🤖〽️Prompt Engineering: Copilot Unabridged (Part 24: The Next Steps in the Development of AI)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the next steps in the development of AI"

Introduction

Artificial Intelligence (AI) is evolving at an unprecedented pace, shaping industries, economies, and daily life. As AI continues to advance, researchers, businesses, and governments are focusing on the next steps in its development - from improving efficiency to addressing ethical concerns. This blog post explores the key areas driving AI’s future growth and the innovations that will define the next phase of AI evolution.

1. Advancing AI Infrastructure and Computing Power

AI’s capabilities depend on high-performance computing, and the next phase of development will focus on enhancing AI infrastructure. Companies and governments are investing in AI supercomputers to support large-scale AI applications.

For example, Ericsson, AstraZeneca, SAAB, SEB, and Wallenberg Investments have formed a consortium to build Sweden’s largest AI supercomputer, powered by Nvidia’s DGX SuperPODs. This infrastructure will enable AI-driven advancements in network intelligence, drug discovery, defense systems, and financial services.

2. AI for Secure and Ethical Software Development

As AI becomes more integrated into software development, security concerns are growing. Companies like Snyk are launching AI-native platforms to govern and secure AI-driven software. These platforms aim to:

• Reduce AI-generated security vulnerabilities in software development.
• Enhance governance and policy enforcement for AI applications.
• Improve AI security posture management to prevent cyber threats.

Ensuring AI is secure and ethically developed will be a major focus in the coming years.

3. AI in Data Analytics and Business Intelligence

AI is revolutionizing data analytics, enabling businesses to process information faster and more efficiently. dbt Labs has introduced the dbt Fusion engine, designed to optimize AI-driven analytics and reduce cloud costs. This innovation will:

• Accelerate AI-powered data processing for enterprises.
• Improve AI-driven decision-making in business intelligence.
• Enhance AI’s ability to manage large-scale data pipelines.

AI’s role in business intelligence and analytics will continue to expand, making data-driven strategies more effective.

4. AI for National and Regional Development

Governments and corporations are investing in AI to drive national and regional innovation. Safaricom, for example, has pledged $500 million to develop AI infrastructure in East Africa. This investment will support:

• AI-powered data centers for regional businesses.
• Edge computing capabilities to improve AI efficiency.
• Digital skills programs to train local developers in AI technologies.

AI’s development is becoming a global priority, with nations investing in AI to boost economic growth and technological leadership.

5. AI’s Role in Scientific Discovery and Automation

AI is transforming scientific research by accelerating discoveries in medicine, engineering, and environmental science. Future AI advancements will focus on:

• AI-driven drug discovery to develop new treatments faster.
• AI-powered automation in manufacturing to improve efficiency.
• AI-enhanced climate modeling to predict environmental changes.

AI’s ability to process vast amounts of data will continue to drive breakthroughs in multiple fields.

Conclusion: AI’s Future Is Shaped by Innovation and Responsibility

The next steps in AI development will focus on enhancing infrastructure, securing AI-driven software, optimizing data analytics, expanding AI’s role in national development, and advancing scientific discovery. As AI continues to evolve, responsible innovation and ethical governance will be crucial to ensuring AI benefits society.

Disclaimer: The whole text was generated by Copilot 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 16: How AI Can Use Its Intelligence to Help Society)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can use its intelligence to help society"

Introduction

Artificial Intelligence (AI) is reshaping industries, solving complex problems, and unlocking new possibilities in science, business, and daily life. While AI comes with challenges, its potential to positively impact society is undeniable. From healthcare advancements to environmental sustainability, AI can use its intelligence to empower individuals, strengthen communities, and create a better future.

1. AI Revolutionizing Healthcare and Medicine

AI-driven tools are transforming healthcare by improving diagnostics, enhancing treatment plans, and enabling personalized medicine. Some of the ways AI benefits healthcare include:

• Early disease detection: AI-powered medical imaging can identify patterns in X-rays, MRIs, and CT scans that humans might miss.
• Predictive analytics: AI helps anticipate disease outbreaks by analyzing health trends and patient data.
• Personalized treatment plans: AI tailors medications and therapies based on individual genetic profiles for better outcomes.

AI’s ability to process vast amounts of medical data makes healthcare more efficient, accessible, and accurate, ultimately saving lives.

2. AI Enhancing Education and Learning

AI is revolutionizing education by personalizing learning experiences and supporting educators. Key benefits include:

• Smart tutoring systems: AI adapts lessons based on a student’s strengths and weaknesses.
• Automated grading: AI streamlines assessments, giving teachers more time for one-on-one interaction.
• Language translation: AI breaks barriers, making education globally accessible.

AI ensures education is more inclusive, adaptive, and engaging, helping learners reach their full potential.

3. AI Tackling Climate Change and Environmental Issues

AI plays a crucial role in environmental sustainability by providing data-driven insights into climate change, conservation efforts, and resource management. AI-driven solutions include:

• Optimizing energy consumption: AI manages electricity grids for efficiency, reducing waste.
• Wildlife protection: AI analyzes satellite images to track endangered species and prevent illegal deforestation.
• Climate modeling: AI predicts environmental changes, helping scientists develop proactive solutions.

AI can monitor, predict, and mitigate environmental challenges, contributing to a healthier planet.

4. AI Advancing Social Good and Humanitarian Efforts

AI supports humanitarian efforts by improving disaster response, combating misinformation, and fostering inclusion. Notable AI contributions include:

• Disaster relief optimization: AI predicts hurricanes, earthquakes, and floods, enabling faster emergency response.
• Combating misinformation: AI detects fake news and disinformation, protecting public discourse.
• Enhancing accessibility: AI-powered speech recognition assists people with disabilities, enabling greater participation.

AI strengthens societies by improving communication, accessibility, and crisis management.

5. AI Driving Economic Growth and Innovation

Businesses and industries thrive on AI’s ability to optimize workflows, reduce inefficiencies, and accelerate innovation. AI promotes economic benefits such as:

• Smarter supply chain management: AI predicts demand fluctuations, improving production efficiency.
• AI-driven research: AI accelerates scientific discoveries in medicine, engineering, and material sciences.
• Job creation in AI fields: AI generates careers in data science, software engineering, and AI ethics.

AI boosts productivity across industries, ensuring economic resilience and technological advancement.

Conclusion: AI’s Role in Shaping a Better Future

AI is more than just technology - it’s a tool for empowerment, innovation, and sustainability. By harnessing AI’s intelligence responsibly, society can improve healthcare, education, environmental protection, humanitarian efforts, and economic growth.

The key to maximizing AI’s benefits lies in ethical development, transparency, and collaboration, ensuring AI serves humanity positively and equitably.

Disclaimer: The whole text was generated by Copilot 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 9: The Perils of AI - Risks and Challenges in a Rapidly Evolving Technology)

Prompt Engineering Series

The Perils of AI: Risks and Challenges in a Rapidly Evolving Technology

Artificial Intelligence (AI) is transforming industries, automating processes, and enhancing human capabilities. However, alongside its benefits, AI presents serious risks that must be carefully managed. From ethical concerns to security vulnerabilities, understanding the perils of AI is crucial for responsible development and regulation.

1. Bias and Discrimination in AI

One of the most concerning risks is algorithmic bias, where AI systems unintentionally reinforce societal prejudices. AI models learn from historical data, and if that data reflects racial, gender, or socioeconomic biases, AI decisions may disadvantage certain groups.

For example, AI-powered hiring algorithms have been found to favor male candidates over female candidates due to past hiring patterns. Similarly, facial recognition technology has lower accuracy rates for people with darker skin tones, leading to misidentifications and unfair treatment.

2. Privacy and Surveillance Threats

AI depends on massive amounts of personal data, which raises concerns about privacy and surveillance. Governments and corporations use AI for monitoring behavior, tracking movements, and collecting user data, often without explicit consent.

Examples of AI-driven privacy risks include:

• Social media monitoring: AI analyzes online activity to shape advertisements or influence user behavior.
• Mass surveillance systems: AI-powered cameras and tracking tools can infringe on personal freedoms.
• Data breaches: AI-driven cyberattacks can compromise sensitive information.

Without strict data protection laws, AI could erode personal privacy and digital security.

3. AI in Cybersecurity: An Increasing Threat

While AI enhances security by detecting cyber threats, it also serves as a powerful weapon for hackers. Malicious actors leverage AI to create sophisticated cyberattacks, including:

• Deepfake scams – AI-generated videos and voice impersonations for fraud.
• AI-driven malware – Malicious software that adapts to evade detection.
• Automated phishing attacks – AI personalizes scam messages for higher success rates.

AI-powered hacking tools outpace traditional security systems, making cybersecurity an ongoing challenge.

4. Job Displacement Due to Automation

AI-driven automation reduces workforce demand in sectors like manufacturing, transportation, customer service, and finance. While AI creates new roles in software engineering, AI ethics, and data science, it eliminates jobs requiring routine tasks and repetitive labor.

Industries facing AI-related job losses include:

• Retail and call centers: AI chatbots and self-checkout systems reduce human employment.
• Manufacturing: AI-powered robotics replace manual labor.
• Finance: AI automates investment decisions, reducing demand for financial analysts.

Governments and businesses must invest in workforce reskilling to ensure workers transition to AI-augmented roles.

5. Ethical Concerns and Unregulated AI

The rapid expansion of AI raises ethical dilemmas, including concerns about autonomous weapons, misinformation, and lack of accountability. Without clear regulations, AI could exacerbate global security risks rather than solving them.

Critical ethical debates surrounding AI include:

• AI in warfare: Autonomous drones and robotic weapons could make war more unpredictable and destructive.
• Misinformation and propaganda: AI-generated fake news threatens democracy and trust.
• Unregulated decision-making: AI lacks human ethics, empathy, and context awareness.

Conclusion: Navigating AI’s Risks

AI presents immense possibilities, but also substantial risks. Bias, privacy threats, cybersecurity vulnerabilities, job displacement, and ethical dilemmas must be addressed through responsible development, transparent regulation, and ethical AI frameworks.

The future of AI depends on how humans govern its growth, ensuring it remains a tool for innovation rather than a source of harm.

Disclaimer: The whole text was generated by Copilot 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 7: The Risks Associated with AI: Challenges in a Rapidly Advancing World)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the risks associated with AI"

Introduction

Artificial Intelligence (AI) has become an integral part of our society, driving innovation across industries and reshaping how businesses, governments, and individuals operate. While AI offers efficiency, automation, and data-driven decision-making, it also introduces significant risks that must be carefully managed. In this blog post, we explore the major risks associated with AI and why ethical considerations are crucial for its responsible development.

1. Bias and Discrimination in AI

One of the most critical risks in AI development is algorithmic bias, which occurs when AI systems inherit prejudices from their training data. AI models are trained on vast datasets collected from real-world sources, but these sources may contain historical inequalities, societal biases, and skewed representations.

For example, AI-driven hiring systems have been found to favor male candidates over female candidates, simply because historical hiring data reflects gender disparities in certain fields. Similarly, AI-powered facial recognition has lower accuracy when identifying people from racial minorities due to biased training datasets.

Mitigating bias in AI requires diverse training data, continuous audits, and transparent AI decision-making. Without these safeguards, AI can reinforce existing biases rather than eliminate them.

2. Privacy and Data Security Risks

AI relies on massive amounts of data to function effectively, but this dependence raises serious privacy concerns. With AI-driven automation and surveillance technologies, individuals face increased risks of data breaches, unauthorized data collection, and loss of personal privacy.

For example, AI-powered marketing tools analyze consumer behavior through social media and online activity. While this allows businesses to deliver personalized advertisements, it also raises concerns about data misuse and manipulation.

Moreover, AI-based cybersecurity threats, such as deepfake technology, enable malicious actors to impersonate individuals and spread misinformation. If AI is not regulated properly, society could face a loss of trust in digital interactions.

3. AI in Cybersecurity: A Double-Edged Sword

AI is both a tool for cybersecurity and a threat to cybersecurity. While AI enhances security by detecting patterns in cyberattacks and automating threat detection, hackers can also use AI to bypass traditional security measures.

Some AI-driven cyberattacks include:

• Deepfake scams: AI-generated videos and audio impersonate real individuals, enabling fraud or misinformation.
• AI-powered malware: Malicious software adapts in real-time to evade detection.
• Automated phishing attacks: AI personalizes fraudulent emails to increase success rates.

Cybersecurity professionals must stay ahead by leveraging AI to counter threats, but the arms race between cybercriminals and security systems continues to evolve.

4. Job Displacement Due to AI Automation

AI automation is transforming industries by replacing repetitive human tasks with machines, but this shift raises concerns about mass job displacement. While AI creates new roles in data science, robotics, and AI ethics, it also replaces traditional jobs in manufacturing, customer service, and transportation.

For example, AI-powered chatbots have reduced the need for human customer service representatives, while autonomous vehicles threaten to disrupt the transportation industry. AI-driven automation in retail, finance, and healthcare could replace millions of jobs unless reskilling programs and workforce adaptations are prioritized.

Governments and businesses must take proactive steps to ensure AI complements human labor rather than completely replacing it.

5. Ethical and Regulatory Challenges

AI's lack of human intuition, morality, and accountability introduces ethical dilemmas that society must address.

Key ethical concerns include:

• AI in warfare: The development of autonomous weapons raises fears about unregulated warfare and unintended consequences.
• Manipulation of information: AI-driven fake news generation threatens democracy by spreading misinformation.
• Lack of transparency: Many AI systems operate as “black boxes”, meaning users cannot fully understand how decisions are made.

To manage these risks, governments, businesses, and researchers must collaborate on ethical AI development and policies that regulate its usage.

Conclusion: AI Requires Responsible Growth

While AI offers groundbreaking possibilities, its risks must be addressed through ethical considerations, regulation, and transparency. Bias, privacy concerns, cybersecurity threats, job displacement, and ethical dilemmas require proactive solutions to ensure AI benefits society without causing unintended harm.

The future of AI depends on how responsibly we shape its development. By implementing accountable AI governance, ethical oversight, and workforce adaptation strategies, society can leverage AI’s advantages while mitigating its risks.

Disclaimer: The whole text was generated by Copilot 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 6: Why There Are No Limits to AI Growth)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on why there are no limits to growth for AI"

Introduction

Artificial Intelligence (AI) has transcended its early stages of development and is now an integral part of business, science, and daily life. Unlike traditional technologies that have clear limits on scalability, AI operates under a unique framework - it evolves, learns, and refines itself over time. This adaptability ensures that AI's growth remains exponential, breaking past barriers that once seemed insurmountable.

AI’s Ability to Learn and Evolve

AI differs from other technologies because it isn’t bound by static programming - it learns and improves through machine learning (ML) and deep learning. These processes enable AI to:

• Optimize itself over time, making operations more efficient.
• Develop new patterns that even human researchers may not anticipate.
• Enhance accuracy and predictive capabilities across industries.

Unlike traditional software, which requires manual updates and modifications, AI continuously refines itself based on real-world interactions. This ability ensures perpetual growth, keeping AI ahead of conventional limitations.

Computational Power Is Increasing Rapidly

One of the biggest factors enabling AI’s endless growth is the exponential increase in computational power. Advances in quantum computing, cloud-based GPUs, and edge computing allow AI models to process massive datasets faster than ever. The more computing resources AI has access to, the more complex problems it can solve.

• Cloud computing democratizes AI, making it accessible to businesses worldwide.
• Quantum computing will eventually enable AI to process unimaginable volumes of data, far surpassing current limitations.
• Neuromorphic computing mimics the human brain, allowing AI models to improve efficiency while consuming less energy.

As technology progresses, AI's capabilities will continue expanding - faster, smarter, and more adaptable than ever before.

AI Has Infinite Applications Across Industries

AI's impact isn’t confined to a single sector; its growth is fueled by its versatility. From healthcare to finance, retail to space exploration, AI finds new ways to integrate into human progress.

• In medicine, AI assists in diagnosing diseases, customizing treatments, and even predicting outbreaks.
• In business, AI automates operations, reduces inefficiencies, and enhances data-driven decision-making.
• In research, AI enables discoveries in fields like genetics, material science, and astrophysics.
• In entertainment, AI creates realistic CGI, automates music composition, and personalizes media experiences.

Since AI isn’t restricted to one field, its expansion is self-perpetuating - each industry unlocks new AI-driven possibilities, further fueling innovation.

AI Growth Is Driven by Human Curiosity

AI’s evolution isn’t happening in isolation; human ingenuity fuels its progress. Researchers, engineers, and businesses continuously explore new ways to push AI beyond current capabilities.

• Developers refine AI models to improve accuracy and efficiency.
• Businesses invest billions in AI research, ensuring advancements continue.
• Governments and educational institutions integrate AI into policies and learning frameworks to accelerate adoption.

As long as humans remain curious about solving problems, enhancing intelligence, and optimizing processes, AI will continue to grow indefinitely.

Ethical Challenges Won’t Halt AI Growth

While ethical concerns - such as AI bias, surveillance, and job displacement - must be addressed, they won’t stop AI’s progress. Instead, they shape its development, ensuring responsible AI integration.

• Regulations will guide ethical AI use rather than stifle growth.
• Bias reduction strategies will make AI more inclusive while improving accuracy.
• AI augmentation will reshape workforces, enabling humans to focus on creative and strategic tasks.

Rather than presenting limitations, ethical considerations ensure that AI’s limitless growth remains aligned with human values and societal needs.

Conclusion: AI’s Infinite Expansion

Unlike previous technological revolutions that reached plateaus, AI’s adaptive nature, increasing computational power, and infinite applications ensure continued growth. With every breakthrough in machine learning, automation, and intelligent computing, AI inches closer to solving problems previously deemed impossible.

The future of AI is limitless - and as long as humans continue pushing boundaries, AI will evolve indefinitely.

Disclaimer: The whole text was generated by Copilot 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 1: The Importance of AI in Society - A Transformational Force)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the importance of AI in society"

Introduction

Artificial Intelligence (AI) has evolved from a futuristic concept to a daily reality that shapes industries, economies, and even personal lives. Once confined to science fiction, AI now drives technological progress and redefines the way we interact with the world. In this blog post, we will explore the significance of AI, how it influences various sectors, and the ethical considerations accompanying its rapid adoption.

AI: The Backbone of Modern Innovation

At its core, AI enables machines to perform tasks that typically require human intelligence - such as learning, reasoning, problem-solving, and decision-making. From automating repetitive tasks to augmenting human capabilities, AI has proven indispensable across multiple domains.

Take healthcare, for example. AI-powered algorithms analyze complex medical data to assist doctors in diagnosing diseases, predicting patient outcomes, and even recommending personalized treatment plans. Innovations like AI-driven robotic surgery and early cancer detection systems save lives and enhance the precision of medical procedures.

In finance, AI plays a crucial role in fraud detection, risk assessment, and automated trading. Financial institutions leverage machine learning models to analyze spending patterns, prevent cyberattacks, and provide tailored investment advice. AI’s ability to process massive amounts of data ensures accuracy and efficiency in financial operations.

Retail and e-commerce have also been transformed. AI-driven recommendation engines predict customer preferences based on purchasing behavior, allowing businesses to offer personalized shopping experiences. Chatbots streamline customer service, while automated inventory management optimizes supply chain efficiency. AI ensures that companies stay competitive in an increasingly digital marketplace.

AI and the Workplace: Automation vs. Augmentation

The integration of AI into the workplace sparks ongoing debates about its impact on jobs. While automation replaces certain manual tasks, it also opens doors for new career opportunities. AI-driven tools enable professionals to focus on creative and strategic work rather than repetitive processes.

For instance, AI-powered analytics help marketers assess consumer sentiment and predict trends with unprecedented accuracy. Similarly, AI assists engineers and researchers in designing smarter infrastructure, accelerating scientific discoveries, and optimizing business operations.

Rather than eliminating jobs, AI redefines them. Organizations must invest in upskilling employees to harness AI effectively, fostering a collaborative environment where human creativity complements machine intelligence.

Ethical Considerations and Responsible AI Development

With great power comes great responsibility. As AI grows more sophisticated, ethical concerns regarding data privacy, bias, and transparency become more pressing.

AI systems rely on large datasets to function, raising concerns about privacy and security. Governments and corporations must establish stringent regulations to protect user information from misuse or exploitation. Additionally, biases in AI models can lead to discriminatory outcomes, necessitating fair and inclusive development practices.

Transparency is another critical factor. Users should understand how AI-driven decisions are made, particularly in sensitive areas like healthcare and criminal justice. Ethical AI implementation requires collaboration between policymakers, technologists, and the public to ensure that advancements benefit society as a whole.

The Future of AI: A Balance Between Progress and Ethics

The importance of AI in society cannot be understated. It enhances productivity, improves decision-making, and solves complex problems across industries. However, its responsible use is paramount to prevent unintended consequences.

As AI continues to evolve, collaboration between academia, businesses, and governments is necessary to establish ethical guidelines and maximize its benefits. The goal should not be to replace human intelligence but to amplify it, fostering innovation while ensuring accountability.

Ultimately, AI is more than just a tool - it is a transformative force shaping the future. Harnessing its potential wisely will determine whether it serves as a steppingstone toward progress or a challenge requiring careful navigation.

Disclaimer: The whole text was generated by Copilot 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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#️⃣Software Engineering: Mea Culpa (Part VIII: A Look Beyond)

Software Engineering Series

With AI on the verge, blogging and bloggers can easily become obsolete. Why bother navigating through the many blogs to get a broader perspective when the same can be obtained with AI? Just type in a prompt of the type "write a blogpost of 600 words on the importance of AI in society" and Copilot or any other similar AI agent will provide you an answer that may look much better than the first draft of most of the bloggers out there! It doesn't matter whether the text follows a well-articulated idea, a personal perspective or something creative! One gets an acceptable answer with a minimum of effort and that's what matters for many.

The results tend to increase in complexity the more models are assembled together, respectively the more uncontrolled are the experiments. Moreover, solutions that tend to work aren't necessarily optimal. Machines can't offer instant enlightenment or anything close to it. Though they have an incomparable processing power of retrieval, association, aggregation, segregation and/or iteration, which coupled with the vast amount of data, information and knowledge can generate anything in just a matter of seconds. Probably, the only area in which humans can compete with machines is creativity and wisdom, though how many will be able to leverage these at scale? Probably, machines have some characteristics that can be associated with these intrinsic human characteristics, though usually more likely the brute computational power will prevail.

At Microsoft Build, Satya Nadella mentioned that foundry encompasses already more than 1900 supported models. In theory, one can still evaluate and test such models adequately. What will happen when the scale increases with a few orders of magnitude? What will happen when for each person there are one or more personalized AI models? AI can help in many areas by generating and evaluating rapidly many plausible alternatives, though as soon the models deal with some kind of processing randomization, the chances for errors increase exponentially (at least in theory).

It's enough for one or more hallucinations or other unexpected behavior to lead to more unexpected behavior. No matter how well a model was tested, as long as there's no stable predictable mathematical model behind it, the chances for something to go wrong increase with the number of inputs, parameters, uses, or changes of context the model deals with. Unfortunately, all these aspects are seldom documented. It's not like using a formula and you know that given a set of inputs and operations, the result is the same. The evolving nature of such models makes them unpredictable in the long term. Therefore, there must always be a way to observe the changes occurring in models.

One of the important questions is how many errors can we afford in such models? How long it takes until errors impact each other to create effects comparable with a tornado. And what if the tornado increases in magnitude to the degree that it wrecks everything that crosses its path? What if multiple tornadoes join forces? How many tornadoes can destroy a field, a country or a continent? How many or big must be the tornadoes to trigger a warning?

Science-Fiction authors love to create apocalyptic scenarios, and all happens in just a few steps, respectively chapters. In nature, usually it takes many orders of magnitude to generate unpredictable behavior. But, as nature often reveals, unpredictable behavior does happen, probably more often than we expect and wish for. The more we are poking the bear, the higher the chances for something unexpected to happen! Do we really want this? What will be the price we must pay for progress?

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🧭Business Intelligence: Perspectives (Part XXX: The Data Science Connection)

Business Intelligence Series

Data Science is a collection of quantitative and qualitative methods, respectively techniques, algorithms, principles, processes and technologies used to analyze, and process amounts of raw and aggregated data to extract information or knowledge it contains. Its theoretical basis is rooted within mathematics, mainly statistics, computer science and domain expertise, though it can include further aspects related to communication, management, sociology, ecology, cybernetics, and probably many other fields, as there’s enough space for experimentation and translation of knowledge from one field to another.  

The aim of Data Science is to extract valuable insights from data to support decision-making, problem-solving, drive innovation and probably it can achieve more in time. Reading in between the lines, Data Science sounds like a superhero that can solve all the problems existing out there, which frankly is too beautiful to be true! In theory everything is possible, when in practice there are many hard limitations! Given any amount of data, the knowledge that can be obtained from it can be limited by many factors - the degree to which the data, processes and models built reflect reality, and there can be many levels of approximation, respectively the degree to which such data can be collected consistently. 

Moreover, even if the theoretical basis seems sound, the data, information or knowledge which is not available can be the important missing link in making any sensible progress toward the goals set in Data Science projects. In some cases, one might be aware of what's missing, though for the data scientist not having the required domain knowledge, this can be a hard limit! This gap can be probably bridged with sensemaking, exploration and experimentation approaches, especially by applying models from other domains, though there are no guarantees ahead!

AI can help in this direction by utilizing its capacity to explore fast ideas or models. However, it's questionable how much the models built with AI can be further used if one can't build mechanistical mental models of the processes reflected in the data. It's like devising an algorithm for winning at lottery small amounts, though investing more money in the algorithm doesn't automatically imply greater wins. Even if occasionally the performance is improved, it's questionable how much it can be leveraged for each utilization. Statistics has its utility when one studies data in aggregation and can predict average behavior. It can’t be used to predict the occurrence of events with a high precision. Think how hard the prediction of earthquakes or extreme weather is by just looking at a pile of data reflecting what’s happening only in a certain zone!

In theory, the more data one has from different geographical areas or organizations, the more robust the models can become. However, no two geographies, respectively no two organizations are alike: business models, the people, the events and other aspects make global models less applicable to local context. Frankly, one has more chances of progress if a model is obtained by having a local scope and then attempting to leverage the respective model for a broader scope. Even then, there can be differences between the behavior or phenomena at micro, respectively at macro level (see the law of physics). 

This doesn’t mean that Data Science or AI related knowledge is useless. The knowledge accumulated by applying various techniques, models and programming languages in problem-solving can be more valuable than the results obtained! Experimentation is a must for organizations to innovate, to extend their knowledge base. It’s also questionable how much of the respective knowledge can be retained and put to good use. In the end, each organization must determine this by itself!

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🧮ERP: Implementations (Part XIV: A Never-Ending Story)

ERP Implementations Series

An ERP implementation is occasionally considered as a one-time endeavor after which an organization will live happily ever after. In an ideal world that would be true, though the work never stops – things that were carved out from the implementation, optimizations, new features, new regulations, new requirements, integration with other systems, etc. An implementation is thus just the beginning from what it comes and it's essential to get the foundation right – and that’s the purpose of the ERP implementation – provide a foundation on which something bigger and solid can be erected. 

No matter how well an ERP implementation is managed and executed, respectively how well people work towards the same goals, there’s always something forgotten or carved out from the initial project. Usually, the casual suspects are the integrations with other systems, though there can be also minor or even bigger features that are planned to be addressed later, if the implementation hasn’t consumed already all the financial resources available, as it's usually the case. Some of the topics can be addressed as Change Requests or consolidated on projects of their own. 

Even simple integrations can become complex when the processes are poorly designed, and that typically happens more often than people think. It’s not necessarily about the lack of skillset or about the technologies used, but about the degree to which the processes can work in a loosely coupled interconnected manner. Even unidirectional integrations can raise challenges, though everything increases in complexity when the flow of data is bidirectional. Moreover, the complexity increases with each system added to the overall architecture. 

Like a sculpture’s manual creation, processes in an ERP implementation form a skeleton that needs chiseling and smoothing until the form reaches the desired optimized shape. However, optimization is not a one-time attempt but a continuous work of exploring what is achievable, what works, what is optimal. Sometimes optimization is an exact science, while other times it’s about (scientifical) experimentation in which theory, ideas and investments are put to good use. However, experimentation tends to be expensive at least in terms of time and effort, and probably these are the main reasons why some organizations don’t even attempt that – or maybe it’s just laziness, pure indifference or self-preservation. In fact, why change something that already works?

Typically, software manufacturers make available new releases on a periodic basis as part of their planning for growth and of attracting more businesses. Each release that touches used functionality typically needs proper evaluation, testing and whatever organizations consider as important as part of the release management process. Ideally, everything should go smoothly though life never ceases to surprise and even a minor release can have an important impact when earlier critical functionality stopped working. Test automation and other practices can make an important difference for organizations, though these require additional effort and investments that usually pay off when done right. 

Regulations and other similar requirements must be addressed as they can involve penalties or other risks that are usually worth avoiding. Ideally such requirements should be supported by design, though even then a certain volume of work is involved. Moreover, the business context can change unexpectedly, and further requirements need to be considered eventually. 

The work on an ERP system and the infrastructure built around it is a never-ending story. Therefore, organizations must have not only the resources for the initial project, but also what comes after that. Of course, some work can be performed manually, some requirements can be delayed, some risks can be assumed, though the value of an ERP system increases with its extended usage, at least in theory. 

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🧩IT: Short Quotes Used in Various Posts

Short quotes used in the various posts:

"A problem well stated is a problem half solved." (Charles F Kettering): Approaching a Query

"An army of principles can penetrate where an army of soldiers cannot." (Thomas Paine): Guiding Principles

"Architecture starts when you carefully put two bricks together." (Ludwig Mies van der Rohe): Guiding Principles

"Data quality requires certain level of sophistication within a company to even understand that it’s a problem." (Colleen Graham): [Who Messed with My Data?]

"Errors, like straws, upon the surface flow;
He who would search for pearls must dive below." (John Dryden): [Who Messed with My Data?]

"Everything should be made as simple as possible, but not simpler." (Albert Einstein): Facts, Principles and Practices

"For every complex problem there is an answer that is clear, simple, and wrong." (Henry L Mencken):  [Who Messed with My Data?]

"I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail." (Abraham Maslow): [Who Messed with My Data?]

"In preparing for battle I have always found that plans are useless, but planning is indispensable." (Eisenhower quoted by Nixon): Planning Correctly Misunderstood

"It's a bad plan that admits of no modification." (Publilius Syrus): Planning Correctly Misunderstood

"Keep it simple, stupid" (aka KISS): Guiding Principles, Facts, Principles and Practices, Simple, but not that Simple

"Management is doing things right […]" (Peter Drucker): Guiding Principles

"No plan ever survived contact with the enemy." (Carl von Clausewitz): Planning Correctly Misunderstood

"Obey the principles without being bound by them." (Bruce Lee): Guiding Principles

"Students are often able to use algorithms to solve numerical problems without completely understanding the underlying scientific concept." (Eric Mazur): [Who Messed with My Data?]

"The ability to simplify means to eliminate the unnecessary so that the necessary may speak." (Hans Hofmann): Facts, Principles and Practices

"The enemy of a good plan is the dream of a perfect plan." (Carl von Clausewitz): Planning Correctly Misunderstood

"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect" (George Lewes, "Style in Literature"): Designing for Simplicity

"The weakest spot in a good defense is designed to fail." (Mark Lawrence): [Who Messed with My Data?]

"To err is human; to try to prevent recurrence of error is science." (Anon): [Who Messed with My Data?

#️⃣Software Engineering: Programming (Part XVI: The Software Quality Perspective and AI)

Software Engineering Series

Organizations tend to complain about poor software quality developed in-house, by consultancy companies or third parties, without doing much in this direction. Unfortunately, this agrees with the bigger picture reflected by the quality standards adopted by organizations - people talk and complain about them, though they aren’t that eager to include them in the various strategies, or even if they are considered, they are seldom enforced adequately!

Moreover, even if quality standards are adopted, and a lot of effort may be spent in this direction (as everybody has strong opinions and there are many exceptions), as projects progress, all the good intentions come to an end, the rules fading on the way either because are too strict, too general, aren’t adequately prioritized or communicated, or there’s no time to implement (all of) them. This applies in general to programming and to the domains that revolve around data – Business Intelligence, Data Analytics or Data Science.

The volume of good quality code and deliverables is not only a reflection of an organization’s maturity in dealing with best practices but also of its maturity in handling technical debt, Project Management, software and data quality challenges. All these aspects are strongly related to each other and therefore require a systemic approach rather than focusing on the issues locally. The systemic approach allows organizations to bridge the gaps between business areas, teams, projects and any other areas of focus.

There are many questionable studies on the effect of methodologies on software quality and data issues, proclaiming that one methodology is better than the other in addressing the multifold aspects of software quality. Besides methodologies, some studies attempt to correlate quality with organizations’ size, management or programmers’ experience, the size of software, or whatever characteristic might seem to affect quality.

Bad code is written independently of companies’ size or programmer's experience, management or organization’s maturity. Bad code doesn’t necessarily happen all at once, but it can depend on circumstances, repetitive team, requirements and code changes. There are decisions and actions that sooner or later can affect the overall outcome negatively.

Rewriting the code from scratch might look like an approachable measure though it’s seldom the cost-effective solution. Allocating resources for refactoring is usually a better approach, though this tends to increase considerably the cost of projects, and organizations might be tempted to face the risks, whatever they might be. Independently of the approaches used, sooner or later the complexity of projects, requirements or code tends to kick back.

There are many voices arguing that AI will help in addressing the problems of software development, quality assurance and probably other areas. It’s questionable how much AI will help to address the gaps, non-concordances and other mistakes in requirements, and how it will develop quality code when it has basic "understanding" issues. Even if step by step all current issues revolving around AI will be fixed, it will take time and multiple iterations until meaningful progress will be made.

At least for now, AI tools like Copilot or ChatGPT can be used for learning a programming language or framework through predefined or ad-hoc prompts. Probably, it can be used also to identify deviations from best practices or other norms in scope. This doesn’t mean that AI will replace for now code reviews, testing and other practices used in assuring the quality of software, but it can be used as an additional method to check for what was eventually missed in the other methods.

AI may also have hidden gems that when discovered, polished and sized, may have a qualitative impact on software development and software. Only time will tell what’s possible and achievable.

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🏭🗒️Microsoft Fabric: Microsoft Fabric [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 8-Dec-2024

Microsoft Fabric 

• {goal}complete (end-to-end) analytics platform [6]
• {characteristic} unified
• {objective} provides a single, integrated environment for all the organization
• {benefit} data professionals and the business users can collaborate on data projects [5] and solutions
• {characteristic}serverless SaaS model (aka SaaS-ified)
• {objective} provisioned automatically with the tenant [6]
• {objective} highly scalable [5]
• {objective} cost-effectiveness [5]
• {objective} accessible 
• ⇐ from anywhere with an internet connection [5]
• {objective} continuous updates
• ⇐ provided by Microsoft
• {objective} continuous maintenance 
• ⇐ provided by Microsoft
• provides a set of integrated services that enable to ingest, store, process, and analyze data in a single environment [5]
• {objective} secure
• {objective} governed
• {goal} lake-centric
• {characteristic} OneLake-based
• all workloads automatically store their data in the OneLake workspace folders [6]
• all the data is organized in an intuitive hierarchical namespace [6]
• data is automatically indexed [6]
• provides a set of features 
• discovery
• MIP labels
• lineage
• PII scans
• sharing
• governance
• compliance
• {characteristic} one copy
• available for all computes 
• all compute engines store their data automatically in OneLake
• ⇐ the data is stored in a (single) common format
• ⇐ delta parquet file format
• open standards format
• the storage format for all tabular data in Microsoft Fabric 
• ⇐ the data is directly accessible by all the engines [6]
• ⇐ no import/export needed
• all compute engines are fully optimized to work with Delta Parquet as their native format [6]
• a shared universal security model is enforced across all the engines [6]
• {characteristic} open at every tier
• {goal} empowering
• {characteristic} intuitive
• {characteristic} built into M365
• {characteristic} insight to action
• {goal} AI-powered
• {characteristic} Copilot accelerated 
• {characteristic} ChatGPT enabled
• {characteristic} AI-driven insights
•  complete analytics platform
• addresses the needs of all data professionals and business users who target harnessing the value of data 
• {feature} scales automatically
• the system automatically allocates an appropriate number of compute resources based on the job size
• the cost is proportional to total resource consumption, rather than size of cluster or number of resources allocated 
•  jobs in general complete faster (and usually, at less overall cost)
• ⇒ not need to specify cluster sizes
• natively supports 
• Spark
• data science
• log-analytics
• real-time ingestion and messaging
• alerting
• data pipelines
• Power BI reporting 
• interoperability with third-party services 
• from other vendors that support the same open 
• data virtualization mechanisms 
• {feature} mirroring [notes]
• {feature} shortcuts [notes]
• allow users to reference data without copying it
• {benefit} make other domain data available locally without the need for copying data
• {feature} tenant (aka Microsoft Fabric tenant, MF tenant)
• a single instance of Fabric for an organization that is aligned with a Microsoft Entra ID
• can contain any number of workspaces
• {feature} workspaces
• {definition} a collection of items that brings together different functionality in a single environment designed for collaboration
• associated with a domain [3]
• {feature} domains [notes]
• {definition} a way of logically grouping together data in an organization that is relevant to a particular area or field [1]
• subdomains
• a way for fine tuning the logical grouping data under a domain [1]
• subdivisions of a domain

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Resources:
[1] Microsoft Learn (2023) Administer Microsoft Fabric [link]
[2] Microsoft Learn: Fabric (2024) Governance overview and guidance [link]
[3] Microsoft Learn: Fabric (2023) Fabric domains [link]
[4] Establishing Data Mesh architectural pattern with Domains and OneLake on Microsoft Fabric, by Maheswaran Arunachalam [link]
[5] Microsoft Learn: Fabric (2024) Introduction to end-to-end analytics using Microsoft Fabric [link]
[6] Microsoft Fabric (2024) Fabric Analyst in a Day [course notes]

Resources:

[R1] Microsoft Learn (2025) Fabric: What's new in Microsoft Fabric? [link]

Acronyms:
API - Application Programming Interface
M365 - Microsoft 365
MF - Microsoft Fabric
PII - Personal Identification Information
SaaS - software-as-a-service

#️⃣Software Engineering: Mea Culpa (Part V: All-Knowing Developers are Back in Demand?)

Software Engineering Series

I’ve been reading many job descriptions lately related to my experience and curiously or not I observed that many organizations look for developers with Microsoft Dynamics experience in the CRM, respectively Finance and Operations (F&O) and Business Central (BC) areas. It’s a good sign that the adoption of Microsoft solutions for CRM and ERP increases, especially when one considers the progress made in the BI and AI areas with the introduction of Microsoft Fabric, which gives Microsoft a considerable boost. Conversely, it seems that the "developers are good for everything" syntagma is back, at least from what one reads in job descriptions. 

Of course, it’s useful to have an inhouse developer who can address all the aspects of an implementation, though that’s a lot to ask considering the different non-programming areas that need to be addressed. It’s true that a developer with experience can handle Requirements, Data and Process Management, respectively Data Migrations and Business Intelligence topics, though if one considers that each of the topics can easily become a full-time job before, during and post-project implementations. I’ve been there and I (hopefully) know that the jobs imply. Even if an experienced programmer can easily handle the different aspects, there will be also times when all the topics combined will be too much for a person!

It's not a novelty that job descriptions are treated like Christmas lists, but it’s difficult to differentiate between essential and nonessential skillset. I read many jobs descriptions lately in which among a huge list of demands, one of the requirements is to program in the F&O framework, sign that D365 programmers are in high demand. I worked for many years as programmer and Software Engineer, respectively in the BI area, where SQL and non-SQL code is needed. Even if I can understand the code in F&O, does it make sense to learn now to program in X++ and the whole framework? 

It's never too late to learn new tricks, respectively another programming language and/or framework. It even helps to provide better solutions in usual areas, though frankly I would invest my time in other areas, and AI-related topics like AI prompting or Data Science seem to be more interesting on the long run, especially when they are already in demand!

There seems to be a tendency for Data Science professionals to do everything, building their own solutions, ignoring the experience accumulated respectively the data models built in BI and Data Analytics areas, as if the topics and data models are unrelated! It’s also true that AI-modeling comes with its own requirements in what concerns data modeling (e.g. translating non-numeric to numeric values), though I believe that common ground can be found!

Similarly, the notebook-based programming seems to replicate logic in each solution, which occasionally makes sense, though personally I wouldn’t recommend it as practice! The other day, I was looking at code developed in Python to mimic the joining of tables, when a view with the same could be easier (re)used, maintained, read and probably more efficient, even if different engines will be used. It will be interesting to see how the mix of spaghetti solutions will evolve over time. There are developers already complaining of the number of objects used in the process by building logic for each layer from the medallion architecture! Even if it makes sense from architectural considerations, it will become a nightmare in time.

One can wonder also about nomenclature used – Data Engineer or Prompt Engineering for the simple manipulation of data between structures in data transformations, respectively for structuring the prompts for AI. I believe that engineering involves more than this, no matter the context! 

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🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part IV: Making It in the Statistics)

Business Intelligence Series

Various sources (e.g., [1], [2], [3]) advance the failure rates for data projects somewhere between 70% and 85%, rates which are a bit higher than the failure of standard projects estimated at 60-75% but not by much. This means that only 2-3 out of 10 projects will succeed and that’s another reason to plan for failure, respectively embrace the failure. 

Unfortunately, the statistics advanced on project failure have no solid fundament and should be regarded with circumspection as long the methodology and information about the population used for the estimates aren’t shared, though they do reflect an important point – many data projects do fail! It would be foolish to think that your project will not fail just because you’re a big company, and you have the best resources, and you have a proven rate of success, and you took all the precautions for the project not to fail.

Usually at the end of a project the team meets together to document the lessons learned in the hope that the next projects will benefit from them. The team did learn something, though as the practice shows even if the team managed to avoid some issues, other issues will impact the next similar project, leading to similar variances. One can summarize this as "on the average the impact of new issues and avoided known issues tends to zero out" or "on average, the plusses and minuses balance each other across projects". It’s probably a question of focus – if organizations focus too much on certain aspects, other aspects are ignored and/or unseen. 

So, your first data project will more likely fail. The question is: what do you do about it? It’s important to be aware of why projects and data projects fail, though starting to consider and monitor each possible issue can prove to be ineffective. One can, however, create a risk register from the list and estimate the rates for each of the potential failures, respectively focus on only the top 3-5 which have the highest risk. Of course, one should reevaluate the estimates on a regular basis though that’s Risk Management 101. 

Besides this, one should focus on how the team can make the project succeed. When adopting a technology, methodology or set of processes, it’s recommended to start with a proof-of-concept (PoC). To make the PoC a helpful experience it’s probably important to start with a topic that’s not too big to handle, but that also involves some complexity that would allow the organization to evaluate the targeted set of tools and technologies. It can also be a topic for which other organizations have made important progress, respectively succeed. The temptation is big to approach the most stringent issues in the organization, respectively to build something big that can have an enormous impact for the organization. Jumping too soon into such topics can just increase the chances of failure. 

One can also formulate the goals, objectives and further requirements in a form that allows the organization to build upon them even if the project fails. A PoC is about learning, building a foundation, doing the groundwork, exploring, mapping the unknown, and identifying what's still missing to make progress, respectively closing the full circle. A PoC is less about overachievement and a big impact, which can happen, though is a consequence of the good work done in the PoC. 

The bottom line, no matter whether you succeed or fail, once you start a project, you’ll still make it in the statistics! More important is what you’ve learnt after the first data project, respectively how you can use the respective knowledge in further projects to make a difference!

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References:
[1] Harvard Business Review (2023) Keep Your AI Projects on Track, by Iavor Bojinov (link)
[2] Cognilytica (2023) The Shocking Truth: 70-80% of AI Projects Fail! (link)
[3] VentureBeat (2019) Why do 87% of data science projects never make it into production? (link)

🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part III: Failure through the Looking Glass)

Business Intelligence Series

There’s a huge volume of material available on project failure – resources that document why individual projects failed, why in general projects fail, why project members, managers and/or executives think projects fail, and there seems to be no other more rewarding activity at the end of a project than to theorize why a project failed, the topic culminating occasionally with the blaming game. Success may generate applause, though it's failure that attracts and stirs the most waves (irony, disapproval, and other similar behavior) and everybody seems to be an expert after the consumed endeavor. 

The mere definition of a project failure – not fulfilling project’s objectives within the set budget and timeframe - is a misnomer because budgets and timelines are estimated based on the information available at the beginning of the project, the amount of uncertainty for many projects being considerable, and data projects are no exceptions from it. The higher the uncertainty the less probable are the two estimates. Even simple projects can reveal uncertainty especially when the broader context of the projects is considered. 

Even if it’s not a common practice, one way to cope with uncertainty is to add a tolerance for the estimates, though even this practice probably will not always accommodate the full extent of the unknown as the tolerances are usually small. The general expectation is to have an accurate and precise landing, which for big or exploratory projects is seldom possible!

Moreover, the assumptions under which the estimates hold are easily invalidated in praxis – resources’ availability, first time right, executive’s support to set priorities, requirements’ quality, technologies’ maturity, etc. If one looks beyond the reasons why projects fail in general, quite often the issues are more organizational than technological, the lack of knowledge and experience being some of the factors. 

Conversely, many projects will not get approved if the estimates don’t look positive, and therefore people are pressured in one way or another to make the numbers fit the expectations. Some projects, given their importance, need to be done even if the numbers don’t look good or can’t be quantified correctly. Other projects represent people’s subsistence on the job, respectively people's self-occupation to create motion, though they can occasionally have also a positive impact for the organizations. These kinds of aspects almost never make it in statistics or surveys. Neither do the big issues people are afraid to talk about. Where to consider that in the light of politics and office’s grapevine the facts get distorted!

Data projects reflect all the symptoms of failure projects have in general, though when words like AI, Statistics or Machine Learning are used, the chances for failure are even higher given that the respective fields require a higher level of expertise, the appropriate use of technologies and adherence to the scientific process for the results to be valid. If projects can benefit from general recipes, respectively established procedures and methods, their range of applicability decreases when the mentioned areas are involved. 

Many data projects have an exploratory nature – seeing what’s possible - and therefore a considerable percentage will not reach production. Moreover, even those that reach that far might arrive to be stopped or discarded sooner or later if they don’t deliver the expected value, and probably many of the models created in the process are biased, irrelevant, or incorrectly apply the theory. Where to add that the mere use of tools and algorithms is not Data Science or Data Analysis. 

The challenge for many data projects is to identify which Project Management (PM) best practices to consider. Following all or no practices at all just increases the risks of failure!

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