CCSP: Emerging Technologies: 4 Types of Data Analytics

Jan 02, 2025

Data analytics versus data analysis

Data analytics is the process of using specialised tools and techniques to find value from raw data.

Data analysis is the practice of interpreting data that leads to meaningful decisions.

Data analytics helps companies gain more visibility and a deeper understanding of their processes and services.
It gives companies detailed insights into the customer experience and customer problems.
By shifting the paradigm beyond data to connect insights with action, companies can
- Create personalised customer experiences
- Build related digital products
- Optimise operations
- Increase employee productivity

There are four types of data analytics, each serving different purposes and using different techniques.

Descriptive analytics helps to answer what happened. It uses several visualisation techniques, including

Pie charts - Diagrams that show data as slices in a circular-shaped graph.

Bar charts - Diagrams that show data in rectangular bars, horizontally or vertically.

Line graphs - Diagrams that use lines to connect single data points generally plotted over a period of time.

Tables - Diagrams that show data in rows and columns.

Generative narratives - You ask questions about your data and receive answers with visualisations.

Diagnostic analytics helps to answer why it happened. It uses several techniques, such as:

Drill-down: Seeing an overview of the data, to a detailed view within the same dataset.
Data discovery: The process of gathering, cataloging, and classifying data from different databases for analytics.
Data mining: Using analytics against a large dataset to discover meaningful insights.
Correlations: A measure between two variables that shows how closely-related they are without stating a cause-and-effect relationship.

Predictive analytics helps answer what might happen in the future. It uses techniques such as

Machine learning (ML): A technique that teaches software how to
- Learn from data
- Find patterns
- Make decisions with minimal human intervention
Forecasting: Predicting the future value by looking at unique trends.
Pattern matching: Finding pre-determined patterns in raw data.
Predictive modelling: Predicting future events by analysing patterns with input data.

Prescriptive analytics recommends actions to the predicted outcome. It uses techniques, such as:

Graph analysis: Analysing relationships between objects in a network or graph.
Simulation: Modelling the behaviour of a system by using the characteristics and relationships of system components.
Complex event processing: Deriving a conclusion through tracking and analysing streaming data.
Neural networks: A machine learning model inspired by the human brain, capable of learning and solving complex problems.
Recommendation engines: A system that uses ML to recommend the most relevant items to a user, based on behaviour patterns.