Data Analytics Framework and Data Types
Foundations
Data Analytics Framework and Data Types
Syllabus tag: KASNEB CPA | Advanced Level | CA34S1 Business Data Analytics
1. What is data analytics?
Data analytics is the process of examining, cleaning, transforming, and modelling data to discover useful information, draw conclusions, and support decision-making. In a business context, it enables organisations to move from intuition-based decisions to evidence-based decisions — identifying patterns, predicting outcomes, and optimising processes.
2. The analytics maturity model
Four levels of analytics sophistication:
| Level | Type | Question answered | Example |
|---|---|---|---|
| 1 | Descriptive | What happened? | Monthly sales report |
| 2 | Diagnostic | Why did it happen? | Root cause of a sales decline |
| 3 | Predictive | What is likely to happen? | Revenue forecast for next quarter |
| 4 | Prescriptive | What should we do? | Optimal pricing strategy |
Most organisations begin at descriptive analytics and progress to predictive and prescriptive over time.
3. Data types
Structured data: organised in rows and columns (tables) — easily stored in relational databases. Examples: financial transaction records, customer databases, payroll data. Amenable to traditional SQL-based analysis.
Unstructured data: no predefined schema — text, images, audio, video, social media posts. Requires special processing techniques (Natural Language Processing for text, computer vision for images).
Semi-structured data: has some organisational properties but does not conform to a rigid tabular structure. Examples: JSON, XML, email, HTML. Common in APIs and web data.
4. Data sources
Internal sources: ERP systems (SAP, Oracle), CRM systems, HR systems, financial accounting systems, operational databases, IoT sensors. External sources: market data providers, social media, government open data portals, competitor databases, weather data, economic statistics (KNBS, World Bank).
5. The data analytics process (CRISP-DM)
The Cross-Industry Standard Process for Data Mining provides a structured approach:
1. Business understanding — define the problem and objectives
2. Data understanding — collect data and explore its characteristics
3. Data preparation — clean, transform, and integrate data
4. Modelling — apply statistical or machine learning models
5. Evaluation — assess model performance against business objectives
6. Deployment — implement insights in business processes
6. Data roles in an organisation
Data analyst: focuses on descriptive analytics — reports, dashboards, KPI tracking. Data scientist: builds predictive and prescriptive models using statistical and machine learning techniques. Data engineer: builds and maintains data infrastructure (pipelines, warehouses). Business intelligence (BI) developer: designs and maintains dashboards and reporting systems.