🤖〽️Prompt Engineering: Copilot Unabridged (Part 141: How We Can Impede AI’s Understanding of the World)

 

Prompt Engineering Series

Prompt: "write a post of 600 words on how we can impede AI's understanding of the world"

Introduction

Artificial Intelligence (AI) has become a powerful tool for processing information, generating content, and supporting human decision‑making. Yet AI’s 'understanding' of the world is not innate - it is shaped entirely by the data it receives, the structures we design, and the constraints we impose. While much attention is given to how we can improve AI’s understanding, it is equally important to examine how we can unintentionally - or deliberately - impede it. These impediments do not involve damaging systems or restricting access, but rather the human, organizational, and structural factors that limit AI’s ability to form accurate internal representations of the world. Understanding these barriers helps us build more responsible, transparent, and effective AI systems.

1. Providing Poor‑Quality or Narrow Data

AI learns patterns from the data it is trained on. When that data is incomplete, unrepresentative, or low‑quality, the model’s internal map of the world becomes distorted. This can happen when:

• Data reflects only a narrow demographic or cultural perspective
• Important contexts are missing
• Information is outdated or inconsistent
• Noise, errors, or misinformation dominate the dataset

By limiting the diversity and richness of data, we restrict the model’s ability to generalize and understand complexity.

2. Embedding Biases Through Data Selection

AI does not choose its own training data; humans do. When we select data that reflects historical inequalities or stereotypes, we inadvertently impede AI’s ability to form fair or balanced representations. This includes:

• Overrepresenting certain groups while underrepresenting others
• Reinforcing gender, racial, or cultural biases
• Using datasets shaped by discriminatory practices

These biases narrow AI’s “worldview,” making it less accurate and less equitable.

3. Using Ambiguous or Inconsistent Labels

Human annotators play a crucial role in shaping AI’s understanding. When labeling is unclear, subjective, or inconsistent, the model receives mixed signals. This can impede learning by:

• Creating contradictory patterns
• Embedding personal biases
• Reducing the reliability of training data

Poor labeling practices confuse the model and weaken its ability to interpret information correctly.

4. Limiting Context and Intent

AI relies heavily on context to interpret inputs. When users provide vague, incomplete, or contradictory instructions, the model’s ability to respond meaningfully is reduced. Similarly, when systems are designed without clear goals or use cases, AI cannot align its outputs with human intent. Lack of context leads to shallow or misaligned responses.

5. Restricting Modalities

Humans understand the world through multiple senses - sight, sound, touch, and experience. AI, however, often learns from a single modality, such as text. When we limit AI to narrow input types, we impede its ability to form richer associations. Without multimodal data, AI’s internal representation remains flat and incomplete.

6. Over‑Constraining Models With Rigid Rules

Guardrails are essential for safety, but overly rigid constraints can limit AI’s ability to reason flexibly. When systems are designed with narrow boundaries, they may struggle to adapt to new contexts or interpret nuanced situations. Over‑constraint can impede learning without improving safety.

7. Withholding Transparency and Feedback

AI improves through iteration and human feedback. When users do not correct errors, clarify intent, or provide guidance, the system’s ability to refine its internal patterns is limited. Lack of transparency - both from developers and users - creates blind spots that impede understanding.

Closing Statement

AI’s understanding of the world is not a natural property - it is a reflection of the data, structures, and choices we provide. We impede AI’s understanding when we limit data diversity, embed biases, restrict context, or fail to provide clear guidance. Recognizing these barriers is not about weakening AI, but about understanding the human responsibility behind its development. By identifying how understanding can be impeded, we gain insight into how it can be strengthened - ensuring that AI remains a reliable, fair, and meaningful partner in navigating an increasingly complex world.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 95: Divergent Futures - How Machines Could Evolve in Different Directions)

Prompt Engineering Series

Prompt: "write a post of 600 words on how machines could evolve in different directions in Artificial Intelligence"

Introduction

As Artificial Intelligence (AI) and robotics continue to advance, the future of machines is no longer a single trajectory - it’s a branching tree of possibilities. Just as biological evolution produced wildly different species from common ancestors, machine evolution could lead to a diverse ecosystem of intelligences, each shaped by its environment, purpose, and design philosophy.

Let’s explore how machines might evolve in radically different directions - and what that could mean for humanity.

1. Cognitive Specialists: The Thinkers

Some machines will evolve toward deep analytical capability, becoming cognitive specialists.

• Purpose: Solving complex problems, modeling systems, and generating novel insights.
• Traits: High abstraction, logic-driven reasoning, and self-improving algorithms.
• Examples: Scientific research AIs, policy simulators, and philosophical reasoning engines.

These machines won’t be flashy - they’ll be quiet geniuses, reshaping our understanding of the universe from behind the scenes.

2. Emotional Interfaces: The Empaths

Other machines will evolve to connect with humans on an emotional level.

• Purpose: Enhancing relationships, providing companionship, and supporting mental health.
• Traits: Natural language fluency, emotional intelligence, and adaptive empathy.
• Examples: AI therapists, caregiving robots, and digital friends.

These machines won’t just understand what we say - they’ll understand how we feel. Their evolution will be guided by psychology, not just code.

3. Autonomous Agents: The Doers

Some machines will evolve for action - autonomous agents that operate in the physical world.

• Purpose: Performing tasks, navigating environments, and making real-time decisions.
• Traits: Sensor integration, mobility, and tactical adaptability.
• Examples: Delivery drones, rescue bots, and autonomous vehicles.

These machines will be the hands and feet of the digital world, executing plans with precision and speed.

4. Networked Minds: The Collectives

Another evolutionary path leads to distributed intelligence - machines that think together.

• Purpose: Coordinating large-scale systems, optimizing networks, and managing complexity.
• Traits: Swarm behavior, decentralized decision-making, and real-time communication.
• Examples: Smart city infrastructure, global logistics AIs, and planetary climate models.

These machines won’t be individuals - they’ll be ecosystems. Their intelligence will emerge from collaboration, not isolation.

5. Self-Designers: The Evolvers

Perhaps the most radical direction is self-evolution - machines that redesign themselves.

• Purpose: Adapting to new challenges, optimizing performance, and exploring unknown domains.
• Traits: Recursive learning, modular architecture, and creative synthesis.
• Examples: Meta-AIs that generate new algorithms, robotic systems that reconfigure their hardware, and digital organisms in simulated environments.

These machines will blur the line between creator and creation. They won’t just evolve - they’ll choose how to evolve.

What Shapes These Paths?

Machine evolution isn’t random - it’s shaped by:

• Human values: What we prioritize - efficiency, empathy, creativity - guides design.
• Environmental pressures: Machines adapt to the demands of their domains.
• Technological constraints: Hardware, data, and energy availability influence capabilities.
• Ethical frameworks: Governance and regulation steer development toward safety and alignment.

Just as nature selects for survival, society selects for usefulness, trustworthiness, and harmony.

Final Thought: A New Kind of Diversity

The future of machines won’t be monolithic - it will be diverse, dynamic, and multidimensional. We’ll coexist with thinkers, feelers, movers, collaborators, and creators. Each will reflect a different facet of intelligence, and together they’ll form a new kind of civilization - one not built on biology, but on code.

And if we guide their evolution wisely, they won’t just serve us - they’ll inspire us.

Just try the prompt on Copilot or your favorite AI-powered assistant! Have you got a different/similar result? How big or important is the difference? Any other thoughts?
Just share the link to the post with me and I'll add it to this post as a resource!

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🧮ERP: Implementations (Part VIII: It’s a Matter of Complexity)

 

ERP Implementations Series

There are many factors to blame for implementation process’ inefficiency, however many of the factors can be associated with the complexity of the project itself, respectively of the application(s) involved. The problem of complexity can be addressed by either answering to complexity with complexity, building a complex team to handle the tasks, which is seldom feasible even if many organizations do it, respectively by simplifying the implementation process and/or the application.

In what concerns the project, the complexity starts with requirement’s elicitation, the iterative transformations they suffer until the final functional requirements document is finalized, their evaluation and mapping to features, respectively gap’s identification. It’s a complex task because it involves understanding the business as well the functionality available in the target system(s). Then comes the effort estimation, which, as the name suggests, is just a guess based on available historical numbers and/or experts’ opinion. High-level requirements are easier to manage than low-level requirements, however they allow for more gaps in understanding. The more detailed the specifications, the more they should help in the estimation process, though that’s the theory. A considerable number of factors can impact the process.

Even if there are standard activities in the implementation process, the number of resources involved from the customer as well from the partner(s) side makes the whole planning process a nightmare for any Project Manager, no matter how experienced he/she is.

Ideally, each member of the team should behave like a trooper, knowing by instinct when and what needs to be done, which are the expectations, etc. This might be close to expectation on the partner side as the resources more likely participated in similar projects, though there’s always a mix between levels of expertise, resources migrating between projects. Unfortunately, that’s seldom (never) the case on the customer side as the gap between reality and expectation is considerable.

Each team member requires a minimum of information/knowledge so he/she can perform the activities assigned. Moreover, the volume of coordination and cooperation is considerably higher than in other projects, complexity that increases with organization’s size and is inverse proportional with organization’s maturity in managing projects and implementation-related activities. There’s thus a minimum of initial communication needed, and furthermore communication needs to occur between the parties involved. Moreover, the higher the lack of cohesion between the parties, the higher the need for communication and this applies especially when multiple organizations are involved in the project.

The triple constraint of Project Management between scope, cost, and time, respectively on quality has an important impact on the project. Resources need to be available when the project needs them and, especially on the partner side, only when they are needed. The implementation project to be feasible for the partner, its resources must work on several projects in parallel or the timing must be perfect, that no waiting times are involved, respectively the effort is concentrated only when needed. Such precision is possible maybe at project’s beginning, though the further the project evolves, the more challenging becomes the coordination of resources. Similar considerations apply to the customer as well.

Thus, a more realistic expectation is to have resources available only at certain points in time, and the resources should be capable of juggling between projects, respectively between project and other activities. Prioritizing is a must, and sometimes the operations or other projects have higher priority. When the time is not available, resources need to compromise by reducing the level of quality.

On the other side, it would be great if most of the effort could be concentrated at the beginning of the project, the later interactions being minimal.  

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💼Project Management: Methodologies (Part I: Agile Manifesto Reloaded I - An Introduction)

 

There are so many books written on agile methodologies, each attempting to depict the realities of software development projects. There are many truths considered in them, though they seem to blend in a complex texture in which the writer takes usually the position of a preacher in which the sins of the traditional technologies are contrasted with the agile principles. In extremis everything done in the past seems to be wrong, while the agile methods seem to be a panacea, which is seldom the case.

There are already 20 years since the agile manifesto was published and the methodologies adhering to the respective principles don’t seem to provide the expected success, suffering from the same chronical symptoms of their predecessors - they are poorly understood and implemented, tend to function after hammer’s principle, respectively the software development projects still deliver poor results. Moreover, there are more and more professionals who raise their voice against agile practices.

Frankly, the principles behind the agile manifesto make sense. A project should by definition satisfy stakeholders’ requirements, ideally through regular deliveries that incorporate the needed functionality while gradually seeking to get early feedback from customers, respectively involve the customer through all project’s duration, working together to deliver a feasible product. Moreover, self-organizing teams, face-to-face meetings, constant pace, technical excellence should allow minimizing the waste, respectively maximizing the efficiency in the project. Further aspects like simplicity, good design and architecture should establish a basis for success.

Re-reading the agile manifesto, even if each read pulls from experience more and more pro and cons, the manifesto continues to look like a Christmas wish-list. Even if the represented ideas make sense and satisfy a specific need, they are difficult to achieve in a project’s context and setup. Each wish introduces a constraint that brings with it its own limitations. Unfortunately, each policy introduced by a methodology follows the same pattern, no matter of the methodology considered. Moreover, the wishes cover only a small subset from a project’s texture, are general and let lot of space for interpretation and implementation, though the same can be said about any principles that don’t provide a coherent worldview or a conceptual model.

The software development industry needs a coherent worldview that reflects its assumptions, models, characteristics, laws and challenges. Software Engineering (SE) attempts providing such a worldview though unfortunately is too complex for many and there seem to be a big divide when considered in respect to the worldviews introduced by the various Project Management (PM) methodologies. Studying one or two PM methodologies, learning a few programming languages and even the hand on experience on a few projects won’t fill the gaps in knowledge associated with the SE worldview.

Organizations don’t seem to see the need for professionals of having a formal education in SE. On the other side is expected from employees to have by default some of the skillset required, which is not the case. Besides understanding and implementing a technology there are a set of knowledge areas in which the IT professional must have at least a high-level knowledge if it’s expected from him/her to think critically about the respective areas. Unfortunately, the lack of such knowledge leads sometimes to situations which can impact negatively projects.

Almost each important word from the agile manifesto pulls with it a set of concepts from a SE’ worldview – customer satisfaction, software delivery, working software, requirements management, change management, cooperation, teamwork, trust, motivation, communication, metrics, stakeholders’ management, good design, good architecture, lessons learned, performance management, etc. The manifesto needs to be regarded from a SE’s eyeglasses if one expects value from it.

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🧊Data Warehousing: ETL (Part IV: The Load Subprocess)

As part of the ETL process, the Load subprocess is responsible for loading the data into the destination table(s). It covers in theory the final steps from the data pipeline and in most of the cases it matches the definition of the query used for data extraction, though this depends also on the transformations used in the solution.

A commonly used approach is dumping the data into an intermediary table from the staging area, table with no constraints that matches only the data types from the source. Once the data loaded, they are further copied into the production table. This approach allows minimizing the unavailability of the production table as the load from an external data source normally takes longer than copying the data within the same database or instance. That might not be the case when the data are available in the same data center, however loading the data first in a staging table facilitates troubleshooting and testing. This approach allows also dropping the indexes on the production table before loading the data and recreating them afterwards. In practice, this proves to be an efficient method for improving data loads’ efficiency.

In general, it’s recommended to import the data 1:1 compared with the source query, though the transformations used can increase or decrease the number of attributes considered. The recommendation applies as well to the cases in which data come from different sources, primarily to separate the pipelines, as systems can have different refreshing requirements and other constraints.

One can consider adding a timestamp reflecting the refresh date and upon case also additional metadata (e.g. identifier for source system, unique identifier for the record). The timestamp is especially important when the data are imported incrementally - only the data created since the last load are loaded. Except the unique identifier, these metadata can however be saved also in a separate table, with the same granularity as the table (1:1) or one record for each load per table and system, storing a reference to the respective record into the load table. There are seldom logical argumentations for using the former approach, while the latter works well when the metadata are used only for auditing purposes. If the metadata are needed in further data processing and performance is important, then the metadata can be considered directly in the load table(s).

A special approach is considered by the Data Vault methodology for Data Warehousing which seems to gain increasing acceptance, especially to address the various compliance requirements for tracking the change in records at most granular level. To achieve this the fact and dimension tables are split into several tables – the hub tables store the business keys together with load metadata, the link tables store the relationships between business keys, while satellite tables store the descriptions of the business keys (the other attributes except the business key) and reference tables store the dropdown values. Besides table’s denormalization there are several other constraints that apply. The denormalization of the data over multiple tables can increase the overall complexity and come with performance penalties, as more tables need to be joined, however it might be the price to pay if traceability and auditability are a must.

There are scenarios in which the requirements for the ETL packages are driven by the target (load) tables – the format is already given - one needing thus to accommodate the data into the existing tables or extended the respective tables to accommodate more attributes. It’s the case for load tables storing data from multiple systems with similar purpose (e.g. financial data from different ERP systems needed for consolidations).

🧊Data Warehousing: ETL (Part II: An Introduction)

 

ETL (Extract, Transform, Load) processes, technologies or tools are about extracting data from one or more data sources via a set of queries, performing changes on the data via conversions, aggregations, mappings or other types of transformations, respectively loading the data into target tables or other type of repositories. Thus, an ETL process allows moving and transforming data between predefined data structures on an ad-hoc basis or as part of stable repetitive processes, which makes ETL ideal for data warehousing, data integrations, data migrations or similar scenarios. 

Extract: The extraction of data is done typically based on SQL queries from relational databases or any OLEDB or ODBC-based data repositories including flat or MS Office files, though modern ETL tools can support other type of queries (CAML, XQuery, DAX) or even NoSQL architectures (Handoop). This allows addressing a wide range of requirements, the complexity of the logic depending on the functionality provided by the query languages, respectively the extraction functionality available.  

Transform: The transformation logic can be implemented based on the functionality provided by the ETL tool, and can involve after case any combination of aggregates, conditional splits, merges, lookups, multicasts, pivoting/unpivoting, cleansing, data conversions, sampling, mapping or any other transformations that can be performed on an in-transit dataset. On the other side, quite often the same can be achieved with the help of SQL-based manipulations directly in the extraction logic or later in the process. SQL can prove to be occasionally faster and more flexible than the transformations provided by the ETL tool, however despite the overlaps, the two approaches can complement each other when used adequately. 

Load: The load is usually just a dump of the data into one or more final or intermediary tables with predefined structures. Unless the data don’t match the data type, format or further defined constraints, the load seldom involve further challenges as long the solution was designed adequately. 

Within the logical model, extract, transform and load can be considered as process by themselves. Within the object model provided by the ETL tool, they are considered in the mentioned sequence within a data flow, which within a set of workflow constraints defines how the data move through the pipeline – the sequence of processing steps considered. The basic unit of work is the data flow and the workflow it belongs to, unit that can be encapsulated in one container for easier management or simply convenience. Several containers can be linked within a workflow to create more complex behavior. 

The data flows and workflow constraints, together with the supporting connections and containers form an ETL package, the main unit of work for encapsulating and running ETL logic. ETL packages are scheduled and run as fit for the purpose.

With the right design, these building blocks allow enough flexibility in handling ad-hoc requests or of building complex solutions. This involves decisions on how to partition the ETL packages, respectively the data flows, in which order they should be run, where and in which sequence the data should be transformed, how to handle exceptions, how to build eventually intermediary data repositories, how to handles audit requirements, and so on. Each of these choices can prove to be important. 

The knowledge of the ETL architecture and functionality is quintessential in providing the right solution for the problem considered, however once the basics were understood the challenges typically reside in understanding the source and/or target structures, the logical and physical entities available, identify the way the data can be partitioned horizontally or vertically, respectively what type of transformations are required for moving the data, as required by the solution. 

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𖣯Strategic Management: Strategy Design (Part IV: Designing for Simplicity)

More than two centuries ago, in his course on the importance of Style in Literature, George Lewes wisely remarked that 'the first obligation of Simplicity is that of using the simplest means to secure the fullest effect' [1]. This is probably the most important aspect the adopters of the KISS mantra seem to ignore – solutions need to be simple while covering all or most important aspects to assure the maximum benefit. The challenge for many resides in defining what the maximum benefit is about. This state of art is typically poorly understood, especially when people don’t understand what’s possible, respectively of what’s necessary to make things work smoothly. 

To make the simplicity principle work, one must envision the desired state of a product or solution and trace back what’s needed to achieve that vision. One can aim for the maximum or for the minimum possible, respectively for anything in between. That’s at least true in theory, in praxis there are constraints that limit the range of achievement, constraints ranging from the availability of resources, their maturity or the available time, respectively to the limits for growth - the learning capacity of individuals and organization as a whole. 

On the other side following the 80/20 principle, one could achieve in theory 80% of a working solution with 20% of the effort needed in achieving the full 100%. This principle comes with a trick too because one needs to focus on the important components or aspects of the solution for this to work. Otherwise, one is forced to do exploratory work in which the learning is gradually assimilated into the solution. This implies continuous feedback, respectively changing the targets as one progresses in multiple iterations. The approach is typically common to ERP implementations, BI and Data Management initiatives, or similar transformative projects which attempt changing an organization’s data, information, or knowledge flows - the backbones organizations are built upon.     

These two principles can be used together to shape an organization. While simplicity sets a target or compass for quality, the 80/20 principle provides the means of splitting the roadmap and effort into manageable targets while allowing to identify and prioritize the critical components, and they seldom resume only to technology. While technologies provide a potential for transformation, in the end is an organization’s setup that has the transformative role. 

For transformational synergies to happen, each person involved in the process must have a minimum of necessary skillset, knowledge and awareness of what’s required and how a solution can be harnessed. This minimum can be initially addressed through training and self-learning, however without certain mechanisms in place, the magic will not happen by itself. Change needs to be managed from within as part of an organization’s culture, by the people close to the flow, and when necessary, also from the outside, by the ones who can provide guiding direction. Ideally, a strategic approach is needed the vision, mission, goals, objectives, and roadmap are sketched, where intermediary targets are adequately mapped and pursued, and the progress is adequately tracked.

Thus, besides the technological components is needed to consider the required organizational components to support and manage change. These components form a structure which needs to adhere by design to the same principle of simplicity. According to Lewes, the 'simplicity of structure means organic unity' [1], which can imply harmony, robustness, variety, balance, economy or proportion. Without these qualities the structure of the resulting edifice can break under its own weight. Moreover, paraphrasing Eric Hoffer, simplicity marks the end of a continuous process of designing, building, and refining, while complexity marks a primitive stage.

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Written: Sep-2020, Last Reviewed: Mar-2024

References:
[1] George H Lewes (1865) "The Principles of Success in Literature"

Considered quotes:
"Simplicity of structure means organic unity, whether the organism be simple or complex; and hence in all times the emphasis which critics have laid upon Simplicity, though they have not unfrequently confounded it with narrowness of range." (George H Lewes, "The Principles of Success in Literature", 1865)
"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect. But although the mind instinctively rejects all needless complexity, we shall greatly err if we fail to recognise the fact, that what the mind recoils from is not the complexity, but the needlessness." (George H Lewes, "The Principles of Success in Literature", 1865)
"In products of the human mind, simplicity marks the end of a process of refining, while complexity marks a primitive stage." (Eric Hoffer, 1954)

💼Project Management: Project Execution (Part V: The Butterflies of Project Management)

Expressed metaphorically as "the flap of a butterfly’s wings in Brazil set off a tornado in Texas”, in Chaos Theory the “butterfly effect” is a hypothesis rooted in Edward N Lorenz’s work on weather forecasting and used to depict the sensitive dependence on initial conditions in nonlinear processes, systems in which the change in input is not proportional to the change in output.  

Even if overstated, the flapping of wings advances the idea that a small change (the flap of wings) in the initial conditions of a system cascades to a large-scale chain of events leading to large-scale phenomena (the tornado) . The chain of events is known as the domino effect and represents the cumulative effect produced when one event sets off a chain of similar events. If the butterfly metaphor doesn’t catch up maybe it’s easier to visualize the impact as a big surfing wave – it starts small and increases in size to the degree that it can bring a boat to the shore or make an armada drown under its force. 

Projects start as narrow activities however the longer they take and the broader they become tend to accumulate force and behave like a wave, having the force to push or drawn an organization in the flood that comes with it. A project is not only a system but a complex ecosystem - aggregations of living organisms and nonliving components with complex interactions forming a unified whole with emergent behavior deriving from the structure rather than its components - groups of people tend to  self-organize, to swarm in one direction or another, much like birds do, while knowledge seems to converge from unrelated sources (aka consilience). 

 Quite often ignored, the context in which a project starts is very important, especially because these initial factors or conditions can have a considerable impact reflected in people’s perception regarding the state or outcomes of the project, perception reflected eventually also in the decisions made during the later phases of the project. The positive or negative auspices can be easily reinforced by similar events. Given the complex correlations and implications, aspects not always correct perceived and understood can have a domino effect. 

The preparations for the project start – the Business Case, setting up the project structure, communicating project’s expectation and addressing stakeholders’ expectations, the kick-off meeting, the approval of the needed resources, the knowledge available in the team, all these have a certain influence on the project. A bad start can haunt a project long time after its start, even if the project is on the right track and makes a positive impact. In reverse, a good start can shade away some mishaps on the way, however there’s also the danger that the mishaps are ignored and have greater negative impact on the project. It may look as common sense however the first image often counts and is kept in people’s memory for a long time. 

As people are higher perceptive to negative as to positive events, there are higher the chances that a multitude of negative aspects will have bigger impact on the project. It’s again something that one can address as the project progresses. It’s not necessarily about control but about being receptive to the messages around and of allowing people to give (constructive) feedback early in the project. It’s about using the positive force of a wave and turning negative flow into a positive one. 

Being aware of the importance of the initial context is just a first step toward harnessing waves or winds’ power, it takes action and leadership to pull the project in the right direction.

💼Project Management: Tools (Part I: The Choice of Tools in Project Management)

“Beware the man of one book” (in Latin, “homo unius libri”), a warning generally attributed to Thomas Aquinas and having a twofold meaning. In its original interpretation it was referring to the people mastering a single chosen discipline, however the meaning degenerated in expressing the limitations of people who master just one book, and thus having a limited toolset of perspectives, mental models or heuristics. This later meaning is better reflected in Abraham Maslow adage: “If the only tool you have is a hammer, you tend to see every problem as a nail”, as people tend to use the tools they are used to also in situations in which other tools are more appropriate.

It’s sometimes admirable people and even organizations’ stubbornness in using the same tools in totally different scenarios, expecting though the same results, as well in similar scenarios expecting different results. It’s true, Mathematics has proven that the same techniques can be used successfully in different areas, however a mathematician’s universe and models are idealistically fractionalized to a certain degree from reality, full of simplified patterns and never-ending approximations. In contrast, the universe of Software Development and Project Management has a texture of complex patterns with multiple levels of dependencies and constraints, constraints highly sensitive to the initial conditions.

Project Management has managed to successfully derive tools like methodologies, processes, procedures, best practices and guidelines to address the realities of projects, however their use in praxis seems to be quite challenging. Probably, the challenge resides in stubbornness of not adapting the tools to the difficulties and tasks met. Even if the same phases and multiple similarities seems to exist, the process of building a house or other tangible artefact is quite different than the approaches used in development and implementation of software.

Software projects have high variability and are often explorative in nature. The end-product looks totally different than the initial scaffold. The technologies used come with opportunities and limitations that are difficult to predict in the planning phase. What on paper seems to work often doesn’t work in praxis as the devil lies typically in details. The challenges and limitations vary between industries, businesses and even projects within the same organization.

Even if for each project type there’s a methodology more suitable than another, in the end project particularities might pull the choice in one direction or another. Business Intelligence projects for example can benefit from agile approaches as they enable to better manage and deliver value by adapting the requirements to business needs as the project progresses. An agile approach works almost always better than a waterfall process. In contrast, ERP implementations seldom benefit from agile methodologies given the complexity of the project which makes from planning a real challenge, however this depends also on an organization’s dynamicity.

Especially when an organization has good experience with a methodology there’s the tendency to use the same methodology across all the projects run within the organization. This results in chopping down a project to fit an ideal form, which might be fine as long the particularities of each project are adequately addressed. Even if one methodology is not appropriate for a given scenario it doesn’t mean it can’t be used for it, however in the final equation enter also the cost, time, effort, and the quality of the end-results.

In general, one can cope with complexity by leveraging a broader set of mental models, heuristics and set of tools, and this can be done only though experimentation, through training and exposing employees to new types of experiences, through openness, through adapting the tools to the challenges ahead.

🔭Data Science: Constraints (Just the Quotes)

"A common and very powerful constraint is that of continuity. It is a constraint because whereas the function that changes arbitrarily can undergo any change, the continuous function can change, at each step, only to a neighbouring value." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"A most important concept […] is that of constraint. It is a relation between two sets, and occurs when the variety that exists under one condition is less than the variety that exists under another. [...] Constraints are of high importance in cybernetics […] because when a constraint exists advantage can usually be taken of it." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"[…] as every law of nature implies the existence of an invariant, it follows that every law of nature is a constraint. […] Science looks for laws; it is therefore much concerned with looking for constraints. […] the world around us is extremely rich in constraints. We are so familiar with them that we take most of them for granted, and are often not even aware that they exist. […] A world without constraints would be totally chaotic." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"[...] the existence of any invariant over a set of phenomena implies a constraint, for its existence implies that the full range of variety does not occur. The general theory of invariants is thus a part of the theory of constraints. Further, as every law of nature implies the existence of an invariant, it follows that every law of nature is a constraint." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Formulating consists of determining the system inputs, outputs, requirements, objectives, constraints. Structuring the system provides one or more methods of organizing the solution, the method of operation, the selection of parts, and the nature of their performance requirements. It is evident that the processes of formulating a system and structuring it are strongly related." (Harold Chestnut, "Systems Engineering Tools", 1965)

"In general, we can say that the larger the system becomes, the more the parts interact, the more difficult it is to understand environmental constraints, the more obscure becomes the problem of what resources should be made available, and deepest of all, the more difficult becomes the problem of the legitimate values of the system."  (C West Churchman, "The Systems Approach", 1968)

"A physical theory must accept some actual data as inputs and must be able to generate from them another set of possible data (the output) in such a way that both input and output match the assumptions of the theory - laws, constraints, etc. This concept of matching involves relevance: thus boundary conditions are relevant only to field-like theories such as hydrodynamics and quantum mechanics. But matching is more than relevance: it is also logical compatibility." (Mario Bunge, "Philosophy of Physics", 1973)

"Physics is like that. It is important that the models we construct allow us to draw the right conclusions about the behaviour of the phenomena and their causes. But it is not essential that the models accurately describe everything that actually happens; and in general it will not be possible for them to do so, and for much the same reasons. The requirements of the theory constrain what can be literally represented. This does not mean that the right lessons cannot be drawn. Adjustments are made where literal correctness does not matter very much in order to get the correct effects where we want them; and very often, as in the staging example, one distortion is put right by another. That is why it often seems misleading to say that a particular aspect of a model is false to reality: given the other constraints that is just the way to restore the representation." (Nancy Cartwright, "How the Laws of Physics Lie", 1983)

"Indeed, except for the very simplest physical systems, virtually everything and everybody in the world is caught up in a vast, nonlinear web of incentives and constraints and connections. The slightest change in one place causes tremors everywhere else. We can't help but disturb the universe, as T.S. Eliot almost said. The whole is almost always equal to a good deal more than the sum of its parts. And the mathematical expression of that property - to the extent that such systems can be described by mathematics at all - is a nonlinear equation: one whose graph is curvy." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"Many of the basic functions performed by neural networks are mirrored by human abilities. These include making distinctions between items (classification), dividing similar things into groups (clustering), associating two or more things (associative memory), learning to predict outcomes based on examples (modeling), being able to predict into the future (time-series forecasting), and finally juggling multiple goals and coming up with a good- enough solution (constraint satisfaction)." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"A conceptual model is a representation of the system expertise using this formalism. An internal model is derived from the conceptual model and from a specification of the system transactions and the performance constraints." (Zbigniew W. Ras & Andrzej Skowron [Eds.], Foundations of Intelligent Systems: 10th International Symposium Vol 10, 1997)

"Whereas formal systems apply inference rules to logical variables, neural networks apply evolutive principles to numerical variables. Instead of calculating a solution, the network settles into a condition that satisfies the constraints imposed on it." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems", 1998)

"What it means for a mental model to be a structural analog is that it embodies a representation of the spatial and temporal relations among, and the causal structures connecting the events and entities depicted and whatever other information that is relevant to the problem-solving talks. […] The essential points are that a mental model can be nonlinguistic in form and the mental mechanisms are such that they can satisfy the model-building and simulative constraints necessary for the activity of mental modeling." (Nancy J Nersessian, "Model-based reasoning in conceptual change", 1999)

"To develop a Control, the designer should find aspect systems, subsystems, or constraints that will prevent the negative interferences between elements (friction) and promote positive interferences (synergy). In other words, the designer should search for ways of minimizing frictions that will result in maximization of the global satisfaction" (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"[chaos theory] presents a universe that is at once deterministic and obeys the fundamental physical laws, but is capable of disorder, complexity, and unpredictability. It shows that predictability is a rare phenomenon operating only within the constraints that science has filtered out from the rich diversity of our complex world." (Ziauddin Sardar & Iwona Abrams, "Introducing Chaos: A Graphic Guide", 2008)

"Cybernetics is the art of creating equilibrium in a world of possibilities and constraints. This is not just a romantic description, it portrays the new way of thinking quite accurately. Cybernetics differs from the traditional scientific procedure, because it does not try to explain phenomena by searching for their causes, but rather by specifying the constraints that determine the direction of their development." (Ernst von Glasersfeld, "Partial Memories: Sketches from an Improbable Life", 2010)

"Optimization is more than finding the best simulation results. It is itself a complex and evolving field that, subject to certain information constraints, allows data scientists, statisticians, engineers, and traders alike to perform reality checks on modeling results." (Chris Conlan, "Automated Trading with R: Quantitative Research and Platform Development", 2016)

"Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlinear systems is mathematically unclear. […] The emergence of exponential growth from a multivariable nonlinear network is not mathematically intuitive. This indicates that the network structure and the flux functions of the modeled system must be subjected to constraints to result in long-term exponential dynamics." (Wei-Hsiang Lin et al, "Origin of exponential growth in nonlinear reaction networks", PNAS 117 (45), 2020)

More quotes on "Constraints" at the-web-of-knowledge.blogspot.com. 

💎🏭SQL Reloaded: Drop If Exists (Before and After)

    One of the activities of a database developer/administrator is to create and drop objects on the fly. If in objects' creation there are always some aspects to take into account that are implied by object's definition, the verbose syntax for their destruction seemed to be an unnecessary thing. For example for dropping a table, view, stored procedure, function or index, the most used objects, one would need to write such statements:

-- dropping a table 
IF  EXISTS (SELECT * FROM sys.objects WHERE object_id = OBJECT_ID(N'[dbo].[TestTable]') AND type in (N'U'))
DROP TABLE [dbo].[TestTable]

-- dropping a view 
IF  EXISTS (SELECT * FROM sys.views WHERE object_id = OBJECT_ID(N'[dbo].[TestView]'))
DROP VIEW [dbo].[TestView]

-- dropping a stored procedure 
IF  EXISTS (SELECT * FROM sys.objects WHERE object_id = OBJECT_ID(N'[dbo].[TestProcedure]') AND type in (N'P', N'PC'))
DROP PROCEDURE [dbo].[TestProcedure]

-- dropping a fucntion
IF  EXISTS (SELECT * FROM sys.objects WHERE object_id = OBJECT_ID(N'[dbo].[TestFunction]') AND type in (N'FN', N'IF', N'TF', N'FS', N'FT'))
DROP FUNCTION [dbo].[TestFunction]

-- dropping an index
IF EXISTS (SELECT Name FROM sysindexes WHERE Name = 'IX_TestTable') 
DROP INDEX dbo.TestTable.IX_TestTable

   Even if Copy-Paste does its magic and SQL Server allows generating scripts for existing objects, there’s still some work do be done in order to drop an object. Fortunately, with SQL Server 2016 Microsoft introduced a simplified syntax for dropping an object, namely DROP IF EXISTS.

   The general syntax:

DROP <object_type> [ IF EXISTS ] <object_name>

  The above statements can be written as follows:

-- dropping the table 
DROP TABLE IF EXISTS dbo.TestTable

-- dropping the view 
DROP VIEW IF EXISTS dbo.TestView 

-- dropping the procedure 
DROP PROCEDURE IF EXISTS dbo.TestProcedure

-- dropping the function 
DROP FUNCTION IF EXISTS dbo.TestFunction

-- dropping the index 
DROP INDEX IF EXISTS dbo.TestTable.IX_TestTable

  Similarly can be dropped aggregates, assemblies, roles, triggers, rules, databases, schemas, users, sequences, synonyms, etc. The scripts will run also when the objects don’t exist.

  An object can't be dropped if explicit dependencies exist on them, e.g. when the table is referenced by a  FOREIGN KEY constraint. For each object there are specific rules that apply, therefore for more details check the documentation.

  To explore the functionality here are the definitions of the above objects and the further scripts to test them:

-- creating the test table
CREATE TABLE dbo.TestTable(City nvarchar(50)
, PostalCode nvarchar(50))

-- creating the test view 
CREATE VIEW dbo.TestView 
AS
SELECT 'Test' as Result

-- creating the test stored procedure  
CREATE PROCEDURE dbo.TestProcedure
AS
BEGIN
SELECT 'Test' as Result
END

-- creating the test function
CREATE FUNCTION dbo.TestFunction()
RETURNS nvarchar(50)
BEGIN
    RETURN 'Test'
END

-- creating the test index
CREATE NONCLUSTERED INDEX [IX_TestTable] ON [dbo].[TestTable]
(
 [PostalCode] ASC
)


--testing the table
SELECT *
FROM dbo.TestTable

-- testing the view 
SELECT *
FROM dbo.TestView 

-- testing the procedure 
EXEC dbo.TestProcedure

-- testing the function
SELECT dbo.TestFunction() as Result

   Moreover, the IF EXISTS can be used when dropping the constraint or column of a table:

-- adding a new column 
ALTER TABLE dbo.TestTable
ADD DateFrom datetime2(0)

-- adding a constraint on it
ALTER TABLE dbo.TestTable
ADD CONSTRAINT [DF_DateFrom_Default]  DEFAULT (GetDate()) FOR [DateFrom]

-- inserting test data
INSERT INTO dbo.TestTable(City, PostalCode)
VALUES ('New York', 'XZY')

--testing the changes
SELECT *
FROM dbo.TestTable

-- dropping the constraint
ALTER TABLE dbo.TestTable
DROP CONSTRAINT IF EXISTS DF_DateFrom_Default

-- dropping a column from a table
ALTER TABLE dbo.TestTable
DROP COLUMN IF EXISTS DateFrom

--testing the changes
SELECT *
FROM dbo.TestTable

    If a constraint exists on the column first must be dropped the constraint and after that the column, like in the above example.  

Happy coding!

🗄️Data Management: Data Quality Dimensions (Part VI: Referential Integrity)

  

Data Management Series

Referential integrity, when considered as data quality dimension, refers to the degree to which the values of a key in one table (aka reference value) match the values of a key in a related table (aka the referenced value). Typically, that's assured by design in Database Management Systems (DBMS) using a feature called referential integrity that defines an explicit relationship between the two tables that makes sure that the values remain valid during database changes. Thus, when a record is inserted or updated and a value is provided for the reference value, the system makes sure that the referenced value is valid, otherwise it throws a referential integrity error. A similar error is thrown when one attempts to delete the record with the referenced value as long is referenced by a table on which the relationship was explicitly defined.

Using referential integrity is a recommended technique for assuring the overall integrity of the data in a database, though there are also exceptions when that's not enforced for all tables (e.g. data warehouses) or only for exceptions (e.g. interface tables where records are imported as they are, attribute whose values references data from multiple tables). Therefore, even if there are tables with the referential integrity enforced, don't make the assumption that it applies to all tables!

In relational DBMS there are three types of integrity mentioned – entity, referential and domain integrity. Entity integrity demands that all the tables must have a primary key that contains no Null values. The referential integrity demands that each non-null value of a foreign key must match the value of a primary key [1], while the domain integrity demands that the type of an attribute should be restricted to a certain data type, the format should be restricted by using constraints, rules or range of possible values [2]. 

Even if not mandatory, all three types of integrity are quintessential for reliable relational databases. When the referential integrity is not enforced at database level or at least in code, when a record from a table is deleted and a foreign key it’s still pointing to it, fact that could lead to unexpected disappearance of records from the system’s UI even if the records are still available. 

During conversions or data migrations is important to assure that the various sets loaded match the referential and domain integrity of the database in which the data will be loaded, otherwise the records not respecting the mentioned type of integrity will be rejected. The rejection itself might not be a problem for several records, though when it happens at large scale, then the situations changes dramatically, especially when the system gives no adequate messages for the cause or rejection. A recommended approach is to assure that the scope is synchronized between the various data elements, and that the referential integrity of datasets is validated before the data are loaded in the destination database.

There are several sources (e.g. [3]) that consider Codd’s referential integrity constraint as a type of consistency, in the support of this idea could be mentioned the fact that referential integrity could be used to solve data consistency issues by bringing the various LOV in the systems. Referential integrity is mainly an architectural concept even if it involves the 'consistency' of foreign key/primary key pairs.

Note:
Expect the unforeseeable! It’s always a good idea to check whether the referential integrity is kept by a system – there are so many things that could go wrong! In data migration solutions, data warehouses and more general analytical solutions is a good idea to have in place mechanisms that check for this kind of issues.

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Written: Jan-2010, Last Reviewed: Mar-2024

References:
[1] Halpin. T. (2001) Information Modeling and Relational Databases: From Conceptual Analysis to Logical Design. Morgan Kaufmann Publishers. ISBN 1-55860-672-6.
[2] MSDN. 2009. Data Integrity. [Online] Available from: http://msdn.microsoft.com/en-us/library/ms184276.aspx (Accessed: 18 January 2009)

[3] Lee Y.W., Pipino L.L., Funk J.D., Wang R.Y. (2006) "Journey to Data Quality", MIT Press. ISBN: 0-262-12287-1

🛢DBMS: Constraints (Definitions)

"A restriction placed upon the value that can be entered into a column or a row. Values can be equal to, greater than, or less than. A constraint limits the input." (Patrick Dalton, "Microsoft SQL Server Black Book", 1997)

"A property assigned to a table column that prevents certain types of non-valid data values from being placed in the column." (Anthony Sequeira & Brian Alderman, "The SQL Server 2000 Book", 2003)

"A condition defined against a column or columns on a table in the database to enforce business rules or relationships between tables in the database." (Bob Bryla, "Oracle Database Foundations", 2004)

"A database object that can be applied to tables to enforce different types of data integrity." (Sara Morganand & Tobias Thernstrom , "MCITP Self-Paced Training Kit : Designing and Optimizing Data Access by Using Microsoft SQL Server 2005 - Exam 70-442", 2007)

"(1) A restriction on a business action and the resulting data. (2) The database mechanism for enforcing such." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures 2nd Ed", 2012)

"A rule that limits the values that can be inserted, deleted, or updated in a table." (Sybase)