🤖〽️Prompt Engineering: Copilot Unabridged (Part 53: The Future of Business Intelligence - Will AI Make It Obsolete?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI may start making business intelligence obsolete"

Introduction

Business intelligence (BI) has long been the backbone of data-driven decision-making, helping organizations analyze trends, optimize operations, and gain competitive advantages. However, as artificial intelligence (AI) continues to evolve, many wonder whether traditional BI tools and methodologies will become obsolete. AI’s ability to process vast amounts of data, generate insights autonomously, and adapt in real time is reshaping the landscape of business analytics. But does this mean BI will disappear entirely, or will it simply evolve?

The Shift from Traditional BI to AI-Driven Analytics

Traditional BI relies on structured data, dashboards, and human interpretation to extract meaningful insights. Analysts and business leaders use BI tools to generate reports, visualize trends, and make informed decisions. However, AI is introducing a new paradigm - one where data analysis is automated, predictive, and adaptive.

AI-driven analytics can:

• Process unstructured data from sources like social media, emails, and customer interactions.
• Identify patterns and correlations that human analysts might overlook.
• Provide real-time insights without requiring manual report generation.
• Predict future trends using machine learning models.

These capabilities suggest that AI is not just enhancing BI - it is fundamentally transforming it.

Why AI Might Replace Traditional BI Tools

Several factors indicate that AI could make traditional BI tools obsolete:

• Automation of Data Analysis: AI eliminates the need for manual data processing, allowing businesses to generate insights instantly. Traditional BI tools require human intervention to clean, structure, and interpret data, whereas AI can automate these processes.
• Predictive and Prescriptive Analytics: While BI focuses on historical data, AI-driven analytics predict future trends and prescribe actions. Businesses can move beyond reactive decision-making and adopt proactive strategies based on AI-generated forecasts.
• Natural Language Processing (NLP) for Data Queries: AI-powered systems enable users to ask questions in natural language rather than navigating complex dashboards. This makes data analysis more accessible to non-technical users, reducing reliance on BI specialists.
• Continuous Learning and Adaptation: AI models improve over time, refining their predictions and insights based on new data. Traditional BI tools require manual updates and adjustments, whereas AI evolves autonomously.

Challenges and Limitations of AI in Business Intelligence

Despite AI’s advancements, there are reasons why BI may not become entirely obsolete:

• Data Governance and Compliance: AI-driven analytics must adhere to strict regulations regarding data privacy and security. Businesses need human oversight to ensure compliance with laws such as GDPR.
• Interpretability and Trust: AI-generated insights can sometimes be opaque, making it difficult for business leaders to trust automated recommendations. Traditional BI tools provide transparency in data analysis.
• Human Expertise in Decision-Making: AI can generate insights, but human intuition and strategic thinking remain essential for complex business decisions. AI should complement, not replace, human expertise.

The Future: AI-Augmented Business Intelligence

Rather than making BI obsolete, AI is likely to augment and enhance business intelligence. The future of BI will involve AI-powered automation, predictive analytics, and real-time decision-making, but human oversight will remain crucial.

Organizations that embrace AI-driven BI will gain a competitive edge, leveraging automation while maintaining strategic control. The key is to integrate AI as a collaborative tool rather than a complete replacement for traditional BI methodologies.

Conclusion

AI is revolutionizing business intelligence, but it is unlikely to make it entirely obsolete. Instead, BI will evolve into a more automated, predictive, and adaptive system powered by AI. Businesses that integrate AI-driven analytics will benefit from faster insights, improved decision-making, and enhanced efficiency.

The future of AI is not about replacement - it’s about transformation. AI will redefine how businesses analyze data, but human expertise will remain essential in shaping strategic decisions.

Disclaimer: The whole text was generated by Copilot (under Windows 10) 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 46: Understanding AI Governance - Balancing Innovation and Responsibility)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI governance works"

Introduction

Artificial intelligence (AI) is rapidly transforming industries, societies, and economies worldwide. Its immense potential has led to groundbreaking advancements in healthcare, finance, transportation, and more. However, with these capabilities come risks - bias in AI models, privacy concerns, and misuse of AI-powered systems. To address these challenges, AI governance has emerged as a critical framework for ensuring responsible AI development and deployment.

What is AI Governance?

AI governance refers to the policies, laws, regulations, and ethical frameworks that guide AI development and usage. It encompasses a broad spectrum of considerations, including data privacy, security, accountability, transparency, and fairness. The goal is to balance the rapid advancement of AI technology with societal norms and ethical principles.

Governance mechanisms differ across regions and industries, but they typically involve collaboration between governments, tech companies, academic researchers, and civil society groups. The underlying challenge in AI governance is ensuring AI systems benefit humanity while mitigating risks such as bias, discrimination, and security vulnerabilities.

Key Principles of AI Governance

Several fundamental principles shape AI governance frameworks across the globe:
Transparency: AI systems should be understandable and explainable. Black-box models, where the decision-making process remains obscure, can lead to concerns regarding bias and accountability.

Explainability helps foster trust among users and regulators.

• Accountability: Organizations developing and deploying AI must take responsibility for their systems’ behavior. This includes ensuring ethical use, addressing unintended consequences, and establishing mechanisms for legal recourse when AI causes harm.
• Privacy and Data Protection: AI systems rely on vast amounts of data, raising concerns about privacy breaches and misuse. Strong governance frameworks require compliance with data protection laws such as GDPR in Europe, ensuring users have control over their personal information.
• Bias and Fairness: AI can inherit biases from training data, leading to discriminatory outcomes. Ethical AI governance emphasizes fairness, reducing disparities in AI-driven decisions affecting hiring, law enforcement, healthcare, and financial services.
• Security and Safety: As AI applications expand, cybersecurity threats, deepfake technology, and AI-driven autonomous weapons become pressing concerns. Governance frameworks must enforce security protocols to prevent malicious use of AI systems.

Global AI Governance Initiatives

Different nations and organizations are approaching AI governance in diverse ways:

• European Union (EU): The EU’s Artificial Intelligence Act seeks to regulate AI based on risk categories. High-risk applications, such as biometric identification and critical infrastructure management, face stricter requirements, while lower-risk systems have minimal oversight.
• United States: The U.S. government has taken a more hands-off approach, emphasizing AI innovation while promoting ethical guidelines through the National Institute of Standards and Technology (NIST) AI Risk Management Framework. States such as California have begun implementing stricter AI policies, particularly regarding data privacy.
• China: China has introduced comprehensive AI laws emphasizing security, data control, and algorithmic regulation. The country focuses on AI governance that aligns with state interests while fostering technological leadership in AI innovation.
• United Nations (UN) & Industry Collaborations: The UNESCO AI Ethics Framework and initiatives like the Partnership on AI bring together global stakeholders to promote responsible AI development. Large tech firms, including Microsoft and Google, have also created internal AI governance structures to align their AI systems with ethical standards.

Challenges in AI Governance

While governance frameworks are evolving, challenges remain:

• Regulatory Complexity: AI development is global, but governance laws vary widely, making international collaboration essential yet difficult.
• Balancing Innovation and Regulation: Striking the right balance between enabling innovation and imposing regulations is crucial to avoid stifling progress.
• Enforcement: Ensuring companies adhere to AI regulations requires oversight and accountability mechanisms, which can be difficult to implement.

The Future of AI Governance

AI governance will continue to evolve as AI capabilities expand. Ethical AI development, global cooperation, and transparent policies will play a crucial role in shaping a future where AI benefits society responsibly. Initiatives promoting AI auditing, fairness assessments, and bias reduction will become integral to AI governance frameworks.

Governance is not about restricting AI; rather, it’s about steering its trajectory toward ethical, secure, and beneficial use. By integrating ethics, accountability, and oversight into AI development, we can maximize AI’s potential while mitigating risks, ensuring its contributions to humanity remain positive.

Disclaimer: The whole text was generated by Copilot (under Windows 10) 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 40: How Disruptive Is AI as a Technology?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how disruptive is AI as technology"

Introduction

Artificial Intelligence (AI) is one of the most transformative and disruptive technologies of the modern era. It is reshaping industries, automating processes, and redefining human interactions with machines. While AI brings efficiency, innovation, and economic growth, it also raises concerns about job displacement, ethical dilemmas, and security risks. Understanding AI’s disruptive nature is crucial for navigating its impact on society.

1. AI’s Disruption Across Industries

AI is revolutionizing multiple sectors, leading to both opportunities and challenges:

• Healthcare: AI-powered diagnostics improve accuracy, but automation may reduce the need for human radiologists and pathologists.
• Finance: AI-driven trading algorithms optimize investments, yet they also introduce risks of market instability.
• Retail: AI personalizes shopping experiences, but automation threatens traditional retail jobs.
• Manufacturing: AI-powered robotics enhance efficiency, yet they replace human labor in factories.

AI’s ability to streamline operations and reduce costs makes it highly disruptive, forcing industries to adapt or risk obsolescence.

2. AI’s Impact on Employment and Workforce Dynamics

One of AI’s most significant disruptions is its effect on employment. While AI creates new job opportunities in data science, AI development, and cybersecurity, it also eliminates traditional roles in sectors like customer service, transportation, and manufacturing.

• Automation replaces repetitive tasks, reducing demand for human workers.
• AI-driven hiring processes change recruitment dynamics, making job searches more competitive.
• Reskilling becomes essential, as workers must adapt to AI-integrated industries.

Governments and businesses must invest in workforce retraining to mitigate AI-induced unemployment.

3. AI’s Ethical and Security Challenges

AI’s disruptive nature extends beyond economics - it raises ethical concerns and security risks:

• Bias in AI algorithms: AI models trained on biased data can reinforce discrimination in hiring, healthcare, and law enforcement.
• Privacy concerns: AI-driven surveillance and data collection raise questions about personal security.
• Cybersecurity threats: AI-powered hacking tools exploit vulnerabilities, making digital security more complex.

Without strong ethical guidelines and security measures, AI’s disruption could lead to unintended consequences.

4. AI’s Role in Decision-Making and Governance

AI is increasingly used in government policies, legal judgments, and corporate strategies, raising concerns about autonomy and accountability:

• AI-driven governance: Governments use AI for predictive policing and policy analysis, but transparency is crucial.
• AI in legal systems: AI assists in legal research, yet its role in judicial decisions remains controversial.
• AI-powered corporate decision-making: Businesses rely on AI for strategic planning, but human oversight is necessary.

Balancing AI automation with human judgment is essential for ethical governance.

Conclusion: AI’s Disruption Requires Responsible Management

AI is undeniably disruptive, reshaping industries, employment, ethics, and governance. While its advancements bring efficiency and innovation, they also introduce challenges that require proactive solutions. Governments, businesses, and researchers must develop ethical AI policies, invest in workforce adaptation, and ensure AI remains a tool for progress rather than an unchecked force.

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 39: How Fast Does Humanity Move in Enforcing Policies to Cope with AI’s Rapid Growth?)

Prompt Engineering Series

Introduction

Artificial Intelligence (AI) is evolving at an unprecedented pace, reshaping industries, governance, and daily life. However, the speed at which AI advances often outpaces policy enforcement, leaving governments and institutions struggling to regulate its impact effectively. While AI’s rapid development demands swift policy responses, humanity’s ability to enforce regulations remains slow and fragmented due to bureaucratic hurdles, ethical dilemmas, and global inconsistencies.

1. The Lag Between AI Innovation and Policy Enforcement

AI’s growth is exponential, but policy enforcement follows a linear and bureaucratic process. Some key reasons for this lag include:

• Complex legislative procedures: Governments require extensive debates, approvals, and revisions before implementing AI regulations.
• Ethical uncertainties: Policymakers struggle to define AI’s ethical boundaries, delaying enforcement.
• Corporate resistance: Tech companies often lobby against strict AI regulations, slowing policy adoption.

This delay creates a regulatory gap, where AI evolves faster than laws can govern its use.

2. Global AI Policy Enforcement: A Fragmented Approach

Different countries enforce AI policies at varying speeds, leading to inconsistent regulations worldwide. Some examples include:

• The European Union (EU): The EU AI Act is one of the most comprehensive AI regulations, but its enforcement is slow due to legal complexities.
• The United States: AI policy enforcement is largely decentralized, with states implementing different regulations.
• China: AI governance is strict, but enforcement focuses on state control rather than ethical concerns.

This fragmented approach makes global AI regulation difficult, as different regions prioritize different aspects of AI governance.

3. The Challenges of AI Policy Enforcement

Even when AI policies are established, enforcement faces several challenges:

• Lack of technical expertise: Policymakers often lack AI knowledge, making enforcement ineffective.
• Corporate non-compliance: Companies may bypass regulations through loopholes or offshore operations.
• Slow adaptation to AI advancements: Policies become outdated quickly as AI evolves.

Without continuous policy updates, AI enforcement remains inefficient and reactive rather than proactive.

4. The Need for Dynamic AI Regulations

To cope with AI’s fast pace, experts propose dynamic AI regulations, where policies evolve alongside technological advancements. Some solutions include:

• AI governance frameworks that adapt to new AI developments.
• International AI cooperation to create unified global standards.
• Automated AI auditing systems to monitor compliance in real time.

By implementing flexible and adaptive policies, humanity can keep pace with AI’s rapid growth.

Conclusion: Humanity Must Accelerate AI Policy Enforcement

While AI evolves rapidly, policy enforcement remains slow and fragmented, creating regulatory gaps that could lead to ethical and security risks. Governments, businesses, and researchers must accelerate AI policy development, ensuring regulations keep pace with technological advancements. Without proactive enforcement, AI’s impact on society could become unmanageable, making responsible AI governance more urgent than ever.

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 38: The Growing Backlog of AI Policies - How Much Needs to Be Done?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how big is the backlog of policies that need to be made" because of AI

Introduction

Artificial Intelligence (AI) is advancing at an unprecedented pace, reshaping industries, governance, and daily life. However, the rapid evolution of AI has outpaced regulatory frameworks, leaving governments, businesses, and institutions struggling to catch up with necessary policies. The backlog of AI-related policies is substantial, covering areas such as ethics, security, employment, privacy, and accountability.

1. The Urgent Need for AI Governance

AI’s widespread adoption has created legal and ethical gaps that require immediate policy intervention. Some of the most pressing areas include:

• AI bias and fairness: Policies must ensure AI systems do not reinforce discrimination.
• Data privacy and security: AI-driven data collection raises concerns about surveillance and personal privacy.
• AI accountability: Governments must define who is responsible when AI makes errors or unethical decisions.
• AI in employment: Regulations must address job displacement and workforce adaptation.

Without clear policies, AI’s impact on society could become unpredictable and potentially harmful.

2. The Backlog of AI Policies Across Industries

Different sectors require specific AI regulations, but many industries are lagging behind in policy development. Some examples include:

• Healthcare: AI-powered diagnostics and treatment recommendations need strict oversight to prevent errors.
• Finance: AI-driven trading algorithms require regulations to prevent market manipulation.
• Education: AI-powered learning tools must be monitored to ensure ethical use and prevent misinformation.
• Law enforcement: AI surveillance and facial recognition technologies raise concerns about civil liberties.

Governments and institutions must accelerate policy development to keep up with AI’s rapid integration into these fields.

3. Global AI Policy Challenges

AI regulation is not uniform across countries, leading to conflicting policies and enforcement difficulties. Some challenges include:

• Different AI ethics standards: Countries have varying perspectives on AI’s role in governance and security.
• Cross-border AI regulations: AI-driven cyber threats and misinformation require international cooperation.
• Corporate AI policies: Businesses must align AI governance with global standards to ensure compliance.

The backlog of AI policies is not just national - it’s global, requiring collaboration between governments, corporations, and researchers.

4. The Role of AI Policy Frameworks

Organizations and governments are working to develop AI policy frameworks, but progress is slow. Some key initiatives include:

• The EU AI Act, which aims to regulate AI applications based on risk levels.
• ISO 42001, an AI management system standard for responsible AI governance.
• Corporate AI policies, guiding ethical AI use within businesses.

Despite these efforts, the backlog of AI policies remains significant, requiring faster implementation and enforcement.

Conclusion: AI Policy Development Must Accelerate

The backlog of AI policies is large and growing, covering ethics, security, employment, privacy, and accountability. Governments, businesses, and institutions must prioritize AI governance, ensuring regulations keep pace with technological advancements. Without proactive policy development, AI’s impact on society could become unmanageable, making responsible AI regulation more urgent than ever.

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 30: The Multiplicity of AI - Expanding Perspectives in Artificial Intelligence)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the multiplicity of AI"

Introduction

Artificial Intelligence (AI) is often discussed in terms of singularity - the idea that AI will reach a point where it surpasses human intelligence and operates autonomously. However, an alternative perspective is multiplicity, which emphasizes AI’s ability to work in diverse, collaborative, and multi-agent systems rather than as a single dominant intelligence. The concept of multiplicity in AI suggests that instead of one all-powerful AI, we will see multiple AI systems interacting, complementing human intelligence, and shaping various aspects of society.

1. What Is AI Multiplicity?

Multiplicity in AI refers to the idea that AI should not be viewed as a singular entity but as a collection of diverse AI systems working together. This approach contrasts with the fear of AI singularity, which suggests that AI could become uncontrollable or surpass human intelligence in a way that threatens humanity. Instead, multiplicity focuses on:

• Collaboration between AI and humans to enhance problem-solving.
• Multiple AI systems working together to improve efficiency and accuracy.
• Diverse AI applications across industries, rather than a single dominant AI.

This perspective encourages distributed intelligence, where AI systems complement each other rather than compete for dominance.

2. AI Multiplicity in Governance and Ethics

The concept of multiplicity is gaining traction in AI governance, where experts argue that AI should be designed to offer multiple perspectives and options rather than a single authoritative answer. Research suggests that AI models often reflect mainstream narratives, limiting diversity in thought and decision-making.

To address this, AI governance can incorporate multiplicity-by-design, ensuring that AI systems:

• Provide multiple viewpoints rather than reinforcing a singular perspective.
• Encourage users to seek additional information beyond AI-generated responses.
• Promote cultural and ideological diversity in AI-generated content.

By integrating multiplicity into AI governance, society can preserve diversity in thought and decision-making while benefiting from AI’s capabilities.

3. AI Multiplicity in Business and Innovation

Multiplicity is also shaping AI’s role in business and innovation, where companies are leveraging multiple AI systems to optimize operations. Instead of relying on a single AI model, businesses are adopting:

• AI-powered recommendation engines that analyze diverse consumer behaviors.
• Multi-agent AI systems that collaborate to improve efficiency.
• AI-driven automation that enhances human decision-making rather than replacing it.

This approach ensures that AI supports human creativity and problem-solving rather than dictating outcomes.

4. AI Multiplicity in Human-AI Collaboration

Rather than replacing human intelligence, AI multiplicity encourages collaborative intelligence, where AI systems assist humans in complex tasks. Examples include:

• AI-assisted medical diagnostics, where multiple AI models analyze patient data for accuracy.
• AI-powered research tools, helping scientists explore multiple hypotheses.
• AI-driven creative platforms, enabling artists and writers to experiment with new ideas.

This collaborative approach ensures that AI enhances human capabilities rather than replacing them.

Conclusion: Embracing AI Multiplicity for a Balanced Future

The future of AI is not about singularity but multiplicity - a world where multiple AI systems interact, collaborate, and support human intelligence. By embracing this perspective, society can ensure AI remains a tool for innovation, diversity, and ethical decision-making rather than an unchecked force.

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 23: How AI Can Be Tamed: Ensuring Responsible Development and Use)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can be tamed"

Introduction

Artificial Intelligence (AI) is advancing at an unprecedented pace, reshaping industries, automating processes, and influencing decision-making. While AI offers immense benefits, its rapid growth raises concerns about ethical risks, bias, security threats, and autonomy. To ensure AI remains a beneficial tool rather than an uncontrollable force, society must take proactive steps to tame AI through regulation, ethical frameworks, and technological safeguards.

1. Establishing Ethical AI Guidelines

One of the most effective ways to tame AI is by implementing ethical frameworks that guide its development and usage. Ethical AI principles should include:

• Transparency: AI systems must be explainable, ensuring users understand how decisions are made.
• Fairness: AI models should be trained on diverse datasets to prevent bias and discrimination.
• Accountability: Developers and organizations must take responsibility for AI-driven decisions.

By embedding ethical considerations into AI development, we can prevent unintended consequences and ensure AI aligns with human values.

2. Regulating AI to Prevent Misuse

Governments and institutions must enforce AI regulations to prevent harmful applications. Key regulatory measures include:

• Data protection laws: Ensuring AI respects privacy and security standards.
• AI auditing requirements: Regular assessments to detect bias and ethical violations.
• Restrictions on autonomous weapons: Preventing AI from making life-or-death decisions without human oversight.

Without proper regulation, AI could be exploited for unethical purposes, making legal frameworks essential for responsible AI governance.

3. Controlling AI’s Energy Consumption

AI requires massive computational power, leading to concerns about energy consumption and environmental impact. To tame AI’s energy demands, researchers are exploring:

• Efficient AI models that reduce processing power without sacrificing performance.
• Renewable energy sources to power AI-driven data centers.
• Optimized algorithms that minimize unnecessary computations.

By making AI more energy-efficient, we can reduce its environmental footprint while maintaining technological progress.

4. Using Blockchain to Enhance AI Security

Blockchain technology offers a potential solution for taming AI’s security risks. By integrating AI with blockchain, we can:

• Ensure data integrity: Blockchain prevents unauthorized modifications to AI training data.
• Enhance transparency: AI decisions can be recorded on a decentralized ledger for accountability.
• Improve security: Blockchain encryption protects AI systems from cyber threats.

Combining AI with blockchain could reduce risks associated with AI manipulation and bias, making AI more trustworthy.

5. Addressing Bias in AI Models

AI systems often inherit biases from their training data, leading to unfair outcomes in hiring, healthcare, and law enforcement. Instead of eliminating bias entirely, researchers suggest controlling bias to achieve fairness. Strategies include:

• Diverse training datasets that represent multiple perspectives.
• Bias detection algorithms that flag discriminatory patterns.
• Human oversight to ensure AI decisions align with ethical standards.

By taming AI bias, we can create more equitable AI systems that serve all communities fairly.

Conclusion: AI Must Be Guided, Not Feared

AI is not inherently dangerous, but without proper oversight, it can amplify risks related to bias, security, and autonomy. By implementing ethical guidelines, regulations, energy-efficient practices, blockchain security, and bias control, society can tame AI and ensure it remains a force for good.

The future of AI depends on how we choose to shape its development - whether as a beneficial tool or an unchecked force.

Disclaimer: The whole text was generated by Copilot at the first attempt.

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🧭🏭Business Intelligence: Microsoft Fabric (Part III: The Metrics Layer) 🆕

Introduction

One of the announcements of this year's Microsoft Fabric Community first conference was the introduction of a metrics layer in Fabric which "allows organizations to create standardized business metrics, that are rooted in measures and are discoverable and intended for reuse" [1]. As it seems, the information content provided at the conference was kept to a minimum given that the feature is still in private preview, though several webcasts start to catch up on the topic (see [2], [4]). Moreover, as part of their show, the Explicit Measures (@PowerBITips) hosts had Carly Newsome as invitee, the manager of the project, who unveiled more details about the project and the feature, details which became the main source for the information below. 

The idea of a metric layer or metric store is not new, data professionals occasionally refer to their structure(s) of metrics as such. The terms gained weight in their modern conception relatively recently in 2021-2022 (see [5], [6], [7], [8], [10]). Within the modern data stack, a metrics layer or metric store is an abstraction layer available between the data store(s) and end users. It allows to centrally define, store, and manage business metrics. Thus, it allows us to standardize and enforce a single source of truth (SSoT), respectively solve several issues existing in the data stacks. As Benn Stancil earlier remarked, the metrics layer is one of the missing pieces from the modern data stack (see [10]).

Microsoft's Solution

Microsoft's business case for metrics layer's implementation is based on three main ideas (1) duplicate measures contribute to poor data quality, (2) complex data models hinder self-service, (3) reduce data silos in Power BI. In Microsoft's conception the metric layer provides several benefits: consistent definitions and descriptions, easy management via management views, searchable and discoverable metrics, respectively assure trust through indicators. 

For this feature's implementation Microsoft introduces a new Fabric Item called a metric set that allows to group several (business) metrics together as part of a mini-model that can be tailored to the needs of a subset of end-users and accessed by them via the standard tools already available. The metric set becomes thus a mini-model. Such mini-models allow to break down and reduce the overall complexity of semantic models, while being easy to evolve and consume. The challenge will become then on how to break down existing and future semantic models into nonoverlapping mini-models, creating in extremis a partition (see the Lego metaphor for data products). The idea of mini-models is not new, [12] advocating the idea of using a Master Model, a technique for creating derivative tabular models based on a single tabular solution.

A (business) metric is a way to elevate the measures from the various semantic models existing in the organization within the mini-model defined by the metric set. A metric can be reused in other fabric artifacts - currently in new reports on the Power BI service, respectively in notebooks by copying the code. Reusing metrics in other measures can mean that one can chain metrics and the changes made will be further propagated downstream. 

The Metrics Layer in Microsoft Fabric (adapted diagram)

Every metric is tied to the original semantic model which allows thus to track how a metric is used across the solutions and, looking forward to Purview, to identify data's lineage. A measure is related to a "table", the source from which the measure came from.

Users' Perspective

The Metrics Layer feature is available in Microsoft Fabric service for Power BI within the Metrics menu element next to Scorecards. One starts by creating a metric set in an existing workspace, an operation which creates the actual artifact, to which the individual metrics are added. To create a metric, a user with build permissions can navigate through the semantic models across different workspaces he/she has access to, pick a measure from one of them and elevate it to a metric, copying in the process its measure's definition and description. In this way the metric will always point back to the measure from the semantic model, while the metrics thus created are considered as a related collection and can be shared around accordingly. 

Once a metric is added to the metric set, one can add in edit mode dimensions to it (e.g. Date, Category, Product Id, etc.). One can then further explore a metric's output and add filters (e.g. concentrate on only one product or category) point from which one can slice-and-dice the data as needed.

There is a panel where one can see where the metric has been used (e.g. in reports, scorecards, and other integrations), when was last time refreshed, respectively how many times was used. Thus, one has the most important information in one place, which is great for developers as well as for the users. Probably, other metadata will be added, such as whether an increase in the metric would be favorable or unfavorable (like in Tableau Pulse, see [13]) or maybe levels of criticality, an unit of measure, or maybe its type - simple metric, performance indicator (PI), result indicator (RI), KPI, KRI etc.

Metrics can be persisted to the OneLake by saving their output to a delta table into the lakehouse. As demonstrated in the presentation(s), with just a copy-paste and a small piece of code one can materialize the data into a lakehouse delta table, from where the data can be reused as needed. Hopefully, the process will be further automated. 

One can consume metrics and metrics sets also in Power BI Desktop, where a new menu element called Metric sets was added under the OneLake data hub, which can be used to connect to a metric set from a Semantic model and select the metrics needed for the project. 

Tapping into the available Power BI solutions is done via an integration feature based on Sempy fabric package, a dataframe for storage and propagation of Power BI metadata which is part of the python-based semantic Link in Fabric [11].

Further Thoughts

When dealing with a new feature, a natural idea comes to mind: what challenges does the feature involve, respectively how can it be misused? Given that the metrics layer can be built within a workspace and that it can tap into the existing measures, this means that one can built on the existing infrastructure. However, this can imply restructuring, refactoring, moving, and testing a lot of code in the process, hopefully with minimal implications for the solutions already available. Whether the process is as simple as imagined is another story. As misusage, in extremis, data professionals might start building everything as metrics, though the danger might come when the data is persisted unnecessarily. 

From a data mesh's perspective, a metric set is associated with a domain, though there will be metrics and data common to multiple domains. Moreover, a mini-model has the potential of becoming a data product. Distributing the logic across multiple workspaces and domains can add further challenges, especially in what concerns the synchronization and implemented of requirements in a way that doesn't lead to bottlenecks. But this is a general challenge for the development team(s). 

The feature will probably suffer further changes until is released in public review (probably by September or the end of the year). I subscribe to other data professionals' opinion that the feature was for long needed and that can have an important impact on the solutions built. 

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Resources:
[1] Microsoft Fabric Blog (2024) Announcements from the Microsoft Fabric Community Conference (link)
[2] Power BI Tips (2024) Explicit Measures Ep. 236: Metrics Hub, Hot New Feature with Carly Newsome (link)
[3] Power BI Tips (2024) Introducing Fabric Metrics Layer / Power Metrics Hub [with Carly Newsome] (link)
[4] KratosBI (2024) Fabric Fridays: Metrics Layer Conspiracy Theories #40 (link)
[5] Chris Webb's BI Blog (2022) Is Power BI A Semantic Layer? (link)
[6] The Data Stack Show (2022) TDSS 95: How the Metrics Layer Bridges the Gap Between Data & Business with Nick Handel of Transform (link)
[7] Sundeep Teki (2022) The Metric Layer & how it fits into the Modern Data Stack (link)
[8] Nick Handel (2021) A brief history of the metrics store (link)
[9] Aurimas (2022) The Jungle of Metrics Layers and its Invisible Elephant (link)
[10] Benn Stancil (2021) The missing piece of the modern data stack (link)
[11] Microsoft Learn (2024) Sempy fabric Package (link)
[12] Michael Kovalsky (2019) Master Model: Creating Derivative Tabular Models (link)
[13] Christina Obry (2023) The Power of a Metrics Layer - and How Your Organization Can Benefit From It (link) 
[14] KratosBI (2024) Introducing the Metrics Layer in #MicrosoftFabric with Carly Newsome [link]

Resources:

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

🏭🗒️Microsoft Fabric: Data Governance [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: 23-May-2024

[Microsoft Fabric] Data Governance

• {definition}set of capabilities that help organizations to manage, protect, monitor, and improve the discoverability of data, so as to meet data governance (and compliance) requirements and regulations [2]
• several built-in governance features are available to manage and control the data within Fabric (MF)  [1]
• {feature} endorsement [aka content endorsement] 
• {definition} formal process performed by admins to endorse MF items
• {benefit} allows admins to designate specific MF items as trusted and approved for use across the organization [1]
• establishes trust in data assets by promoting and certifying specific MF items [1]
• users know which assets they can trust and rely on for accurate information [1]
• endorsed assets are identified with a badge that indicates they have been reviewed and approved [1]
• {scope} applies to all MF items except dashboards [1]
• {benefit} helps admin manage the overall growth of items across your environment [1]
• {feature} promoting [aka content promoting] 
• {definition} formal process performed by contributors or admins to promote content
• promoted content appears with a Promoted badge in the MF portal [1]
• workspace members with the contributor or admin role can promote content within a workspace [1]
• MF admin can promote content across the organization [1]
• {feature} certification [aka content certification]
• {definition} formal process that involves a review of the content by a designated reviewer and managed by the admin [1]
• can be customized to meet organization’s needs [1]
• users can request item certification from an admin [1]
• via Request certification from the More menu [1]
• the certified content appears with a Certified badge in the Fabric portal [1]
• {benefit} allows organizations to label items considered to be quality items [1]
• an organization can certify items to identify them an as authoritative sources for critical information [1]
• ⇐ all Fabric items except Power BI dashboards can be certified [1]
• {benefit} allows to specify certifiers who are experts in the domain [1]
• domain level settings
• enable or disable certification of items that belong to the domain [1]
• provides a URL to documentation that is relevant to certification in the domain [1]
• {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
• can be assigned to teams or departments based on governance requirements and data boundaries [2]
• are associated with domains [3]
• ⇐ {benefit} allows to group data into business domains
• all the items in the workspace are then associated with the domain, and they receive a domain attribute as part of their metadata [3]
• ⇐ {benefit} enables a better consumption experience [1]
• {benefit} enables better discoverability and governance [2]
• {feature} domains [Notes]
• {definition} a way of logically grouping together data in an organization that is relevant to a particular area or field [1]
• allows to group data by business domains
• ⇒{benefit} allows business domains to manage their data according to their specific regulations, restrictions, and needs [3]
• {feature} subdomains
  • {definition} a way for fine tuning the logical grouping data under a domain [1]
  • ⇐ subdivisions of a domain
• {feature} labeling
• default labeling, label inheritance, and programmatic labeling, 
• {benefit} help achieve maximal sensitivity label coverage across MF [2]
• once labeled, data remains protected even when it's exported out of MF via supported export paths [2]
• [Purview Audit] compliance admins can monitor activities on sensitivity labels
• {feature|preview} folders
• {definition} a way of logically grouping MF items
• {feature|preview} tags
• {benefit} allow managing Fabric items for enhanced compliance, discoverability, and reuse
• {feature} scanner API
• a set of admin REST APIs 
• {benefit} allows to scan MF items for sensitive data [1]
• can be used to scan both structured and unstructured data [1]
• {concept} metadata scanning
• facilitates governance of data by enabling cataloging and reporting on all the metadata of organization's Fabric items [1]
• it needs to be set up by Admin before metadata scanning can be run [1]
• {concept} data lineage
• {definition} 
• {benefit} allows to track the flow of data through Fabric [1]
• {benefit} allows to see where data comes from, how it's transformed, and where it goes [1]
• {benefit} helps understand the data available in Fabric, and how it's being used [1]
• {concept} Fabric item (aka MF item)
• {definition} a set of capabilities within an experience
• form the building blocks of the Fabric platform
• {type} data warehouse
• {type} data pipeline
• {type} semantic model
• {type} reports
• {type} dashboards
• {type} notebook
• {type} lakehouse
• {type} metric set

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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) 

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

Acronyms:
API - Application Programming Interface
MF - Microsoft Fabric

🧭Business Intelligence: Data Products (Part I: A Lego Exercise)

Business Intelligence Series

One can define a data product as the smallest unit of data-driven architecture that can be independently deployed and managed (aka product quantum) [1]. In other terms one can think of a data product like a box (or Lego piece) which takes data as inputs, performs several transformations on the data from which result several output data (or even data visualizations or a hybrid between data, visualizations and other content). 

At high-level each Data Analytics solution can be regarded as a set of inputs, a set of outputs and the transformations that must be performed on the inputs to generate the outputs. The inputs are the data from the operational systems, while the outputs are analytics data that can be anything from data to KPIs and other metrics. A data mart, data warehouse, lakehouse and data mesh can be abstracted in this way, though different scales apply. 

For creating data products within a data mesh, given a set of inputs, outputs and transformations, the challenge is to find horizontal and vertical partitions within these areas to create something that looks like a Lego structure, in which each piece of Lego represents a data product, while its color represents the membership to a business domain. Each such piece is self-contained and contains a set of transformations, respectively intermediary inputs and outputs. Multiple such pieces can be combined in a linear or hierarchical fashion to transform the initial inputs into the final outputs. 

Data Products with a Data Mesh

Finding such a partition is possible though it involves a considerable effort, especially in designing the whole thing - identifying each Lego piece uniquely. When each department is on its own and develops its own Lego pieces, there's no guarantee that the pieces from the various domains will fit together to built something cohesive, performant, secure or well-structured. Is like building a house from modules, the pieces must fit together. That would be the role of governance (federated computational governance) - to align and coordinate the effort. 

Conversely, there are transformations that need to be replicated for obtaining autonomous data products, and the volume of such overlapping can be considerable high. Consider for example the logic available in reports and how often it needs to be replicated. Alternatively, one can create intermediary data products, when that's feasible. 

It's challenging to define the inputs and outputs for a Lego piece. Now imagine in doing the same for a whole set of such pieces depending on each other! This might work for small pieces of data and entities quite stable in their lifetime (e.g. playlists, artists, songs), but with complex information systems the effort can increase by a few factors. Moreover, the complexity of the structure increases as soon the Lego pieces expand beyond their initial design. It's like the real Lego pieces would grow within the available space but still keep the initial structure - strange constructs may result, which even if they work, change the gravity center of the edifice in other directions. There will be thus limits to grow that can easily lead to duplication of functionality to overcome such challenges.

Each new output or change in the initial input for this magic boxes involves a change of all the intermediary Lego pieces from input to output. Just recollect the last experience of defining the inputs and the outputs for an important complex report, how many iterations and how much effort was involved. This might have been an extreme case, though how realistic is the assumption that with data products everything will go smoother? No matter of the effort involved in design, there will be always changes and further iterations involved.

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References:
[1] Zhamak Dehghani (2021) Data Mesh: Delivering Data-Driven Value at Scale (book review) 

🔖Book Review: Zhamak Dehghani's Data Mesh: Delivering Data-Driven Value at Scale (2021)

	Zhamak Dehghani's "Data Mesh: Delivering Data-Driven Value at Scale" (2021) is a must read book for the data professional. So, here I am, finally managing to read it and give it some thought, even if it will probably take more time and a few more reads for the ideas to grow. Working in the fields of Business Intelligence and Software Engineering for almost a quarter-century, I think I can understand the historical background and the direction of the ideas presented in the book. There are many good ideas but also formulations that make me circumspect about the applicability of some assumptions and requirements considered.  So, after data marts, warehouses, lakes and lakehouses, the data mesh paradigm seems to be the new shiny thing that will bring organizations beyond the inflection point with tipping potential from where organization's growth will have an exponential effect. At least this seems to be the first impression when reading the first chapters.
The book follows to some degree the advocative tone of promoting that "our shiny thing is much better than previous thing", or "how bad the previous architectures or paradigms were and how good the new ones are" (see [2]). Architectures and paradigms evolve with the available technologies and our perception of what is important for businesses. Old and new have their place in the order of things, and the old will continue to exist, at least until the new proves its feasibility.   The definition of the data mash as "a sociotechnical approach to share, access and manage analytical data in complex and large-scale environments - within or across organizations" [1] is too abstract even if it reflects at high level what the concept is about. Compared to other material I read on the topic, the book succeeds in explaining the related concepts as well the goals (called definitions) and benefits (called motivations) associated with the principles behind the data mesh, making the book approachable also by non-professionals.  Built around four principles "data as a product", "domain-oriented ownership", "self-serve data platform" and "federated governance", the data mesh is the paradigm on which data as products are developed; where the products are "the smallest unit of architecture that can be independently deployed and managed", providing by design the information necessary to be discovered, understood, debugged, and audited. It's possible to create Lego-like data products, data contracts and/or manifests that address product's usability characteristics, though unless the latter are generated automatically, put in the context of ERP and other complex systems, everything becomes quite an endeavor that requires time and adequate testing, increasing the overall timeframe until a data product becomes available.  The data mesh describes data products in terms of microservices that structure architectures in terms of a collection of services that are independently deployable and loosely coupled. Asking from data products to behave in this way is probably too hard a constraint, given the complexity and interdependency of the data models behind business processes and their needs. Does all the effort make sense? Is this the "agility" the data mesh solutions are looking for? Many pioneering organizations are still fighting with the concept of data mesh as it proves to be challenging to implement. At a high level everything makes sense, but the way data products are expected to function makes the concept challenging to implement to the full extent. Moreover, as occasionally implied, the data mesh is about scaling data analytics solutions with the size and complexity of organizations. The effort makes sense when the organizations have a certain size and the departments have a certain autonomy, therefore, it might not apply to small to medium businesses. Previous Post <<||>>  Next Post References: [1] Zhamak Dehghani (2021) "Data Mesh: Delivering Data-Driven Value at Scale" (link) [2] SQL-troubles (2024) Zhamak Dehghani's Data Mesh - Monolithic Warehouses and Lakes (link)

🧭🏭Business Intelligence: Microsoft Fabric (Part II: Domains and the Data Mesh I -The Challenge of Structure Matching)

Business Intelligence Series

The holy grail of building a Data Analytics infrastructure seems to be nowadays the creation of a data mesh, a decentralized data architecture that organizes data by specific business domains. This endeavor proves to be difficult to achieve given the various challenges faced  – data integration, data ownership, data product creation and ownership, enablement of data citizens, respectively enforcing security and governance in a federated manner. 

Microsoft Fabric promises to facilitate the creation of data mashes with the help of domains and subdomain by providing built-in security, administration, and governance features associated with them. A domain is a way of logically grouping together all the data in an organization that is relevant to a particular area or field. A subdomain is a way for fine tuning the logical grouping of the data.

Business domains & their entities

At high level the challenge of building a data mesh is on how to match or aggregate structures. On one side is the high-level structure of the data mesh, while on the other side is the structure of the business data entities. The data entities can be grouped within a taxonomy with multiple levels that expands to the departments. That’s why it seems somehow natural to consider the departments as the top-most domains of the data mesh. The issue is that if the segmentation starts from a high level, iI becomes inflexible in modeling. Moreover, one has only domains and subdomains, and thus a 2-level structure to model the main aspects of the data mesh.

Some organizations allow unrestricted access to the data belonging to a given department, while others breakdown the access to a more granular level. There are also organizations that don’t restrict the access at all, though this may change later. Besides permissions and a way of grouping together the entities, what value brings to set the domains as departments? 

Therefore, I’m not convinced about using an organizations’ departmental structure as domains, especially when such a structure may change and this would imply a full range of further changes. Moreover, such a structure doesn’t reflect the span of processes or how permissions are assigned for the various roles, which are better reflected on how information systems are structured. Most probably the solution needs to accommodate both perspective and be somehow in the middle. 

Take for example the internal structure of the modules from Dynamics 365 (D365). The Finance area is broken down in Accounts Payable, Accounts Receivables, Fixed Assets, General Ledger, etc. In some organizations the departments reflect this delimitation to some degree, while in others are just associated with finance-related roles. Moreover, the permissions are more granular and, reflecting the data entities the users work with. 

Conversely, SCM extends into Finance as Purchase orders, Sales orders and other business documents are the starting or intermediary points of processes that span modules. Similarly, there are processes that start in CRM or other systems. The span of processes seem to be more appropriate for structuring the data mesh, though the system overlapping with the roles involved in the processes and the free definition of process boundaries can overcomplicate the whole design.

It makes sense to define the domains at a level that resembles the structure of the modules available in D365, while the macro data-entities represent the subdomain. The subdomain would represent then master as well as transactional data entities from the perspective of the domains, with there will be entities that need to be shared between multiple domains. Such a structure has less chances to change over time, allowing more flexibility and smaller areas of focus and thus easier to design, develop, test, deploy and maintain.

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🗄️Data Management: Data Literacy (Part I: A Second Language)

At the Gartner Data & Analytics Summit that took place in 2018 in Grapevine, Texas, it was reiterated the importance of data literacy for taking advantage of the emergence of data analytics, artificial intelligence (AI) and machine learning (ML) technologies. Gartner expected then that by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy [1] – or how they put it – learning to ‘speak data’ as a ‘second language’.

Data literacy is typically defined as the ability to read, work with, analyze, and argue with data. Sure, these form the blocks of data literacy, though what I’m missing from this definition is the ability to understand the data, even if understanding should be the outcome of reading, and the ability to put data into the context of business problems, even if the analyzes of data could involve this later aspect too.

Understanding has several aspects: understanding the data structures available within an organization, understanding the problems with data (including quality, governance, privacy and security), respectively understanding how the data are linked to the business processes. These aspects go beyond the simple ability included in the above definition, which from my perspective doesn’t include the particularities of an organization (data structure, data quality and processes) – the business component. This is reflected in one of the problems often met in the BI/data analytics industry – the solutions developed by the various service providers don’t reflect organizations’ needs, one of the causes being the inability to understand the business on segments or holistically.  

Putting data into context means being able to use the respective data in answering stringent business problems. A business problem needs to be first correctly defined and this requires a deep understanding of the business. Then one needs to identify the data that could help finding the answers to the problem, respectively of building one or more models that would allow elaborating further theories and performing further simulations. This is an ongoing process in which the models built are further enhanced, when possible, or replaced by better ones.

Probably the comparison with a second language is only partially true. One can learn a second language and argue in the respective language, though it doesn’t mean that the argumentations will be correct or constructive as long the person can’t do the same in the native language. Moreover, one can have such abilities in the native or a secondary language, but not be able do the same in what concerns the data, as different skillsets are involved. This aspect can make quite a difference in a business scenario. One must be able also to philosophize, think critically, as well to understand the forms of communication and their rules in respect to data.

To philosophize means being able to understand the causality and further relations existing within the business and think critically about them. Being able to communicate means more than being able to argue – it means being able to use effectively the communication tools – communication channels, as well the methods of representing data, information and knowledge. In extremis one might even go beyond the basic statistical tools, stepping thus in what statistical literacy is about. In fact, the difference between the two types of literacy became thinner, the difference residing in the accent put on their specific aspects.

These are the areas which probably many professionals lack. Data literacy should be the aim, however this takes time and is a continuous iterative process that can take years to reach maturity. It’s important for organizations to start addressing these aspects, progress in small increments and learn from the experience accumulated.

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References:
[1] Gartner (2018) How data and analytics leaders learn to master information as a second language, by Christy Pettey (link)