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Storytelling with Data
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2025
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Pages
226
Published
2014
Data Analytics
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Business Intelligence and Data Mining
A practical introduction to data analytics concepts, methods, and tools for business professionals
Build a solid foundation in business intelligence and data mining so you can turn raw data into decisions that actually move the needle.
Business Intelligence and Data Mining gives you a structured, jargon-light entry point into one of the most in-demand skill sets in modern business. Covering the full arc from foundational concepts to practical tools, the book equips analysts, managers, and aspiring data professionals with the vocabulary, frameworks, and techniques needed to extract meaning from data and communicate findings that drive real organizational decisions.
About this book
Data surrounds every modern organization, but raw data does nothing on its own. The gap between having data and making decisions from it is where business intelligence and data mining live. This book closes that gap for readers who are new to the field or who need a clear, structured foundation before going deeper into tools and techniques.
Anil Maheshwari builds the subject from the ground up. You start with what business intelligence actually means in practice, how data warehouses are organized, and why the architecture behind a BI system determines what questions you can and cannot answer. From there, the book moves into data mining: the methods used to find patterns, build predictive models, and segment customers or markets in ways that inform strategy rather than just confirm hunches.
The treatment is deliberately accessible. Formulas appear where they clarify, not to impress. Each concept is tied back to a business context so you always know why a technique matters, not just how it works. This makes the book equally useful for a business manager who needs to evaluate BI proposals and for an analyst who is building their first reporting pipeline.
Topics covered include:
- Data warehousing concepts including star schemas, fact tables, and OLAP cubes
- The ETL process and why data quality upstream determines insight quality downstream
- Core data mining techniques: classification, clustering, association rules, and regression
- Evaluation methods for assessing model accuracy and avoiding overfit
- Text mining and web analytics as extensions of the core toolkit
- Practical guidance on selecting and applying BI tools in an organizational setting
By the final chapter you will have a working mental model of the entire analytics pipeline, from raw source data through transformation and storage to visualization and decision support. That model gives you a durable frame of reference that holds up as tools and platforms evolve.
Whether you are entering a data analyst role, managing a team that produces BI reports, or taking a graduate course that needs a readable primary text, this book gives you the conceptual grounding to participate confidently in data-driven conversations.
🎯 What you'll learn
- Explain how data warehouses are structured and why that structure shapes what analysis is possible
- Trace the full data pipeline from source systems through ETL into a reporting layer
- Apply core data mining methods including classification, clustering, and association rule mining to real business problems
- Evaluate predictive models using appropriate accuracy metrics and avoid common pitfalls like overfitting
- Distinguish between OLAP, descriptive analytics, and predictive analytics and choose the right approach for a given question
- Interpret and communicate data mining results to non-technical stakeholders
- Assess BI tool options against organizational needs and data maturity
👤 Who is this book for?
- Business analysts and managers who work with BI reports but want to understand what is happening under the hood
- Students in MBA or graduate programs taking a first course in data analytics or business intelligence
- Aspiring data professionals who need a solid conceptual foundation before learning specific tools
- IT professionals supporting BI systems who want the business context behind the technology they maintain
- Consultants and project managers who evaluate or commission analytics work and need to ask the right questions
Table of contents
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01
Introduction to Business Intelligence
Defines business intelligence in concrete terms and maps the landscape of tools, roles, and organizational contexts where BI creates value. You establish a shared vocabulary that carries through the rest of the book.
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02
Data Warehousing Fundamentals
Covers the architecture of data warehouses including star schemas, snowflake schemas, fact tables, and dimension tables. You learn why design decisions made at this stage constrain every downstream analysis.
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03
ETL and Data Quality
Walks through the extract, transform, and load process and explains how data quality problems introduced early propagate through the entire analytics pipeline. You examine common data quality issues and how to detect them.
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04
OLAP and Multidimensional Analysis
Introduces Online Analytical Processing, the cube model, and operations like drill-down, roll-up, and slice-and-dice. You see how OLAP differs from transaction processing and when it is the right tool for a reporting task.
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05
Introduction to Data Mining
Defines data mining, situates it within the broader KDD process, and surveys the main families of techniques. You build a mental map of the field before going into any single method in depth.
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06
Classification and Prediction
Covers decision trees, naive Bayes, and logistic regression as classification methods, working through the logic of each with business examples. You also learn how to evaluate model performance using confusion matrices and accuracy metrics.
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07
Clustering and Segmentation
Introduces k-means and hierarchical clustering and shows how segmentation results translate into actionable business strategy. You work through the process of choosing the right number of clusters and interpreting the output.
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08
Association Rules and Market Basket Analysis
Explains support, confidence, and lift and applies association rule mining to retail and e-commerce scenarios. You learn how to filter large rule sets to surface the ones that are genuinely useful rather than trivially obvious.
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09
Text Mining and Web Analytics
Extends core data mining concepts to unstructured text and clickstream data. You see how sentiment analysis, term frequency analysis, and web log mining fit into the same analytical framework as structured data techniques.
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10
BI Strategy and Implementation
Addresses how organizations select, deploy, and govern BI systems, covering tool evaluation, change management, and success metrics. You finish with a framework for assessing BI maturity and planning the next step in an organization's analytics journey.
Frequently asked questions
Do I need a background in statistics or programming to read this book?
No prior programming or advanced statistics background is required. The book is written for business professionals and graduate students encountering these topics for the first time. Mathematical concepts are introduced only where they add genuine clarity.
Is this book better suited for self-study or as a course textbook?
It works well in both contexts. The structure follows a logical learning progression that maps naturally onto a semester course, and each chapter is self-contained enough for self-directed readers to pause and return without losing the thread.
Does the book cover specific BI software tools like Tableau, Power BI, or Python?
The book focuses on concepts and methods rather than any single vendor or platform. It gives you the foundation to evaluate and learn specific tools, but hands-on software tutorials are not the primary focus.
Is the content still relevant given that it was published in 2014?
The core concepts of data warehousing, ETL, and data mining methods covered here are foundational and have not changed materially. Specific platform examples may be dated, but the conceptual framework remains sound and widely applicable.
Who is this book not a good fit for?
If you are already working as a data scientist or have hands-on experience with machine learning frameworks, this book will feel introductory. It is designed as a first text, not an advanced reference.
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