Data Integration: Benefits and Examples

Benefits of Data Integration

• It provides a holistic view of business operations
• It improves decision-making
• Enhances data accuracy
• Ensures flexibility and scalability
• Allows for data enrichment

Data Sources

• Include point-of-sale (POS) systems
• E-commerce platforms
• CRM systems
• Financial systems
• Electronic Health Records (EHR)
• Staff scheduling systems
• Patient feedback systems

Examples of Data Integration

• Integrating sales data and inventory data from physical and online stores
• Linking customer data with financial performance to identify profitable customer segments

Improved Decisions

• Operational efficiency improves by correlating patient feedback with staff scheduling
• Resource allocation improves by combining patient data and financial systems to analyze treatment effectiveness

Validating Production Output

• Production data includes products finished, while inventory data tracks goods entering the warehouse
• Validation compares produced goods with warehoused goods
• Discrepancies signal missing inventory or data entry mistakes

Raw Materials vs Production Output

• Validation ensures the correct quantity of raw materials is used for each unit produced
• It is also used for calculating production efficiency or potential waste

Flexibility and Scalability

• Achieved by connecting to diverse sources like local files, databases, cloud services, and web data
• Businesses can integrate new data systems seamlessly as they grow

Data Enrichment

• Involves combining different data types
• Helps unlock new insights by revealing hidden patterns and correlations

Structured Data

• Organized and follows a defined schema
• Structured data is stored in relational databases like SQL Server
• Examples include customer databases with fields, financial records with sales figures, expenses, and revenue

Semi-Structured Data

• Data that doesn't fit into traditional rows and columns but has some organizational structure
• Common formats include JSON, XML, and HTML

JSON Structure

• Objects are collections of key-value pairs
• Arrays are ordered lists of values

XML vs JSON

• XML tags data with opening and closing tags like <name>John Doe</name>
• JSON uses key-value pairs and curly braces like "name": "John Doe"

Unstructured Data

• Includes text, images, videos, audio files, and documents
• Requires tools like AI and NLP to extract information
• Examples include emails, social media posts, chat logs, images, videos, PDF documents, and sensor data

Power BI and Unstructured Data

• Power BI can handle unstructured data with limitations
• Unstructured data like text, images, and social media content requires pre-processing or external tools before Power BI analysis

File-Based Data Sources

• Power BI connects to common file formats
• Excel: Import spreadsheets and tables for quick analysis
• CSV/Flat Files: Import structured data in comma-separated format
• XML/JSON: Parse and import semi-structured data

Relational Databases

• Connect to traditional databases like SQL Server, MySQL, PostgreSQL, and Oracle
• Support both Import and DirectQuery modes

Relational Database Example

• Tables include "users," "ratings," "movies," and "tags"
• Relationships are defined between tables using keys

Key Properties of Relational Databases

• Data organization: Data is organized into tables (schema)
• Keys: Each table has a unique identifier (Primary Key) and can be linked to other tables through Foreign Keys
• Management: Relational databases are managed using SQL (Structured Query Language) for querying, updating, and managing data

Cloud-Based Data Sources

• Power BI integrates with cloud platforms like Azure SQL Database, Google BigQuery, and Amazon Redshift
• These provide scalability and flexibility for real-time data analysis
• Accessing large datasets in Google BigQuery can help visualize website traffic trends

Web Data Sources

• Data can be imported directly from web pages
• Web scraping capabilities are usable
• Support importing data in tabular formats
• Stock market websites can be scraped to capture price movements

API Data Sources

• Custom data integrations are created with API endpoints
• Real-time or scheduled data retrieval is possible
• APIs are ideal for accessing non-standard data sources
• Weather data is integrated using public weather APIs for forecasting in business dashboards

REST API Model

• Involves a client, REST API, and server communicating via HTTP requests and responses
• It uses methods like GET, POST, PUT, and DELETE and data formats like JSON, XML, and HTML

DirectQuery

• Rarely used for REST APIs
• Designed for databases
• Can be enabled indirectly using tools like APIs connected to a SQL or Azure service (e.g., Azure Synapse)

Power Query

• An engine in Power BI (and Excel) that cleans, transforms, and prepares data before loading it into Power BI for analysis
• Uses the M Language for defining queries and data transformations

Power Query Functionalities

• Data Transformation: Filter & Sort, Merge & Append, Split & Combine Columns
• Data Cleaning: Remove Duplicates & Errors, Change Data Types, Text and Date Functions
• Automation: Power Query saves each transformation step

Import Mode

• Data behavior: The data is fetched and stored in the Power BI (.pbix) file
• Visualizations run against the imported data
• Data becomes static after import, requiring manual or scheduled updates
• Best for static datasets

Direct Query Mode

• Data is not stored in Power BI, queries are sent to the source
• Power BI generates SQL queries to retrieve data
• Visualizations reflect the latest data
• Recommended for frequently changing data or when data has large size or sensitivity

Best Practices for Managing Large Datasets

• Use DirectQuery for large databases
• Aggregate data to improve performance
• Partition data to improve performance
• Use incremental refresh
• Optimize DAX calculations

Managing Large Datasets Example

• A retail company used DirectQuery for a customer transaction database (over 100 million rows) stored in SQL Server
• The benefit was that it queried data only when necessary when creating reports
• Faster performance and reduced memory usage