Data & Analytics | Quantum Valley

What is Data?

Data is raw information collected from users, systems, applications or devices. Examples include website visits, login records, transaction details and application logs.

What is Data Analytics?

Data Analytics is the process of examining and analyzing data to discover patterns, trends and useful insights that help organizations make decisions.

Data Types

Structured Data

Structured data is organized in rows and columns and stored inside databases. It follows a fixed schema and can be easily searched using SQL queries. Examples: customer tables, login records, bank transactions, employee databases.

Semi-Structured Data

Semi-structured data does not follow strict tables but still has some organization using tags or keys. Commonly used in APIs and web applications. Examples: JSON files, XML responses, API responses and configuration files.

Unstructured Data

Unstructured data has no predefined format and cannot be stored in traditional database tables. It requires processing tools to analyze. Examples: images, videos, emails, chat messages and social media posts.

Data Pipeline

A data pipeline shows how system information travels inside an application:

Data → Logs → Processing → Dashboard → Alert

Application generates events
Events are written into logs
Logs are processed by monitoring systems
Dashboards display system health
Alerts are triggered when errors occur

Monitoring & Logs

Application Logs: Track user activity like login, logout, API requests.
Error Logs: Capture failures such as server errors, database errors, authentication failures.
Monitoring Alerts: Trigger notifications when system crashes, high CPU usage or service downtime occurs.

Tools Used

SQL: Querying database records to verify users, transactions and login issues.
Excel: Filtering and analyzing exported reports and user data.
Python: Running small scripts for log analysis and automation.
Power BI / Tableau: Monitoring dashboards and visualizing system usage.
Log Monitoring Tools: Checking application logs, errors and alerts (ex: Datadog, Splunk).

Why Data Analytics is Important

Helps companies understand customer behavior
Improves product performance
Detects fraud and security issues
Supports business decision making