🗃️Posts

The following section contains a list of the posts grouped by topic. Just click on the link to jump to the post. General Book Reviews Brent Dawson (2023) "Planning for Successful Data Migration" (Part I, Part II) Jürgen Faisst (2022) "International Business Communication Standards (IBCS Version 1.2: Conceptual, perceptual, and semantic design of comprehensible business reports, presentations, and dashboards" (link) Lynn Beighley (2011) "Head First SQL: Your Brain on SQL - A Learner's Guide" 1st Ed. (link) Zhamak Dehghani (2021) "Data Mesh: Delivering Data-Driven Value at Scale" (link) Laurent Bossavit (2015) "The Leprechauns of Software Engineering" (link) Willard C Brinton (1915) "Graphic Methods for Presenting Facts" (link) Debunked Zhamak Dehghani's Data Mesh Monolithic Warehouses and Lakes Quote of the Day Never-Ending Stories in Praxis Inflection Points and the Data Mesh The Good, the Bad and the Ugly Concept Documents Data Vault 2.0 Dynamics 365 Invoice Capture Methodologies Self-Service BI SSIS SSRS Azure Synapse & Microsoft Fabrics Azure Tools & Services The Azure Diagrams Architecture Advisor (Test Drive) Azure Synapse Building a Modern Data Warehouse with Azure Synapse SQL Reloaded Alternatives for Better Code Maintainability in SQL Server & Azure Synapse Part I Part II Automatic Statistics Creation & Dropping for CETAS based on CSV File Format in Serverless SQL Pool Copilot stories Part I: Copilot in Azure is in Private Preview Part II: Creating a Schema for Customer Addresses Part III: Formatting Code via Prompts Part IV: Extracting vs. Concatenating Emails Part V: Common Table Expressions for Employee Hierarchies and Interest Rates Part VI: Queries based on Dynamics 365 for Finance & Operations Part VII: Using the sp_MSForEachTable Undocumented Stored Procedures in SQL Server Part VIII: Recursive Functions Part IX: Working with the Stuff Function Part X: Working with the Left Function Data Management Views for the Synapse serverless SQL pool Documenting External Tables Misusing Views in Serverless SQL Pool Monitoring the Synapse serverless SQL pool with Dynamics Management Views Part I Part II Querying Azure Synapse Metadata for the D365 CRM & FO Tables Exported via Azure Synapse Link for Dataverse SQL Antipatterns  Part I: JOINs, UNIONs & DISTINCT  Temporary Tables and Tempdb in Serverless SQL Pool KQL Reloaded First Steps Part I: Simple Queries Part II: Working with Dates Part III: Basic Joins Part IV: Left, Right, Anti-Joins and Unions Part V: Database Metadata Part VI: Actual vs. Estimated Count Part VII: Basic Data Visualizations Part VIII: Translating SQL to KQL - Full Joins Part IX: Translating SQL to KQL - More Joins Part X: Translating SQL to KQL - Correlated Subqueries Part XI: Window Functions

Business Intelligence

Introduction Between Potential, Reality, Quality and Stories Between Products, Partners, People and Processes Enterprise Reporting Issues New Technologies, Old Challenges Part I: An Introduction Part II: ETL vs. ELT Data Architecture  Monolithic vs. Distributed Architecture Part I: Zhamak Dehghani's Data Mesh - Debunked Part II: Architectural Choices Part III: Architectural Applications Data Products  Part I: A Lego Exercise Part II: The Complexity Challenge Data Projects Why Data Projects Fail to Deliver Real-Life Impact Part I: First Thoughts Part II: There's Value in Failure Part III: Failure through the Looking Glass Part IV: Making It in the Statistics Data Science Back to the Future  Part I: About Beginnings Part II:  Big Data vs. Business Strategies Generalists vs Specialists in the Field of Data Science Notes Team Data Science Process (TDSP) Myths Monolithic Warehouses and Lakes Graphical Representation Graphics We Live By Part I: The Analytics Marathon Part II: Discount Rates in MS Excel Part III: Exchange Rates in Power BI Part IV: Area Charts in MS Excel Part V: Pie Charts in MS Excel Part VI: Conversion Rates in Power BI Part VII: Reading a Conversion Rates Chart with ChatGPT and Copilot Part VIII: List of Items in Power BI Part IX: Word Clouds in Power BI Part X: Pie and Donut Charts in Power BI and Excel Part XI: Comparisons Between Data Series Bar Charts Line Charts Scatter & Bubble Charts HR Data Overreaching in Data Visualizations Reports vs. Data Visualizations The Analytics Marathon visuals (Notes) heatmaps sparklines Metrics  KPIs Part I: An Introduction Part II: Between Certainty and Uncertainty Microsoft Fabric Part I: Notebooks Part II: Domains and the Data Mesh I - The Challenge of Structure Matching Part III: The Metrics Layer 🆕 Part IV: Is Microsoft Fabric Ready? Part V: One Person Can’t Learn or Do Everything Part VI: Data Stores Comparison Delta Tables in Action CRUD Operations I: Basic Operations CRUD Operations II: Looking at the Files Import data from CSV Files into Delta Table Views and other Data Objects Table Metadata II: Updating a table's COMMENT attributes Table Metadata I: General Information Lakehouses at Work Part I: Proof-of-Concept SQL Reloaded

• Part I: Monitoring the Warehouse
• SQL Databases 
• Part I: Creating a View
• Part II: Under the Hood
• Part III: Backups
• Part IV: Automatic Tuning Options
  
• Part V: Manual Index Maintenance
• Part VI: Index Usage Analysis
• Part VII: Things That Don't Work
• Part VIII: Permissions
• Part IX: From OLTP to OLAP Data Models
• Part X: Templates for Database Objects
• Part XI: Database and Server Properties
• Part XII: Databases

Notes

• Azure
• Azure Data Factory (ADF)
• Azure Data Lake Storage Gen2 (ADLS Gen2) 
• Azure service principals (SPN)
• Azure storage account
• general
• continuous integration & continuous deployment [CI/CD]
• data governance
• medallion architecture
• data pipeline
• Hadoop
• Microsoft Fabric
• Data Factory
• Copy job
• data sharing
• external data sharing
• data store
• dataflows (Gen1)
• dataflows (Gen2)
• delta lake
• delta tables
• Eventhouses
• Activator
• Eventstreams
• Kusto Query Language (KQL)
• Real-Time Dashboards (RTD)
• Real-Time Hub (RTH)
• Real-Time Intelligence (RTI)
• folders
• governance
• Data Loss Prevention (DLP) 
• Purview
• lakehouse
• partitions in lakehouse
• metrics layer
• mirroring
• notebooks
• T-SQL notebook
• OneLake
• OneLake Role-Based Access Control (RBAC)
• parquet format
• Python
• user data functions
• query acceleration
• security
• sharding
• shortcuts
• SQL Server
• SQL analytics endpoint
• Result Set Caching
• Polaris
• Polaris SQL Pool
• warehouses
• caching
• clone tables
• query optimizer
• security
• statistics
• v-order
• warehouse snapshots
• z-order
• workspaces

Perspectives

• Part 1: General Issues
• Part 2: The Complexity Myth
• Part 3: Self Service BI
• Part 4: How Big is Your Report?
• Part 5: Data Soup - From BI to Analytics
• Part 6: Data Soup - Reports vs Data Visualization
• Part 7: Insights - Aha Moments!
• Part 8: Insights - The Complexity Perspective
• Part 9: Dashboards are Dead & Other Crap
• Part 10: The Top 5 Pains of a BI/Analytics Manager
• Part 11: Ways of Thinking about Data
• Part 12: From Data to Data Models
• Part 13-15: From Data to Storytelling (Part I, II, III) 
• Part 16: On the Cusps of Complexity
• Part 17: Creating Value for Organizations
• Part 18: There’s More to Noise
  
• Part 19: Data Visualization between Art, Pragmatism and Kitsch
• Part 20: From BI to AI
• Part 21: Data Visualization Revised
  
• Part 22: Queries' Complexity
• Part 23: In between the Many Destinations
• Part 24: Building Castles in the Air
• Part 25: Grounding the Roots
• Part 26: Monitoring - A Cockpit View
• Part 27: A Tale of Two Cities II
• Part 28: Cutting through Complexity
• Part 29: Navigating into the Unknown
• Part 30: The Artificial Intelligence Connection
• Part 31: More on Data Visualization
• Part 32: Data Storytelling in Visualizations

Power BI

• Learning curve
• Part I:
• Metrics Layer
• An Introduction
• Notes
• Metrics Layer [private preview]
• Visual Calculations
• Preparatory Steps for Creating a Power BI Report
• Working with Visual Calculations (new feature)
• Part I: Test Drive
• Part II:  Simple Tables with Square Numbers as Example
• Part III: Matrix Tables with Square Numbers as Example

Software Engineering

• A One Man Show
• Part I: Some Personal Background and a Big Thanks!
• Part II: In the Cusps of Complexity
• Part III: The Microsoft Fabric
• Part IV: Data Roles between Past and Future
• Part V: Focus on the Foundation
• Part VI: The Lakehouse Perspective
• A Software Engineer's Perspective 
• Part I: Houston, we have a Problem!
• Part II: Major Knowledge Gaps
• Part III: More of a One-Man Show
• Part IV: The Loom of Interactions
• Part V: From Process Management to Mental Models in Knowledge Gaps
• Part VI: The Data Citizen
• Part VII: Think for Yourself!
• Mea Culpa
• Part II: The Beginnings
• Part V: All-Knowing Developers are Back in Demand?
  Part VI: A Look Back
• Part VII: A Look Forward
• Part VIII: A Look Beyond
• Part IX: A Look Inward
• Part X: A Look Beyond AI
• Approaching a Query
• Learning SQL

Reports

• A Reporting Guy’s Issues
• Addressable Questions in Reports’ Creation I
• Best Practices
• Choosing Report’s Attributes
• Filtering Internal Kitchen
• How Big Is Your Report?
• How Many Reporting Systems You Need?
• Levels of Accuracy in Reporting
• Levels of Detail in Reporting
• Reports Types
• Reports’ Lifecycle - Definition

Strategy

• What is a BI Strategy?

Python

• Drawing Concentric Circles with matplotlib.pyplot
• Installing PySpark and GraphFrames on a Windows 10 Machine
• Plotting Data with the Radar Chart
• Test Drive PySpark and GraphFrames

R Language

• Data Summaries without Using a DataFrame
• Data Transformations
• Part I: Temperatures' comparison between F° and C°
• Drawing Function Plots
• Part I: Basic Curves
• Part II: Basic Curves & Inflection Points
• Hello World in R & Working with Data Frames
• Using the lessR Package in Microsoft Fabric's Notebooks (Test Drive)
• Visualizing the Iris Dataset

SSRS (& Paginated Reports)

• Covid-19 Data Project
• Matrix Report Display - Fifth Magic Class
• Parameter Dependencies in Dropdowns- Fourth Magic Class
• Poor Design of Parameters
• Ranking Rows in Reports
• Report Formatting - Third Magic Class
• Report Parameters - Second Magic Class
• SQL Server CPU Utilization
• The Good, the Bad and the Ugly
• Wizarding a Report
• Graphical Representations II - Sixth Magic Class

Test Drives

• Cognos & SQL Server 2008 - The First Report

Data Management

• Business Rules
• Part I: An Introduction
• Data Culture
• Part I: No Silver Bullet
• Part II: Leadership is Necessary but not Sufficient
• Part III: A Tale of Two Cities
• Part IV: Quo vadis? [Where are you going?]
• Part V: Quid nunc? [What now?]
• Data Driven
• The Data-Driven Enterprise
• Data Governance
• Part I: No Guild of Heroes
• Part II: Heroes Die Young
• Part III: Taming the Complexity
• Data Integration
• From Disintegration to Integration
• Data Literacy
• Data Literacy as Second Language
• Data Operations
• One Database, Two Vendors, No Love Story or Maybe…
• Data Profiling
• Part I: An Introduction
• Part II: Using CLR functions
• Data Quality 
• An Introduction
• Data Cleansing
• Data Migration
• Data Migration’s Perspective I (A Bird’s-Eye View)
• Dimensions
• Accuracy
• Completeness
• Conformity
• Consistency
• Referential Integrity
• Uniqueness
• Information Systems' Perspective
• Data Security
• SQL Injection I - Introduction
• Data Soup
• Data Lakes & other Puddles
• Data Structuredness
• From Business Intelligence to Analytics
• Master Data Management (MDM)
•  Understanding Master Data Management’s Integration Challenges (Answer)
• Notes
• Data
• Master Data Management

Data Migrations

Introduction Introduction What is Data Migration? Planning Approaches to Planning a Data Migration for an ERP Implementation Create a Data Migration Strategy for Dynamics 365 Conceptualization Part I: Goals, Objectives & Requirements Part II: Plan vs. Concept vs. Strategy Part III: Heuristics Part IV: Data Access Part V: The Extraction Layer Part VI: The Data Migration Layer Part VII: The Data Import Layer Facts, Principles and Practices Guiding Principles The SQL Server Perspective Licensing Costs and Edition Choices Data Quality’s Perspective  Part I: A Bird’s-Eye View Hands-On Migrating AdventureWorks to Dynamics 365 - Products In-house Built Solutions Part I: Introduction Part II: The Import Layer Part III: The Preparation Layer Programming Functions Useful in Data Migrations Useful Functions in Data Conversion

Data Warehousing

• Data Vault 2.0
• The Good, the Bad and the Ugly
• Data Warehousing
• A Personal Journey
• Data Load Optimization - A Success Story
• Data Warehousing and Microsoft Dynamics 365
• A Few Issues to Consider (Part I)
• Building a Data Lakehouse for Dynamics 365 Environments with Serverless SQL Pool
• Data Warehousing and Microsoft Dynamics 365
• Dynamics 365, the Data Lakehouse and the Medallion Architecture
• ETL
• An Introduction
• Push vs. Pull
• SSIS packages vs. SQL code
• Synchronization
• The Extract Subprocess
• The Load Subprocess
• The Transform Subprocess
• Microsoft Fabric
• Data(base) Mirroring in Microsoft Fabric (New feature)
• SSIS
• Aggregate Data Flow Transformation
• Percentage Sampling Data Flow Transformation
• Second Magic Class - SQL Server to Oracle Data Export
• The Conditional Split Data Flow Transformation
• The Good, the Bad and the Ugly
• The Union All Data Flow Transformation
• Third Magic Class - Data Flow Task
• Using Oracle as Data Source
• Visual Studio 2010 CR doesn’t support SSIS Projects
• Wizarding an SSIS Package - First Magic Class

Database Management Systems (DBMS)

• Concepts
• Definitions
• Part I: The Stored Procedure Case
• Part II: What's in a Name
• Challenges
• TSQL Challenge 24
• Cheat sheets
• DELETE vs. TRUNCATE TABLE
• Database objects
• Dependencies I - Introduction
• Views I
• Views II
• Table Value Constructors at Work
• Temporary Tables vs. Table Variables and TempDB
• Databases
• An Introduction
• Event Streaming Databases - More of a Kafka’s Story
• Running a Statement for Each Database - CLR Version
• Searching a Value within a Database
• SQL Reloaded
• A Look into Reverse
• Advices on SQL logic split in Web Applications
• Aggregates
• Number of records
• COUNT
• ROWCOUNT in action
• The CLR Version
• The DMV Approach
• via sys.partitions DMV
• via cursor
• via sp_MSForEachTable undocumented stored procedure
• Before and After
• Drop If Exists
• Splitting a String
• Trimming Strings
• Best practices
• Query Writing I
• Concatenation
• Common table expressions
• 
  
• CRUD 
• Incremental Update Technique
• Saving Data With Stored Procedures
• Self-Join in Update Query I
• Self-Join in Update Query II
• Stored Procedures from Metadata
• Part I: Insert & Update
• Part II: Get, Dropdown & Delete
• Updating Data With Values From The Same Table
• Cursor and Linked Server for Data Import
• Cursors and Lists
• Data Processing
• Preparing test data
• Processing JSON Files with Complex Structure in SQL Server 2016+
• Processing JSON Files with Flat Matrix Structure in SQL Server 2016+
• Randomized Data Selection
• SQL Server and Excel Data
• Trivial Equalities in Queries
• Useful Functions in Data Conversion
• Ways of Looking at Data
• Part I
• Part II
• Part III
• Part IV: Hierarchies
• Part V: Hierarchies
• When Queries Look Like Ducks
• XML on SQL Server 2005
• Dates
• GetDate SQL Server 2000/2005
• More Date-related Functionality I
• More Date-related Functionality II
• Some Notes on Dates
• Dynamic Queries
• Evaluating Textual Expressions with CLR UDFs
• Handling duplicates
• Dealing with data duplicates on SQL Server
• Deleting Sequential Data From a Table
• Joins
• Self-Join in Update Query I
• Self-Join in Update Query II
• Self-Joins and Denormalized Data Loading in Normalized Models
• The Power of Joins
• Part I - An Introduction
• Part II - Horizontal Joins
• Part III - Vertical Joins
• Part IV - Multi-Joins Under Control
• Part V - Semi-Joins and Anti-Joins
• Part VI - Not-Equal Joins
• Just in CASE
• Part I: Introduction
• Part II: Clauses and Joins
• Part III: Quest for Performance
• Part IV: Other Scenarios
• Part V: Dynamic Queries
• Functions Useful in Data Migrations
• Lists
• List Based Aggregations - On Hand example
• Lists as Parameters in Stored Procedures
• Lists, Sets and a Little Math
• Table-valued Functions and List of Values
• Math
• Prime Numbers
• To be or not to be a Prime
• 6 out of 49
• Part I: Getting the data
• Part II: Modeling the Data
• Part III: Basic Data Analysis
• Part IV: More Basic Data Analysis
• Misusing Views and Pseudo-Constants
• 
  
• Null-ifying the World
• Out of Space
• PIVOT Operator Example
• Partition-based functions
• Successive Price Increases/Discounts via Windowing Functions and CTEs
• STRING_AGG and STRING_SPLIT at Work, and a Bit of Pivoting
• String_Split Function
• Tricks with Strings via STRING_SPLIT,  PATINDEX and TRANSLATE
• window functions
• Simple Aggregations (SQL Server 2022)
• Running Totals (SQL Server 2022)
• Ranking (SQL Server 2022)
• Window Functions
• Pulling the Strings of SQL Server 
• Part I - An Introduction
• Part II - Creation and Selection
• Part IV - Spaces, Trimming, Length and Comparisons
• Part V - Character Indexes
• Part VI - Subparts of a String
• Part VII - List of Values
• Part VIII - Insertions, Deletions and Replacements
• Part IX - Special Characters
• Part X - Dynamic Queries
• The STUFF function
• Oracle
• Hierarchies in Oracle
• Troubleshooting
• ANSI 92 JOIN syntax error
• No records returned by queries
• Oracle vs SQL Server
• Averages I
• Date Conversion
• Date Operations
• Date Validation
• Handling Missing Dates
• Running Totals
• Query Patterns in SQL Server
• Aggregate Functions
• Inserts
• Joins and Subqueries
• Matrix Representation
• Window Functions
• SQL Server
• Administration
• Database Recovery on SQL Server 2017
• End of Life for 2008 and 2008 R2 Versions
• Feature Bloat
• Killing Sessions - Killing ‘em Softly and other Snake Stories
• Monitoring the Database Logs
• System.OutOfMemoryException in SQL Server Management Studio and other 32-bit Drawbacks
• Metadata
• Part I: Indexes Overview
• Features
• New
• Hello World in R & Working with Data Frames
• SQL Server 2012 is almost here
• Undocumented
• Part I: Reading the SQL Server Log
• Part II: Execute Command for Each Table
• Part III: CPU Utilization via the Ring Buffer
• Part IV: DBCC SQLPERF
• Wish List
• Replace From

• Notes

• buffer pool 
• checkpoints
• compression
• databases
• tempdb
• resource
• garbage collection
• language
• folding
• Null
• live query statistics
• logging
• minimally logged
• locking
• memory
• memory manager
• NUMA
• objects
• cursor [defs]
• databases [defs]
• model database [defs]
• tempdb database [defs]
• functions
• aggregate functions [defs]
• analytics functions
• system functions
• table-valued functions
• user-defined functions
• indexes [defs]
• clustered index [defs]
• clustered vs non-clustered indexes
• columnstore indexes
• composite indexes [defs]
• covering indexes [defs]
• index selectivity [defs]
• non-clustered indexes [defs]
• keys
• composite key [defs]
• foreign key [defs]
• primary key [defs]
• schemas [defs]
  
• statistics [defs]
• issues
• stored procedures [defs]
• system stored procedures [defs]
• user-defined stored procedures
• tables [defs]
• contained database
• resource database
• scalable shared database
• stretched databases
• table variables
• tempdb
• temporal databases
• temporary tables
• triggers [defs]
• views [defs]
• user-defined views
• indexed views
• system views
• parameter sniffing
• partitions
• plan cache
• queries
• correlated subquery [defs]
• query optimization
• query plan
• query processing
• subqueries
• real-time operational analytics 
• resource governor
• resource monitor
• service broker
• sessions
• transaction log [defs]
• transactions [defs]
• variables
• Troubleshooting
• Adventure Works - one year later
• Adventure Works installation error on Vista
• AdventureWorks requires FILESTREAM enabled
• Could not load file or assembly Microsoft.MSXML…
• Errors
• Information Schema Views
• Logical errors
• Login Failed for User
• Microsoft SQL Server 2008 installation error
• Porting 32 bit CLR UDFs on 64 bit Platforms
• Problems in SQL Server 2000 DTS Packages
• Root Blocking Sessions
• Searching the Error Log
• Who is Active

ERP Systems

• Data Migration
• Create a Data Migration Strategy for Dynamics 365
• Data Warehousing
• Dynamics 365, the Data Lakehouse and the Medallion Architecture
• Implementations
• Part I: The Right ERP software
• Part II: General Points of Failure
• Part III: It’s all about Scope I - Functional Requirements
• Part IV: It’s all about Scope II - Nonfunctional Requirements & MVP
• Part V: It’s all about Partnership I - An Introduction
• Part VI: It’s all about Partnership II -
• Part VII: The Process Seems to Be Broken
• Part VIII: It’s a Matter of Complexity
• Part IX: Simplifying the Implementation Project
• Part X:  Introducing an Upfront Proof-of-Concept Setup
• Part XI: Tales from the Crypt 
• Part XII: The Process Perspective
• Part XIII: On Project Management
• Part XIV: A Never-ending Story
• Part XV: An Ecosystemic View
• Part XVI: It’s All About Politics
• Part XVII: Taming the Monsters
• Part XVIII: The Price of Partnership
• Dynamics 365
• general
• The Good, the Bad and the Ugly
• Finance
• Invoice Capture 
• features
• setup areas
• process diagram
• thoughts
• The Good, the Bad and the Ugly
• Notes
• Business Performance Analytics
• Business Performance Planning
• Business Process Catalog (BPC)
• Fixed Assets
• Financial tags
• Invoice capture
• Vendor Invoices Processing 
• setup areas
• Operations
• "Create Product" Process Diagram in Dynamics 365 for Finance & Operations
• SQL queries
• AX 2009 vs. D365 FO - Product Prices with two Different Queries
• Base Enums Metadata
• Blocked Products in Dynamics 365 F&O
• Dynamics AX 2009: Deleting Obsolete Companies
• Released Products by Legal Entity in Dynamics 365 F&O
• Security Queries
• Useful Queries for D365 FO (and not only)
• Oracle e-Business Suite
• APPS internals II
• APPS internals I

Metablogging

• Blogging
• Introduction
• Changing the Status quo
• Past, Present and Future

MS Office

• MS Excel
• Excel for SQL Developers
• Part I - Range to Delimited List of Values
• Part II: Insert Statementso
• Part III: Creating a Stored Procedure from Table’s Metadata
• Part IV: Differences Between Two Datasets
• Part V: Formatting Output to HTML
• MS Access
• Is MS Access or MS Excel the Answer to your Problem?
• MS Access vs. LightSwitch - About Starts and Ends of Software Products
• The Limitations of MS Access Database
• Why I dislike MS Access

Process Management

• Dynamics 365 for Finance & Operations
• "Create Product" Process Diagram
• "Invoice Capture" Process Diagram
• ITIL
• "Manage Problem" Process Diagram

Project Management

• Methodologies: 
• Agility under Eyeglasses 
• Part I
• Part II
• The Good, the Bad and the Ugly
• Project Planning
• Planning Correctly Misundersood
• Part I
• Part II
• Part III
• Part IV
• Some Thoughts on Planning
• Part I
• Part II
• The Butterflies of Project Management
• Project Tools
• The Choice of Tools in Project Management
• Project Management
• Projects' Dynamics 
• Introduction
• Motion
• Redefining Projects' Success I
• The Agile Manifesto Reloaded
• Introduction
• Requirements Management

Software Engineering

• Application Design
• Rapid Prototyping
• Who Messed with My Data?
• Business Intelligence 
• A Problem Solving Approach to Troubleshooting I

• Copilot Unabridged

  (Answers to Copilot Prompts, so feel free to experiment with them as well!) 🤖〽️
• Part 1: The Importance of AI in Society: A Transformational Force I
• Part 2: The Importance of AI in Society: A Transformational Force II
• Part 3: Why AI in Society Doesn’t Matter (Or Does It?)
• Part 4: Why Society Doesn't Matter for AI
• Part 5: Why There Are Limits to AI Growth
• Part 6: Why There Are No Limits to AI Growth
• Part 7: The Risks Associated with AI - Challenges in a Rapidly Advancing World
• Part 8: Critical Points in the Development of AI - Milestones and Challenges
• Part 9: The Perils of AI - Risks and Challenges in a Rapidly Evolving Technology
• Part 10: When Will AI Become a Danger for Society?
• Part 11: How the Danger of AI on Society Can Be Alleviated
• Part 12: How Humanity Can Respond When AI Becomes a Danger to Society
• Part 13: What Humanity Can't Do When AI Becomes a Danger to Society
• Part 14: How Fast Humanity Could Face Extinction Due to AI Intervention
• Part 15: What AI Can Use Its Intelligence to Damage Society
• Part 16: How AI Can Use Its Intelligence to Help Society
• Part 17: Can AI Become Self-Conscious? Exploring the Possibilities
• Part 18: How Fast Can AI Surpass Human Intelligence?
• Part 19: How Much AI Influences the Recruitment Process
• Part 20: The Negative Influence of AI on the Recruitment Process
• Part 21: Can AI Be Fooled? Understanding Its Vulnerabilities
• Part 22: How AI Can Fight Against Other AI - The Battle of Algorithms
• Part 23: How AI Can Be Tamed - Ensuring Responsible Development and Use
• Part 24: The Next Steps in the Development of AI
• Part 25: How AI Can Outsmart Us - The Rise of Machine Intelligence
• Part 26: How Humans Can Outsmart AI - The Power of Human Ingenuity
• Part 27: The Negative Impact of AI on the Economy
• Part 28: The Gray Areas of AI: Navigating Ethical and Practical Uncertainty
• Part 29: The Duality of AI - Innovation and Ethical Challenges
• Part 30: The Multiplicity of AI - Expanding Perspectives in Artificial Intelligence
• Part 31: The Potential of AI to Reach Singularity
• Part 32: Can AI Be Stopped from Reaching Singularity?
• Part 33: The Conflict of Interests Among Superintelligent AIs
• Part 34: How the Average Citizen Can Use AI in Everyday Life
• Part 35: How AI Impacts the Average Citizen
• Part 36: How AI Can Reduce Unemployment
• Part 37: How AI Can Increase Unemployment
• Part 38: The Growing Backlog of AI Policies - How Much Needs to Be Done?
• Part 39: How Fast Does Humanity Move in Enforcing Policies to Cope with AI’s Rapid Growth?
• Part 40: How Disruptive Is AI as a Technology?
• Part 41: How AI Can Play Devil’s Advocate - Challenging Assumptions and Expanding
• Part 42: How AI Can Help in Understanding Complex Systems
• Part 43: How Can I Use AI for Blogging? 
• Part 44: How Can I Misuse AI for Blogging? 
• Part 45: How Fast a Conflict with AI can escaladate
• Part 46: Understanding AI Governance - Balancing Innovation and Responsibility
• Part 47: The Future of AI - How Artificial Intelligence Could Evolve in the Next Decade
• Part 48: AI - Society’s Illusion of Progress
• Part 49: The End of AI - Will We Ever Stop Using Artificial Intelligence?
• Part 50: The Obsolescence Effect - How AI May Render Technologies and Jobs Redundant
• Part 51: Will AI Make Programmers Obsolete?
• Part 52: Will AI Make Project Managers Obsolete?
• Part 53: Will AI Make Software Engineering Obsolete?
• Part 54: The Future of Business Intelligence - Will AI Make It Obsolete?
• Part 55: Will AI Make Data Analysis Obsolete?
• Part 56: AI and the Search for Immortality - A Digital Quest for Eternal Life
• Part 57: AI and the Search for Spirituality - A New Frontier
• Part 58: AI and the Search for Consciousness - Can Machines Truly Think?
• Part 59: The Exploitable Weaknesses of AI - Risks and Challenges
• Part 60: The Foolishness of AI: Weaknesses That Can Be Exploited
• Part 61: The Competitive Gap: AI-Adopting vs. AI-Resistant Organizations
• Part 62: When AI Goes Terribly Wrong: Risks and Consequences
• Part 63: Is AI Making Humanity Dumber? The Risks of Over-Reliance
• Part 64: How AI Can Make Humanity Smarter
• Part 65: AI: A Reflection of Humanity 
• Part 66: The Rise of AI: A New Era of Power Transition 
• Part 67: The Reality of AI: A World Beyond Human Perception 
• Part 68: AI: A Reflection of Intelligence, Not a Replica 
• Part 69: AI and the Illusion of Knowledge: Learning Without Understanding 
• Part 70: AI and the Illusion of Consciousness: Can Machines Truly Think?
• Documentation
• Lessons Learned
• Concept Documents: The Good, the Bad and the Ugly
• Knowledge Management
• Keeping Current or the Quest to Lifelong Learning for IT Professionals
• Mea Culpa 
• Part II: The Beginnings
• Part V: All-Knowing Developers are Back in Demand?
• Part VI: A Look Back
• Programming
• Part I: Learning SQL
• Part II: To get or not Certified?!
• Part III: Football and Software Development
• Part IV: Believe and Not Doubt?!
• Part V: Is MS Access or Excel the Answer to your Problems?
• Part VI: What is Programming About?
• Part VII: Documentation - Lessons Learned
• Part VIII: Pair Programming
• Part IX: Programmer, Coder or Developer?
• Part X: Programming as Art
• Part XI: The Dark Side of Programming
• Part XII: Misconceptions about Programming I
• Part XIII: Misconceptions about Programming II
• Part XIV: Good Programmer, Bad Programmer
• Part XV: Rapid Prototyping - Introduction
• Part XVI: The Software Quality Perspective and AI
• Part XVII: More on AI

Strategic Management

• Lean Management
• Between Value and Waste
• Part I: Introduction
• Notes
• Strategy
• Performance Management
• First Time Right (The Aim toward Operational Excellence)
• Over-Educated, Yet Under-Qualified?
• The Need for Perfection vs. Excellence
• Simplicity
• Part I: Simple, but not that Simple
• Part II: A System's View
• Part III:
• Part IV: Designing for Simplicity
• Part V: ERP Implementations' Story I
• Part VI: ERP Implementations' Story II
• Strategy
• Strategic Perspectives
• Part I: Agile vs. Lean Organizations
• Part II: The Elephant in the Room
• Defining the Strategy
• Quality Acceptance Criteria for Strategies and Concepts
• Strategic Planning
• The Reason behind a Strategy
• The Impact of New Technologies
• A Nail Keeps the Shoe
• The Technology-oriented Patient
• Checking the Vital Signs
• Inflection Points and the Data Mesh

Systems Engineering

• Complex Systems
• Never-Ending Stories in Praxis (Quote of the Day) 
• A Play of Problems (Much Ado about Nothing)
• Simplicity
• Part I: Simple, but not that Simple
• Part II: A System's View
• Part IV: Designing for Simplicity