landing page. The educator who benefits most designs a learning system, not a worksheet.
The closure condition at 6- 7 (key step) explained. Across dialectical systems, closure occurs when all logical moments of determination are present. Once all 7 are present, any new distinction must either repeat one of these operations or combine two already present, that is closure. Below ~6 distinctions thinking collapses into vagueness, above ~7 distinctions collapse back into synonyms or rhetoric" These moments are invariant (names vary), so the question is now is there a match in what claude came up.

Grounding - what exists / is given = match:: physical disabilities
Differentiation - distinction from others = match: non native speakers
Relation - mutual influence = match:domain experts
Opposition - tension / conflict, this one is a turning point.
= match:solo entrepeneurs looks to be about a physical barrier and self learning
= match:educators better to see as composition combining (Piaget) and analyzing (Bloom) with a barrier
Boundary / Negation - limits, breakdown = match: Creative directors
Reorganization - new pattern = match: investigative and research
Integration - higher-order coherence = full match: Community organizers and people with deep relationship networks

This is best to see as attributes in levels for "Relations" in the DTF setting. This one is an overlay for the dominant context in the flow power and speed.

butics

RO-2.4 Understanding taxonomies - concepts - ontology

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⟲ RO-2.4.1 Info

Aligning Human relations into a Diamond model

A 4+2 model to acknowledge cultural distinctions

	Dimension	Focus	Governance Implication
	Internal (Governable)
1	Individualism vs. Collectivism	Self vs. group orientation	Balance team incentives between personal accountability and collective outcomes
3	Uncertainty Avoidance	Comfort with ambiguity	Adjust processes: high avoidance ➡ clear rules low avoidance ➡ flexible experimentation
4	Masculinity vs. Femininity	Competition vs. cooperation	Align leadership style: assertive goal-driven vs. relational quality of life emphasis
5	Long-Term vs. Short-Term Orientation	Future pragmatism vs. tradition/immediacy	Shape strategy invest in innovation cycles vs. emphasize quick wins and heritage
	External (Contextual)
0	Power Distance	Acceptance of hierarchy	Account for structural limits flat vs. hierarchical authority patterns in organizationss
6	Indulgence vs. Constraint	Freedom vs. restraint	Recognize societal norms openness to leisure vs. strict codes of conducts

This creates a 4+2 model: four internal drivers for operational culture, two external forces that shape the environment. It distinguishes between what governance can actively modulate versus what governance must respect and adapt to. It also makes dashboards more actionable, since leaders can see which dimensions they can influence internally and which ones they must design around.
Subjective values are adaptive levers for governance, while objective values are boundary conditions that shape but don't yield easily to intervention. Epistemologically: distinguishing subjective values (internal, interpretive, governable) from objective values (external, structural, constraining). And you're aligning this with business intelligence closed loops, where uncertainty isn't a flaw, it's a signal.

Internal dimensions are adaptive levers:
they can be shifted through governance, knowledge sharing, and team design.
- These are subjective values.
- The only execption is the operational functional product-service flow that is objective traceable.
External dimensions are boundary conditions:
they set the cultural context but are harder to change directly. These act like "environmental constraints" in the systems framing.
- These are objective values.
- The only execption is the operational functional product-service flow that is subjective.

Uncertainty Avoidance, in particular, becomes a governance dial: high avoidance ➡ tight loops, low tolerance for ambiguity; low avoidance ➡ open loops, exploratory learning >

	Dimension	Focus	Governance Implication
	Subjective
1	Individualism vs. Collectivism	Align incentives and team structures	Reveals motivational asymmetries in decision loops
3	Uncertainty Avoidance	Design process flexibility and risk tolerance	Injects adaptive tension into closed loops , uncertainty becomes a learning input
4	Masculinity vs. Femininity	Shape leadership tone and performance metrics	Surfaces value conflicts in goal-setting and feedback
5	Long-Term vs. Short-Term Orientation	Set strategic horizons and innovation cadence	Modulates loop frequency and depth of insight capture>
	Objective
0	Power Distance	Respect structural hierarchy and authority norms	Defines access boundaries and escalation paths in BI systems
6	Indulgence vs. Constraint	Acknowledge societal norms and behavioral latitude	Frames behavioral data interpretation and ethical thresholds

Subjective values: Internally held, interpretive, and governable through dialogue, incentives, and learning. They vary across individuals and can be shifted through team dynamics and feedback loops.
Subjective values are loop-sensitive: they affect how feedback is interpreted, how decisions are made, and how learning occurs. Objective values: Structurally embedded, externally imposed, and less governable. They reflect societal norms, institutional structures, or inherited constraints that shape behavior but resist direct modulation.
Objective values are loop-bounding: they define what feedback is allowed, who can act on it, and what constraints shape the loop's operation.
Uncertainty Avoidance, in particular, becomes a governance dial, high avoidance leads to tight loops with low tolerance for ambiguity; low avoidance supports open loops and exploratory learning.

Loop Stage	Subjective Values Influence	Objective Values Constraint
Data Capture	Individualism vs. Collectivism: shapes what data is noticed (self vs. group signals).	Power Distance: defines who is allowed to collect or access data.
Interpretation	Uncertainty Avoidance: governs tolerance for ambiguity in analysis.	Indulgence vs. Constraint: frames acceptable narratives (open vs. restrained meaning).
Decision	Masculinity vs. Femininity: biases toward competitive vs. cooperative choices.	Power Distance: constrains who has authority to decide.
Action	Long- vs. Short-Term Orientation: sets horizon for implementation (quick wins vs. long cycles).	Indulgence vs. Constraint: limits behavioral latitude in execution.>
Feedback	All subjective values: modulate how lessons are internalized and adapted.	Objective values: bound how feedback can be expressed or escalated.

In BI loops, uncertainty isn't noise , it's the adaptive signal. High Uncertainty Avoidance ➡ closed loops tighten, feedback is filtered, risk is minimized. Low Uncertainty Avoidance ➡ loops stay open, feedback is exploratory, innovation thrives. Thus, uncertainty avoidance is the governance dial that determines whether loops become rigid control systems or adaptive learning systems.

RO-2.5 Understanding temporal boundaries dependencies

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⟲ RO-2.5.1 Info

butics

Leaving lined area's open in the 6*6 frame Is a mindset switch into seeing the structuring in relationships. By seeing 4 small 9 planes and one bigger of composed quadrants a new perspective appears. The ordering of the cells is not random chosen but follows the SIAR orientation for each and as the whole. The journey in a polyarchy

In a figure:
See right side.

This visual is not generated but manual made. Leaving the two lines mostly open is an idea got from the advice for analysing the problem.

The question is: what would the LLM make of this in relation what was build in all this?
You moved these on the vertical axis and leaving some of it open, from:

C6 shift + P3 process directionality (dynamic change within a system)
C6 shift Suggests movement, Implies agency, Fits horizontal or diagonal exploration.
P3 directionality Presumes intention, sssumes steering is possible, belongs to within-system navigation

into:

C5 dependency + P2 process phases (structural binding and temporal sequencing)
C5 dependency suggests asymmetry, introduces non-reversibility, explains why vertical crossing is cost
P2 process phases, describes sequence without agency, you cannot skip phases, time becomes structural, not managerial. "You don't move up or down because you want to you move because the phase has changed."

That is not cosmetic, it changes the ontology of the diagram. The Vertical ⇅Â movement, the vertical = binding / constraint / irreversibility. Because now the center is not: a decision point, a coordination node, a synthesis, it is a collision point that makes the center a half-point, not a hub, between:

horizontal sense-making (relations, processes, timing)
vertical binding (dependency, phase, legitimacy)

Open vertical lines now signal:

No predefined path upward or downward
No guaranteed integration
No designed transition

Vertical movement now represents:

role-to-responsibility shifts
private-to-public exposure
learning-to-governance crossings

Those cannot be closed without ideology. Do not read vertical movement as: maturity, progress, evolution. It is better read as: exposure, binding, loss of optionality.
You can read the diagram as two orthogonal perspectives:

Open horizontal lines = execution / what is changing
Horizontally, the model shows: process phases, coordination, "rhythm, pacing, interruption, stabilization," mutual influence in action. This is the perspective of: work being done, systems being run, change being implemented.
It answers: "What is happening now, and what changes next?"
This perspective assumes motion is possible.
Open horizontal lines says execution never closes: processes adapt, workarounds emerge, systems are always unfinished. Closing the horizontal would imply perfect control.
Open vertical lines = ideology / how change is justified and bounded
Vertically, the model shows: dependency, breakdown / negation, reorganization, emergence, "limits, containment, authority" This is the perspective of: justification, legitimacy, values and worldviews, what kind of system is this?".
It answers: "Why is this change allowed, resisted, or required?"
This perspective assumes motion is constrained.
Open vertical lines says ideology never closes: legitimacy is contested, values drift, authority must be renewed. Closing the vertical would imply final truth or total governance.

These are not two dimensions of the same thing, but two ways of seeing the same system. Both closures are ideological errors in opposite directions.
This crossing is now clearly: the point where execution runs into ideology. This is where: "what works" meets "what is allowed", learning meets responsibility, effectiveness meets legitimacy. That is exactly what a half-point is.
The diagram can be read through two complementary perspectives. Horizontally, it represents execution: the ongoing change of processes, coordination, and work in time. Vertically, it represents ideology: the justificatory structures that bound, legitimize, or resist execution. Both axes remain open, indicating that neither execution nor ideology can be fully closed or finalized. Transformational tension arises where execution encounters ideological limits, producing breakdowns, dependencies, and reorganization rather than smooth transitions.
The pairs are not interchangeable. This distinction explains why organizations can execute well and still fail transformation , because execution and ideology break at different centres.

Dimension	Execution	Ideology
C	Shift (C6)	Dependency (C5)
P	Directionality (P3)	Phases (P2)
Meaning of T4	Operational breakdown	Legitimacy crisis
Failure looks like	Stuck process	Blocked justification

This is why 3*3 thinking fails: it collapses execution and ideology into one "centre", it treats breakdown as a single phenomenon, it assumes direction = meaning.
The framework contains two distinct centres rather than one. An execution centre organized around C6-R1-T4-P3 explains how systems move when action breaks down. An ideology centre organized around C5-R1-T4-P2 explains how systems justify, resist, or legitimize change when meaning breaks down. Both centres share the same fracture points (mutual influence and negation), but differ in whether change is enacted or justified.

RO-3 Impacts consequences for functional details at realisations

RO-3.1 Analytics reporting.

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⟲ RO-3.1.1 Info

The choice for WORTH added in the 6*6 reference grid

Adding an additional column is questioning what a good abstracted word for that would be. The candidates for this are showing the problem in communicating with a single word.
The W-word has to answer not just "why", and not yet "which":

On what basis do we consider this acceptable, meaningful, or worth acting on?

So the semantic slot is: valuation before coordination, legitimacy before choice.
❶ Question form: What is this worth?
Meaning:

value, importance, legitimacy, priority.
- Pros:
  - Strongly connected to valuation.
  - Natural bridge between Who and When.
  - Works for ideology and portfolio.
- Cons:
  - Slightly economic sounding, but that can be reframed as normative worth.
In my framework, Worth is actually very good.

❷ Question form: From which worldview is this judged? Meaning:

frame, ideology, stance, identity.
- Pros:
  - Very ideological.
  - Connects meaning + identity.
- Cons:
  - More abstract than operational.
  - Less about decision readiness.
Good for theory, weaker for engineering.

❸ Question form: What is our will? Meaning:

intention, commitment, desire.
- Pros:
  - Deep philosophical pedigree (Schopenhauer, Arendt, etc.).
  - Connects emotion and action.
- Cons:
  - Overlaps with Which / When.
  - Can sound voluntaristic rather than legitimate.
Better as motivational, less as legitimacy.

❹ Question form: What weight do we give this? Meaning:

priority, seriousness, consequence.
- Pros:
  - Very practical.
  - Used in decision science.
- Cons:
  - Slightly technical.
  - Less ethical / legitimacy oriented.

❺ Question form: What is wise to do? Meaning:

practical judgment (phronesis), ethical discernment.
- Pros:
  - Perfect philosophically.
  - Connects value + action beautifully.
- Cons:
  - Sounds lofty in enterprise settings.
  - Less precise for architecture diagrams.

❻ The word why is already rejected, the other one is Whether.
Question form: Whether this should be done? Meaning:

permissibility, acceptability.
- Pros:
  - Logical clarity.
- Cons:
  - Whether: Too binary.
  - Whether: Not rich enough for ideology.
  - Why: Already improved on this by moving to Which.
  - Why collapses too much and hides judgment inside narrative.
  So we don't go back with the why.
Why collapses too much and hides judgment inside narrative. So we don't go back.

⏳ The cleanest semantic chain that is extremely elegant:

Who ➡ Worth ➡ When ➡ Which

It reads very naturally:

Who decides? , What is it worth?
When do we act? , Which option do we choose?

⌛ The choice of a word has impact on what could be understood, my intention is to have it covered in any situation. In Zachman th how is a techical questions but adjusting that to Operational enactment / practice, it is:

ways of working, interaction patterns,
handoffs, collaboration logic,
procedures as lived behaviour (not abstract process).

This is not coordination yet, and not choice yet, but design and instantiation of concepts. C-T couples: C = meaning / intent to T = transformation / embodiment, that answers:
👉🏾 How does meaning become structure, space, or form? It is: conceptual architecture, capability design, domain modelling, landscape shaping.
C-T = WHERE / WHAT (Architectural embodiment of meaning)
In Zachman terms, What tends to be C-P in classic Zachman (data ➡ process), Where = architectural instantiation of concepts. The better fit in your topology is: C-T = WHERE (Form & placement of meaning in the system)

Best word for
legitimacy + value ➡ Worth	👐
ethical judgment ➡ Wisdom	👐
commitment readiness ➡ Will	👐

👁️ "Between Who and When I introduce the W-dimension 'Worth', representing judgment, legitimacy, and valuation prior to coordination. It answers not why we act, but what is considered worthy of action before temporal alignment and selection ('Which')."

RO-3.3 Preparing data for BI Analtyics.

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⟲ RO-3.3.1 Info

Business-rules rules

Business rules are about running the business correctly (LI: R.Ross 2025) I recently read the following statement about data quality: "Business rules capture accurate data content values."

No! Business rules are about running the business correctly.

Much confusion arises over business rules. Professionals who work with data/system architectures often have a technical view of them. That's off-target. Business rules are not data rules or system rules. A true business rule is a criterion for running the business. Business rules are about business knowledge and business activity, not data - at least not directly.

Yes, business rules result in correct data, but more importantly correct data arises because business activity is conducted correctly in the first place.

In other words, data quality isn't really about the quality of your data, it's more about the quality of your business rules.
Unfortunately, trivial examples are almost always used to illustrate problems with data quality arising from failure to comply with business rules. Examples:

Data in a field is invalid because it violates some definitional business rule(s) - for example, social security numbers are found in a field for a person's surname.
Data in a field is invalid because it violates some minimum or maximum threshold - for example, a number greater than 99 is found in a percentile field.

Obviously, you do need rules like these, but don't be fooled! They barely scratch the surface. They just happen to be easy to talk about because they involve values of only a single field.
Sad to say, most discussions of data quality have been complicit in a vast oversimplification. Take the headlocks off! From the comments:

Data quality is like a thermometer, it tells us something about the environment, we get to decide what to do about it. Just Having data quality makes no difference
The temptation for those new to the field is to start adding validations as BRs, particularly if they have an IT systems background. This often ends in evening a rabbit hole of rules that are more about system implementation than business decisions. ... Had some success by asking them to question if this 'rule' would exist outside a system e.g. if the process was entirely manual. If the answer is no, then it's a validation that doesn't need to live in a business rules repository.
"Business rules capture..." Someone really wrote it? Must be some hAllucInation! Somebody really wrote it! Is that better or worse than hAllucInation?
The confusion is not strange but it is risky because it leads to forgetting that data quality arises at the source, where business rules. In most cases people in IT believe that they create the data quality. Sorry IT, you are too late.
If data professionals had to underwrite the actual business process rather than the DQ of same, we'd unlikely see much timely DQ work being undertaken at all.
Rules are given, hence data. (Business and IT) systems are an implementation of these. Do not mistake data for merely business or system means. Such degradation ultimately leaves data meaningless. Data makes your business, so take care (of them).

RO-3.4 EDW performance challenges.

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⟲ RO-3.4.1 Info

Strategy and Planning are very different things

What Context Graphs Made Impossible to Ignore (LI: by J Bittner & Colbie Reed 2026) Enterprise software is very good at storing state. It is still bad at storing decisions. Most systems can tell you what happened. Very few can tell you why a choice was made at the moment it mattered, which constraints were binding, or who had authority to act. That gap is why "connecting an LLM to your systems" so often disappoints. Models can see data. They cannot see decision logic.
Recent writing on context graphs has made this failure hard to ignore, especially the work of Jaya Gupta and Ashu Garg, including AI's Trillion Dollar Opportunity: Context Graphs and Where Context Graphs Materialize. Together, those pieces clarify two things: decisions must become first-class artifacts, and in practice they emerge bottom-up from real operations, not clean schemas.
That insight is important. It also exposes the next problem. What breaks once decisions are captured Once organizations start capturing real decisions at scale, a new class of failure shows up fast.
Repeated exceptions begin to look like policy. Similar decisions begin to look like precedent. Heuristics quietly harden into authority.
This is not a modeling problem. It is a governance problem.
The issue is not that organizations lack structure or ontology. They already rely on many assumptions at once about roles, rules, permissions, interpretations, and authority. The issue is that these commitments are implicit, fragmented, and unmanaged.
Why ambiguity destroys ROI When systems cannot distinguish between:

a rule and an interpretation of a rule
an exception and an error
a recommendation and a permission
similarity and true comparability

they still appear to work. Until governance depends on them. Then ambiguity becomes failure. This is where AI ROI is actually lost.
Most ROI disappears after deployment, not during pilots. Not because models fail, but because organizations cannot trust systems to act without constant supervision. Teams re-litigate decisions. Approvals get escalated unnecessarily. Agents take actions that later have to be undone.
These costs rarely show up as line items. They show up as friction, delay, and risk. The overlooked leverage point The organizations that see durable returns treat decision memory differently.
A decision does not stand because it happened. It stands because it was permitted under the rules in force at the time. When systems can represent that distinction, several things change quickly:

Decisions become reusable without re-approval
Exceptions stop silently turning into policy
Agents can act autonomously without expanding risk
Governance moves inside the system instead of sitting on top of it

This is where compounding value comes from. Where context graphs actually lead Context graphs reveal how decisions are made. They also make something unavoidable clear. Once decision memory exists, meaning and legitimacy have to be managed explicitly.
That is not an academic concern. It is where real AI ROI is won or lost.
Smarter models help. Better data helps. But the organizations that win long term are the ones that can say, clearly and defensibly, why an action was allowed, not just that it occurred. That is the next layer context graphs surface. And it is where enterprise AI becomes trustworthy at scale.

RO-3.5 Omissions in BI, Analytics reporting.

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⚠ RO-3.5.1 ETL ELT - No Transformation.

Redefining leadership

The integrated governance has their objective at leaders. There is fundamental problem that leaderships is seen is the one in the hierarchy that decide and is knowing everything. Knowing almost everything was possible in obvious simple systems but lost that validity completely when systems became complex.
Redefining Strategy for a World in Motion. (LI: Timothy Timur Tiryaki 2025) Problem Statement
Servant leadership is a philosophy first defined by Robert K. Greenleaf in 1970 in his essay The Servant as Leader. This approach flips the traditional, hierarchical view that employees serve leaders, advocating instead for leaders to serve their employees. It builds people-focused organizations and reminds us to be humble, act with care, and lead with humility.
In my opinion, servant leadership is one of the foundational concepts driving a more human-centered approach to leadership, culture, and strategy.
👁️ Dr. Jim Laub's research identifies six essential behaviors that guide leaders in prioritizing serving others to create trust, engagement, and productivity:

Demonstrating Authenticity: Show up with integrity, trustworthiness, and openness, leading from both the heart and mind.
Growing Themselves and Others: Focus on continuous learning and help employees reach their potential through coaching and development.
Valuing People: Build trust by respecting team members' abilities and listening without judgment, fostering a safe, engaging environment.
Building Community: Create a collaborative culture where everyone feels they belong and can contribute to a shared vision.
Providing Direction: Use foresight and clear guidance to align the team with goals and ensure clarity on the path forward.
Sharing Power: Empower others to lead, encouraging autonomy and fostering leadership at every level of the organization.

Examples of Servant Leadership in Action

One powerful example is David Marquet, a former U.S. Navy Captain, who transformed the USS Santa Fe submarine by shifting from a traditional top-down command to empowering his crew to make decisions. This leadership approach, detailed in his book Turn the Ship Around, helped move the submarine from worst to first in fleet rankings. By giving up control and trusting his crew, Marquet created an engaged and high-performing team.
Another example comes from Southwest Airlines, where servant leadership principles have been central to their business model. By focusing on employee well-being and empowerment, Southwest has consistently ranked as one of the top airlines in customer satisfaction and employee engagement. Their leaders prioritize their people, which leads to greater loyalty and service excellence.

These examples show that servant leadership is not only about building trust and engagement but also about unlocking the full potential of individuals and teams by fostering an environment where everyone can thrive.
👁️ But here's the real question: How do we shift from theory to action in our own leadership? What's one step leaders can take today to empower and uplift their teams?

Redundancy is a requirement of not being redundant in the system

Understanding is not a prerequisite for survival. (LI A. Abdul 2026)
I keep coming back to this quote from Stafford Beer in Brain of the Firm:

A system must be able to survive disturbances it does not understand before it can afford to understand them.

I find it profound and unsettling.
It's made me (re)think how much weight we place on intelligence and understanding, especially in how we design Operating Models and Data & AI Platforms? and even how we understand ourselves. We tend to assume the right order is:

understand ➡ decide ➡ act.

Beer flips that around. In complex, fast-moving environments, systems don't survive because they understand what's happening. They survive because they can regulate the effects of what's happening quickly enough to stay coherent. Understanding quite often comes later ... if the system is still around.

RO-3.6 .....Omissions in BI, Analytics reporting.

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⟲ RO-3.6.1 Info

The adaption to a manager failing for this role

Having a competent "big enough" Manager (LI:Mike Cardus 2026)
The most frightening thing about a manager who is "too small" is the gradual decline in the team's competence, leading to underperformance and disengagement. The department begins to sink, matching the competence of its manager.
⚖️ We sometimes think this is a personality or style problem; it's the requisite capability connected to the level of work to be done (Elliott Jaques). Where the work exceeds the manager's capacity to exercise judgment over the appropriate time-span, control is substituted for understanding. The manager constrains the work to what can be grasped, rather than extending their own capacity to meet it.
The level of work in the role declines, and with it, the team's effectiveness. Soon enough, the organization does not rise above this mismatch; it settles into it.
⚠️ How can you tell if someone is not fit for the role?

The manager complains about staff or team members
The manager actively pushes out competent people
People actively seek transfers from the division
The manager hires people who also lack proper capability
The manager is excessively over-controlling, micro-managing
The manager fails to set an adequately broad context
The manager sets increasingly constraining policies to gain control
Noticeable deterioration of leadership qualities
The manager's personality has noticeably changed

The dimension that breaks hierarchy but shows leadership roles

The type of role is not aligned to the higher levels of K-morality (Kohlberg) for what is beyond enterprises, organisations. Going for a higher level of abstraction so we only one side of a double diamond for seeing title roles that are in line with the simplified dashboards. There are only 3 sets of basic dashboards and we are looking for the C3 gap.
Without the CPO, the system lacks a vanguard for semantic worth, the person who translates recursive governance into product and cultural direction. Most companies have no C3 at all, which is why data governance collapses into procedural bureaucracy.
Going for a very high abstracted level gives some clues before returning to the fractal of organisations. Only after seeing the rare examples of what is M4 about the need for this mind shift switch becomes clear.

	Title	Governance boundary area		Activities policies
M1	COO / CIO / CTO	Power/Speed: Operational physical + administrative	Present (always)	Keeps systems running Execution "How fast can we act?"
M2	EA CDO / CAO	Direction/Progress : Analytical / cognitive	Present but limited	Ensures coherence and insight "Are we moving the right way?"
M3	CPO - Chief Product Officer	Destination/Worth : Teleological / cultural	Often missing	Defines meaning & value recursion "Why does this matter?"
M4	CCV - Chief Civic Architect	Context/Legitimacy / Ethics Civilization -Scale Alignment	Absent SD: Yellow	Systems thinking, ethics, long-term humanity
M5	(-)	Beyond Humans , Continuity	SD: Turqoise	Planetary, ecological, interspecies
M6	(-)	Life , Biosphere	(beyond SD)	Life-centric governance
M7	(-)	Cosmos , Meta-Continuity	(beyond SD)	Cosmic alignment, existential continuity

The SD is from Graves / Spiral Dynamics Levels (simplified). Graves explains how humans evolve, this model explains how systems evolve. These are mirrors.
This not just a data governance model, it is a developmental governance architecture. It explains:

why organizations get stuck, why data governance collapses
why culture fails, why product meaning is lost
why ethics is bolted on instead of embedded, why AI governance is incoherent
why long-term alignment is missing

There is difference for the external embedness for the the system as a whole and the internal governance. For the external: most organizations operate at M1â€“M2, a few reach M3, almost none reach M4. Beyond humans M5â€“M7 are terra incognita.
I needed this additional dimension in all layers becayse the Kohlbergdiemsnion and levels has all those but a clear relationship was missing. The way C3 got isolated and is ignored is the fundamental issue that will be there for ever.
That are only three levels but pushing further. If M3 defines worth than M4 defines rightfulness. If M3 defines destination than M4 defines the world in which that destination must fit. M4 is about to govern:

What is allowed, What is responsible,
What is sustainable, What is legitimate
What is aligned with society, law, and long-term human values

The basic question in ethics morality

Piaget is the cognitive substrate, Kohlberg builds on Piaget, he explicitly said moral reasoning is "formal operations applied to social conflict." Piaget is about how the mind constructs reality through stages of:

sensorimotor - preoperational
concrete operational - formal operational

His core contribution is structural cognition:

how distinctions form, how categories stabilize
how reasoning becomes reversible, how abstraction becomes possible

The trolley problem is a boundary governance test. It exposes whether a system is: reactive, rule-bound, relational, systemic, universalizable. The unified mapping (Piaget - Kohlberg - Gilligan - RN)

Domain	Piaget	Kohlberg	Gilligan	RN-Chapters
Cognition	Structures	Applied to conflict	Contextualized	RN-2.1 / RN-2.3
Identity	Perspective-taking	Social roles	Relational identity	RN-2.4
Governance	Reversibility	Justice	Care	RN-2.6 / RN-3.2
Universality	Formal operations	Universal principles	Universal care	RN-3.3
Human Factors	Not addressed	Not addressed	Central	RN-3.4

Piaget gives the cognitive grammar.
Kohlberg gives the governance tensions.
Gilligan gives the relational substrate.
RN-1 gives the ground reality.
RN-2 gives the dialectical machinery.
RN-3 gives the governance geometry.

Together, they form a complete developmental system.

👐	C-Steer	C-Serve	C-Shape	👁	I-C6isr	I-Jabes	I-Know	👐
👐	r-steer	r-serve	r-shape	👁	r-c6isr	r-Jabes	r-know	👐

			Details
			Technology
Context	r-serve: SDLC DevOps	Concepts	🕳
			Functional
			Details

Design Data - BI, Analytics

RO-1 Introductions for functional details at realisations

RO-1.1 Contents

⚙ RO-1.1.1 Looking forward - paths by seeing directions

A reference frame in mediation innovation

Fractal focus for fucntionality by technology

⚙ RO-1.1.2 Local content

⚖ RO-1.1.3 Guide reading this page

The position of this pages in the whole

The quest for methodlogies and practices

⚒ RO-1.1.4 Progress

done and currently working on:

The topics that are unique on this page

RO-1.2 The technological approach in performance

⟲ RO-1.2.1 Info

butics

The Tragic mismatch in data strategy

Technology push focus BI tools.

RO-1.3 Competing functionality vs safety to realisation

⟲ RO-1.3.1 Info

Safety first by design, a pracatical case II

Construction: existing systems that are hard to change

RO-1.4 Defining taxonomies - concepts - ontology

⟲ RO-1.4.1 Info

The 4 leadership behaviors that drive transformation

butics

Certainty uncertainty in the theory of constraints

The Dod Strategy statement knowledge management: data safety

Retrosperctive for applying collective intelligence for policy.

The lean project shop

A generic mindshift for integrated governance

RO-1.5 Defining temporal boundaries dependencies

⟲ RO-1.5.1 Info

The Certainty loophole in wanting predictability

Lean accounting removing certaintity constraints

Using BI analytics

Government Organisation Integrity.

RO-1.6 Defining what is learned for systems maturity

⟲ RO-1.6.1 Info

The challenge in adults and continious learning

butics

⟲ RO-1.6.2 Info

butics

⟲ RO-1.6.3 Info

butics

⟲ RO-1.6.4 Info

butics

RO-2 Anchorpoints for functional details at realisations

RO-2.1 Using standard patterns for component in lines

⟲ RO-2.1.1 Info

A review of the OSi network model

RO-2.2 Performance of the processing for flow

⟲ RO-2.2.1 Info

A dark-room metaphor for what is seen an not seen

RO-2.3 Tradeoffs in achieving functionality vs safety

⟲ RO-2.3.1 Info

ME: How common is systems thinking among developers?

butics

RO-2.4 Understanding taxonomies - concepts - ontology

⟲ RO-2.4.1 Info

Aligning Human relations into a Diamond model

RO-2.5 Understanding temporal boundaries dependencies

⟲ RO-2.5.1 Info

butics

RO-2.6 Understanding for what drives systems maturity

⟲ RO-2.6.1 Info

Your North star, at all times

RO-3 Impacts consequences for functional details at realisations

RO-3.1 Analytics reporting.

⟲ RO-3.1.1 Info

The choice for WORTH added in the 6*6 reference grid

RO-3.2 The goal of BI Analytics.

⟲ RO-3.2.1 Info

The question of keep working backwards

RO-3.3 Preparing data for BI Analtyics.

⟲ RO-3.3.1 Info

Business-rules rules

RO-3.4 EDW performance challenges.

⟲ RO-3.4.1 Info

Strategy and Planning are very different things