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Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jen Stirrup, CEO of AI and BI leadership consultancy Data Relish, and Thomas Weinandy, research economist at Upside, show you how to use data already available to your organization.
Springboarding from the skills that you already possess, this book adds AI to your organization’s technical capability and expertise with Microsoft Power BI. By using your conceptual knowledge of BI, you’ll learn how to choose the right model for your AI work and identify its value and validity.
The adoption rate of AI in businesses has seen significant growth and is impacting various industries in diverse ways. As of 2023, the global AI market was valued at $136.6 billion and it is anticipated to reach $1,811 billion by 2030 (1). Further, the global acquisition rate of AI has increased in recent years, demonstrating a significant uptick in AI utilization across different business sectors. Regarding specific industry impacts, AI is expected to drive a substantial AI-driven boost to the GDP in various sectors. This widespread adoption emphasizes AI’s versatility and prospect for transforming data, which lies at the root of all businesses. Due to the promise of AI, companies are keen to leverage AI as a Service – AIaaS platforms – to use sophisticated AI tools without needing vast in-house expertise. For example, AI will revolutionize customer interactions in the retail industry. Experts predict that 19 in every 20 customer interactions will be AI-assisted by 2025, demonstrating an increasing dependence on AI for enriching customer service and engagement.
What is the current state of AI technology in businesses?
At the time of writing, businesses increasingly employed AI to streamline efficiencies and increase productivity through automation. Businesses increasingly use AI to automate tasks such as data entry, customer services like chatbots, and inventory management. This automation increases efficiency and allows human employees to focus on the creative and innovative tasks that make us human.
Humans cannot hold billions of data points simultaneously in our heads, so we have tools such as AI and Power BI! Businesses are increasingly analyzing large datasets with AI to extract insights and support data analytics through AI tools in data exploration and data engineering. As of 2023, research shows that 48% of businesses use machine learning, data analysis, and AI tools to maintain the accuracy of their big data stores (2). AI can be another friend at the analytics table, helping to forecast future trends and making data-driven decisions. It applies to many business spheres, such as customer behavior analysis, social media analytics, and operational inefficiencies. AI can support personalization by analyzing customer data, from bringing in new customers to retention efforts if they look like they may churn. Businesses can craft recommendations, relevant content, and marketing messages for each customer lifecycle stage.
AI systems assist businesses in strategic planning and risk assessment. AI helps leaders make strategic planning and risk assessments, but the data has to be appropriately presented so that the message of the data is clear. Using AI and data visualization together gives decision-makers the best tools to make optimal decisions. You can support this journey by providing business leaders with comprehensive analyses in Power BI powered by AI.
We see the use of AI in Power BI as a journey that brings together many parts of a business, such as data, business goals, and cloud computing infrastructure. The book’s structure is designed to help you navigate this journey in a cohesive manner.
Every journey needs a map, so we start our journey by providing a roadmap in Chapter 1 – Getting Started with AI in the Enterprise’. Data Modelling is a timeless skill that transcends technology but is sometimes forgotten!
In Chapter 2 – ‘A Great Foundation: AI and Data Modeling,’ we cover what you need to know so that your data is in great shape for your journey in AI and Power BI. Businesses usually want everything done in a manner that is good, fast, and cheap!
We show you how to get started with AI tools productively and we will look at OpenAI and ChatGPT with Power BI (3) in Chapter 3 – Blueprint for AI in the Enterprise. One blocker to getting started quickly is the data. If businesses think that their data is perfect, most likely they have not looked properly! In Chapter 4 – ‘Automating Data Editing and Exploration’ – we help you to identify data quality issues before you start to go down the wrong path.
From Chapter 5 to Chapter 11, we take you through practical examples where you will use AI and Power BI to tackle real-world problems to help you and your business. In Chapter 5, ‘Working with Time Series Data’, we will put our best foot forward with Time Series Analysis, a tool that is important when analyzing business trends. Chapter 6, ‘Cluster Analysis and Segmentation’, shows how to use cluster analysis and segmentation to support your business needs when grouping together similar entities. In Chapter 7, ‘Diving Deeper: Using Azure AI Services in Power BI’, you’ll see how to use Azure AI Services, Microsoft’s latest AI offering, to help you quickly get on board with AI.
AI and Power BI are not only for traditional ‘rectangular’ shaped data, such as spreadsheets or CSV files. Technologies like text processing are used in customer service to understand customer feedback, enhancing interaction and service quality. In Chapter 8, called ‘Text Analytics’, we will cover text analytics in detail. Image data can be challenging in AI, and we will explore image data in Chapter 9, ‘Image Tagging’.
What happens when you need to customize your AI? We cover this topic in Chapter 10, ’Custom Machine Learning Models’, so you can move further in your AI journey. In Chapter 11, ‘Data Science Languages: Python and R in Power BI’ we dive into Python and R to support you as you develop your AI capabilities.
In Chapter 12, ‘Making your AI Production-ready with Power BI’, we take the AI from your laptops and put it into production! We look at how you can iterate effectively in your AI development process.
We finish by looking at AI and beyond with ethics in Chapter 13, ‘The AI Feedback loop.’ From a business perspective, there is a growing emphasis on ethical AI practices, mainly where AI interacts with customers. Businesses are becoming increasingly sensitive to the need to design and use AI in a way that is ethical, transparent, and compliant with privacy and data security guidelines.
Overall, the application of AI in business is diverse and rapidly evolving, with new use cases emerging as the technology advances. Businesses increasingly recognize AI’s value in gaining competitive advantage, improving customer experience, and streamlining operations.
Understandably, people want to develop their careers to match the skill gap in AI. This book is aimed at intermediate data-savvy analysts and business intelligence users interested in rounding off their toolset with the knowledge of AI in Power BI. Throughout the book, we provide practical examples so you can get started immediately in an actionable manner that is relevant to businesses.
Despite its business benefits, AI in business also faces challenges, including data quality issues, a lack of skilled personnel, and a technology mix that can also be confusing. There is also a need to learn more about AI ethics.
We are thrilled to take you on this journey of exploring AI, and we wrote this book to help you meet these challenges. We look forward to seeing how you can apply the knowledge in your business context.
Jen Stirrup is the Founder and CEO of Data Relish, a UK-based AI and Business Intelligence leadership boutique consultancy delivering data strategy and business-focused solutions. Jen is a recognized leading authority in AI and Business Intelligence Leadership, a Fortune 100 global speaker, and has been named as one of the Top 50 Global Data Visionaries, one of the Top Data Scientists to follow on Twitter and one of the most influential Top 50 Women in Technology worldwide. Jen has clients in 24 countries in 5 continents, and her client list includes Microsoft, the NHS, the UK and Northern Ireland Governments, The Ashridge Hult Business School, CBS Interactive, O’Reilly and Virgin Atlantic.
She holds postgraduate degrees in AI and Cognitive Science. Jen has authored 3 books in data and artificial intelligence has been featured on CBS Interactive and the BBC as well as other well-known podcasts, such as Digital Disrupted, Run As Radio and her own Make Your Data Work webinar series.
Jen has also given keynotes for colleges, universities, as well as donating her expertise to charities and non-profits as a Non-Executive Director. Jen’s keynotes are about AI Leadership, Diversity and Inclusion in Technology, Digital Transformation and Business Intelligence. All of Jen’s keynotes are based on her two decades plus years of global experience, dedication and hard work.
Dr. Thomas J. Weinandy is a Research Economist at Upside, a digital promotions marketplace that increases the financial power of people and businesses in the real world. There he develops data-driven thought leadership at the intersection of consumer behavior and macroeconomic trends, particularly for the grocery, fuel, and restaurant industries.
He previously worked as a Data Scientist at BlueGranite, where he integrated machine learning with business intelligence for organizations across various sectors and countries. In addition to his role as a consultant, he hosted the popular “AI in a Day with Power BI” webinar series with over 25,000 online viewers.
Dr. Weinandy received a Ph.D. in Applied Economics from Western Michigan University, an M.B.A. from Wheeling Jesuit University, and a B.A. in Spanish and Social Entrepreneurship from John Carroll University. His dissertation was on “Applied Microeconomics and Business Intelligence in the Digital Age” that analyzed novel instances of digital technology mediating economic activity. He currently resides in Grand Rapids, Michigan, USA.
All of Jen’s keynotes are based on her two decades plus of global experience, dedication, and hard work. She has given keynotes to audiences across the globe and at several industry leading events including:
AI is a significant and timely topic for businesses, and there is much interest in adopting it. Overall, the adoption of AI in businesses is on a robust upward trajectory, with its impact felt across every industry. AI technology enhances efficiency and productivity, drives innovation, and changes industry landscapes.
Workflows in Power BI using AI
How are dataflows created?
Things to note before creating the workflows
Streaming dataflows and automatic aggregations
Getting your Data Ready First
Getting data ready for Dataflows
Where should the data be cleaned and prepared?
Real Time data ingestion vs batch processing
Real time Datasets in Power BI
Importing Batch Data with Power Query in Dataflows
The Dataflow Calculation Engine
Dataflow Options
DirectQuery in Power BI
Summary
What is a Data Model?
Why Is Data Modeling Important?
Why are data models important in Power BI?
Why do we need a data model for AI?
Advice for setting up a data model for AI
Data Modelling Disciplines to support AI
Data Vault
Data Modelling Versus AI Models
Data modeling in Power BI
What do Relationships Mean for AI?
Power BI Flat File Structure vs Dimensional Model Structure
What Is a Data Strategy?
Artificial Intelligence in Power BI Data Visualization
The Power BI Decomposition Tree
Power BI Key Influencer Visuals
Q&A Visual
Insights Using AI
Using AI to Reduce Cognitive Load
Automated Machine Learning (AutoML) in Power BI
Cognitive Services
Data Modeling
Real-World Problem-Solving with Data
Binary Prediction
Classification
Regression
Practical Demonstration of Binary Prediction to Predict Income Levels
Gathering the Data
Create a Workspace
Create a Dataflow
Model Evaluation Reports in Power BI
Prediction Report
Accuracy Report
Training Report
Summary
Text as Data
Limitations of Text Analytics
Demo Part 1: Ingest AirBnB Data
Language Detection
How It Works
Performance and limitations
Demo Part 2: Language Detection
Key Phrase Extraction
How It Works
Performance and Limitations
Demo Part 3: Key Phrase Extraction
Sentiment analysis
How It Works
Recommendations and Limitations
Demo Part 4: Sentiment Analysis
Conclusion
Demo Part 5: Exploring a Report with Text Analytics
Images as Data
Deep Learning
A Simple Neural Network
Image Tagging for Business
How It Works
Limitations of Vision
Demo Part 1: Ingest AirBnB Data
Demo Part 2: Image Tagging
Demo Part 3: Exploring a Report with Vision
Conclusion
AI Business Strategy
Organizational Learning with AI
Successful Organizational Behaviors
Custom Machine Learning
Machine Learning versus Typical Programming
Narrow AI versus General AI
Azure Machine Learning
Azure Subscription and Free Trial
Azure Machine Learning Studio
Demo 9-1: Forecasting Vending Machine Sales
Creating an Azure Machine Learning workspace
Training a Custom Model in Azure Machine Learning Studio
Deploying a Custom Model in Azure Machine Learning
Consuming a Custom Model in Power BI
Summary
About the Authors