Sale

Business Analytics Explained

Name: Business Analytics Explained
SKU: 80221
Availability: InStock

Price range: $49 through $197

★★★★★ (4.93)

Loved by our amazing community

How to run a business on evidence instead of instinct, even when the data is messy, the dashboards lie, and the deadline is now, explained chapter by chapter across 111 pages of usable knowledge.

Master proven frameworks including the DIKW Pyramid, the four analytics types, the North Star Metric framework, and Bayesian decision-making
Use practical tools like the data quality audit, the question framing matrix, root cause analysis, and the analytics decision chain
Learn from real failure patterns where dashboards mislead, vanity metrics dominate, and correlation gets confused for causation
Diagnose where your analytics actually breaks down, from data quality at the source to insight that never reaches a decision

AS FEATURED IN

Choose one of the options:

Purchase options

All Products Special Offer

Single eBook Purchase

Clear

SKU: 80221 Category: eBooks

OR SAVE $4566 TODAY

Get all 66 products for $197

Risk-free Purchase: Full refund within 14 days

Paddle Safe Checkout

Risk-free: Full refund within 14 days

Paddle Safe Checkout

10 eBooks Included

1 × Customer Journey Blueprint

$99

1 × Strategic Thinking Blueprint

$60

1 × KPI Mastery Blueprint

$60

1 × Business Models Blueprint

$99

1 × Project Execution Blueprint

$99

1 × Content Creation Blueprint

$60

1 × Process Optimization Blueprint

$27

1 × Employee Retention Blueprint

$27

1 × Negotiation Gameplan Blueprint

$27

1 × Team Harmony Blueprint

$45

Description

Description

About Business Analytics Explained

Whether you are a founder, an executive, or an operator responsible for making decisions that move the business, this book gives you the frameworks to turn data into action with clarity and confidence.

This eBook walks you through how companies actually use analytics to make better decisions, from asking the right questions and auditing data quality to identifying root causes, applying predictive models, and embedding insight into the weekly decision rhythm. It covers analytics for enterprises and small businesses alike, not just companies with data teams and dedicated warehouses.

Covering decision framing, data quality, customer and financial analytics, predictive modeling, AI-powered analytics, and execution, it ensures you can build an analytics system that survives messy data, vanity metrics, and the pressure to decide before the numbers are ready. With real failure patterns and ready-to-use frameworks, this eBook becomes the go-to reference you reach for every time a decision needs evidence behind it.

Chapter 1: Why Analytics Fails and the Traps That Follow

The Core Failure: Data Everywhere, Decisions Still Running On Instinct
Reporting vs. Real Analysis: Why Producing Numbers Is Not The Same As Producing Insight
Vanity Metrics: The Measurements That Feel Good And Change Nothing
Why Dashboards Lie: The Gap Between What The Visualization Shows And What The Business Needs To Know
The Four Mid-Journey Traps: Overanalysis, False Precision, Tool Obsession, And Ignoring Business Context
Why More Data Without Better Questions Makes Decisions Worse, Not Better

Chapter 2: What Business Analytics Actually Means

The Only Definition That Matters: Turning Data Into Decisions That Change Outcomes
The DIKW Pyramid: Data, Information, Knowledge, Wisdom And Why Most Companies Never Get Past The First Level
The Four Analytics Types: Descriptive, Diagnostic, Predictive, Prescriptive And Where Most Companies Stop
The Decision Chain: Data To Insight To Decision To Action And What Breaks At Each Transition
Analytics As A System, Not A Tool: Why Software Purchases Without Process Changes Produce Nothing

Chapter 3: Asking The Right Questions

Why The Quality Of The Question Determines The Value Of The Analysis Before Any Data Is Touched
Starting With The Decision: Defining What Needs To Be Decided Before Defining What Needs To Be Measured
The Problem Framing Test: Three Signs Your Analytics Question Is Too Vague To Produce A Useful Answer
How To Reframe “What Does The Data Show” Into “What Decision Does This Data Need To Support”

Chapter 4: Data Quality

Why Bad Data Is More Dangerous Than No Data: The False Confidence Problem
The Four Data Quality Failures: Incomplete, Inconsistent, Outdated, And Untrustworthy At The Source
How To Audit Your Current Data Before Building Any Analytics System On Top Of It
The Data Quality Minimum: What Good Enough Looks Like Before Analysis Is Worth Running
When To Fix The Data And When To Acknowledge Its Limits Inside The Decision It Informs

Chapter 5: The North Star Metric Framework

What A North Star Metric Is: The Single Number That Best Captures The Value Your Business Delivers
Why Companies With Too Many Top-Level Metrics Make Slower Decisions And Build Misaligned Teams
How To Identify Your North Star: The Three Criteria It Must Meet To Qualify
The Relationship Between The North Star And Supporting Metrics: What Feeds It And What It Should Not Include
When The North Star Becomes The Wrong Metric And How To Know It Is Time To Change It

Chapter 6: Root Cause Analysis

The Core Principle: Analytics That Explains What Happened Without Explaining Why Is Incomplete
The Five Methods: The 5 Whys, Fishbone Diagram, Fault Tree Analysis, Pareto Analysis, And Change Analysis
How To Choose The Right Method Based On Problem Complexity And Available Data
The Depth Problem: Why Most Root Cause Analysis Stops One Level Too Early And Fixes The Symptom
From Root Cause To Decision: Translating The Finding Into A Change That Prevents Recurrence

Chapter 7: Correlation vs. Causation

Why Most Business Insights Drawn From Data Are Directionally Wrong
The Correlation Trap: How Two Numbers Moving Together Fools Analysts And Executives Equally
Three Tests For Causation: Temporal Precedence, Covariation, And Ruling Out Alternative Explanations
How To Present Uncertain Findings Honestly Without Killing Confidence In The Analysis
The Business Cost Of Acting On Correlation As If It Were Causation

Chapter 8: Bayesian Thinking

Why Waiting For Certainty Before Deciding Is Itself A Decision, Usually The Wrong One
The Bayesian Core: Start With A Prior Belief, Update It As New Data Arrives, Decide With What You Have
How To Apply Bayesian Thinking Without The Mathematics: The Practical Business Version
Where Bayesian Thinking Beats Traditional Analysis: Fast-Moving Markets, Limited Data, High-Stakes Bets
The Update Discipline: How To Build A Team Habit Of Revising Conclusions When Evidence Changes

Chapter 9: Visualization That Reveals Truth

Why Most Dashboards Are Built For The Person Who Built Them, Not The Person Who Decides
The Three Chart Types That Clarify And The Three That Consistently Mislead
The Decision-Maker Test: If The Visualization Does Not Immediately Suggest An Action, It Is Not Finished
How To Strip A Dashboard To The Minimum That Changes Decisions And Nothing More
The Difference Between A Report Someone Reads And An Insight Someone Acts On

Chapter 10: Customer Analytics

The Four Customer Metrics That Actually Explain Business Health: Acquisition Cost, Conversion Rate, Lifetime Value, And Churn Rate
Segmentation: How To Split Customers Into Groups Where The Behavior Is Different Enough To Change The Decision
Cohort Analysis: Tracking Customer Groups Over Time To Find What Aggregate Reporting Hides
Churn Prediction: The Leading Indicators That Appear Before A Customer Leaves And What To Do With Them

Chapter 11: Analytics For Finance

The Numbers That Actually Run The Business: Gross Margin, Contribution Margin, Cash Conversion Cycle, And Burn Rate
Why Financial Reporting Looks Backwards And How To Build Forward-Looking Indicators From The Same Data
Cost Analytics: Finding Where Money Is Spent vs. Where It Creates Value
The Unit Economics Test: The One Calculation That Tells You Whether The Business Model Works At Scale
How To Connect Financial Analytics To Operational Decisions Rather Than Leaving Them In The CFO’s Deck

Chapter 12: Predictive Analytics

What Predictive Analytics Is (And What It Isn’t)
The Four Business Use Cases Where Prediction Adds The Most Value: Demand Forecasting, Churn, Pricing, And Risk
Scenario Planning Without Overcomplicating: How To Build Three Futures And Assign Probabilities To Each
Where Predictive Models Fail: The Assumptions That Break When The Environment Changes
How To Present A Prediction To A Decision-Maker Who Does Not Trust Models

Chapter 13: AI-Powered Analytics

What Actually Changed In The AI Analytics Market, And The Five Things Vendors Only Pretend Changed
The New Definition: From “ML Models That Predict” To “AI Systems That Ask, Analyze, Decide, And Explain”
Conversational Analytics: Why Dashboards Are Losing Ground To Natural Language As The Executive Interface
Agentic Analytics: AI Agents That Run Analysis Loops, Surface Anomalies, And Propose Decisions On Their Own
Causal AI: The Move From Correlation To Cause And Effect, And Why Explainability Is Now Competitive
The Hallucination Problem: The Most Underdiscussed Risk In Enterprise AI, And The Validation Discipline Every Team Needs
The 2026 Analytics Stack: The Tool Categories That Did Not Exist Three Years Ago, And Where To Start Without Overspending
Synthetic Data: How Generative AI Is Closing The Analytics Gap For Data-Poor Businesses
The Three AI Applications Worth Deploying Now, By Company Stage
The Skill Shift: Why Knowing How To Write A Query Is Fading, And The Two Skills Replacing It
The Validation Discipline: The Four-Step Check Every AI Insight Should Pass Before It Informs A Decision
The Vendor Evaluation Framework: The Five Questions To Ask Any AI Analytics Platform Before Signing
The Strategic Question Every Leader Must Answer: Is AI A Multiplier On Your Analytics Function, Or A Replacement For Parts Of It

Chapter 14: Analytics For Small Businesses

Why Most Analytics Advice Assumes A Data Team, A Data Warehouse, And A Budget That Most Businesses Do Not Have
The Small Business Analytics Starting Point: Three Decisions That Repeat Every Week And The Data That Should Be Informing Each One
How To Build A Decision-Grade Analytics System With A Spreadsheet, One Dashboard, And No Analysts
The Five Metrics Every Small Business Must Track Before Adding Any Others
Customer Analytics At Small Scale: How To Understand Buyer Behavior Without Enterprise Tools Or A CRM Team
Financial Analytics For Operators: The Four Numbers That Tell You Whether The Business Is Healthy Before The Accountant Does
The Small Business AI Opportunity: The Three AI-Powered Analytics Tools That Are Accessible Now And Change Daily Decisions Immediately
How To Build The Habit: Embedding Analytics Into Weekly Decisions Without Making It A Separate Exercise

Chapter 15: Building Your Analytics System

The Analytics Audit: A Structured Diagnostic Of Your Current Metrics, Data Quality, And Decision-Making Gaps
Step 1: Define Your North Star Metric And Test It Against The Three Qualifying Criteria
Step 2: Map Your Top-Level Metric Down To The Drivers Your Team Can Actually Influence
Step 3: Run The Data Quality Audit And Fix The Foundation Before Building Anything On Top Of It
Step 4: Map Your Top Five Decisions To The Data They Currently Use And The Data They Should Be Using
Step 5: Embed Analytics Into Your Weekly Decision Rhythm So Insight Produces Action, Not Just Reports
The 30-Day Quick Wins
The 60-Day Foundations
The 90-Day Build
One-Page Analytics System Summary Template