📘 1. What Is Data & Analytics in Business?
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Data & Analytics is the process of collecting, analysing, interpreting, and using data to improve decision-making, increase efficiency, and gain a competitive advantage.
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It transforms raw data into actionable insights.
Example:
Amazon uses customer purchase data to recommend products and optimise inventory.
📗 2. Types of Data
| Data Type | Description | Example |
|---|---|---|
| Structured | Organised, easily searchable | Sales figures in Excel |
| Unstructured | Raw, unorganised | Social media posts, emails |
| Quantitative | Numerical data | Revenue, click-through rate |
| Qualitative | Descriptive/non-numeric | Customer reviews, survey comments |
| Internal | Generated within the organisation | Website traffic, employee KPIs |
| External | From outside sources | Market trends, competitor prices |
📙 3. Data Analytics Process
Step-by-Step Framework (DIKW Pyramid):
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Data → Raw facts (e.g., “100 users visited the site”)
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Information → Organised data (e.g., “100 users visited via mobile”)
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Knowledge → Insights (e.g., “Most mobile visitors are from Nairobi”)
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Wisdom → Decision (e.g., “Improve mobile site speed for Nairobi region”)
Analytics Lifecycle:
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Define the Problem/Goal
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What decision are you trying to make?
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E.g., “Why are sales declining in Q2?”
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Collect Data
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Use sources like CRM, Google Analytics, POS systems, or surveys.
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Clean & Prepare Data
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Remove duplicates, correct errors, and format properly.
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Analyze Data
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Use tools like Excel, SQL, Python, Power BI, or Tableau.
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Interpret Results
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What story does the data tell? What are the trends?
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Take Action
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Implement changes based on insights.
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📕 4. Types of Business Analytics
| Type | Description | Example |
|---|---|---|
| Descriptive | What happened? | “Sales dropped by 20% in May.” |
| Diagnostic | Why did it happen? | “Due to reduced ad spend.” |
| Predictive | What will happen? | “Sales expected to rise next quarter.” |
| Prescriptive | What should we do? | “Increase social media marketing.” |
📘 5. Key Metrics & KPIs by Department
| Department | KPIs/Examples |
|---|---|
| Marketing | Click-through rate (CTR), conversion rate |
| Sales | Sales growth, revenue per customer |
| Finance | Net profit margin, ROI |
| HR | Employee turnover rate, training ROI |
| Operations | Inventory turnover, order fulfilment time |
📗 6. Popular Tools for Data & Analytics
| Tool | Use Case |
|---|---|
| Excel | Basic analysis, pivot tables |
| Google Analytics | Web traffic & behaviour |
| Power BI / Tableau | Data visualisation & dashboards |
| SQL | Query databases |
| Python / R | Advanced analytics, automation |
| Google Data Studio | Real-time, shareable reports |
| CRM Tools | Sales & customer analytics (e.g., Salesforce) |
📙 7. Real-World Examples
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Netflix uses viewing data to recommend shows and produce new content.
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Spotify tracks listening habits to create personalised playlists.
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Zara uses real-time sales data to adjust its inventory weekly.
✅ Summary Table
| Area | Description | Example |
|---|---|---|
| Data Types | Structured, unstructured, internal, external | Sales logs vs. social media mentions |
| Analytics Types | Descriptive, Diagnostic, Predictive, Prescriptive | Diagnosing the sales drop causes |
| Tools | Excel, Power BI, SQL, Google Analytics | Tableau for visual dashboards |
| Application Areas | Sales, marketing, finance, HR | Analysing customer churn in a subscription app |
📌 Final Thought:
"Without data, you're just another person with an opinion."
— W. Edwards Deming
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