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Data Integration

Data helps businesses make better decisions, provide a better customer experience, and increase efficiency. But today, data is distributed across countless sources, bringing new complexities for businesses large and small. Learn what data integration is, how it works, major benefits, and how to choose the best data integration system.

What is Data Integration, and How Does it Work?

Data Integration Explained

Data integration is the process of combining data from various sources into one, unified view for effecient data management, to derive meaningful insights, and gain actionable intelligence.

With data growing exponentially in volume, coming in varying formats, and becoming more distributed than ever, data integration tools aim to aggregate data regardless of its type, structure, or volume. It is an integral part of a data pipeline, encompassing data ingestion, data processing, transformation, and storage for easy retrieval.

Why Does it Matter?

Organizations are moving to become more data-driven, yet data sources are more distributed and fragmented than ever before. By connecting systems that contain valuable data and integrating them across departments and locations, organizations are able to achieve one-point data storage and access, data availability, and data quality.

Integrated data unlocks a layer of connectivity that businesses need if they want to compete in today’s economy. By connecting systems that contain valuable data and integrating them across departments and locations, organizations are able to achieve data continuity and seamless knowledge transfer. This benefits the company as a whole, not just a team or individual, promoting intersystem cooperation.

Benefits of Data Integration

When systems are properly integrated, collecting data and converting it into its final, usable format takes less time and allows organizations to make better choices based on deeper understanding of their business data.

  • Data integrity and data quality
  • Seamless knowledge transfer between systems
  • Easy available, fast connections between data stores
  • Increased efficiency and ROI
  • Better customer and partner experience
  • Complete view of business intelligence, insights, and analytics
  • Ultimately, data integration allows for a full overview of business

processes and performance - from sales, marketing, customer service, website activity, and analytics, to IT systems, applications, and software, providing intersystem cooperation, actionable insights, and operational efficiency.

Real-Life Examples and Use Cases

Real-Life Examples & Use Cases

To explain how data integration works, we'll bring a real life example of how a medium-sized business would integrate data.

Typically, businesses large and small use numerous disparate systems to run its operations. Combining that data could include integrating user profiles, sales, marketing, accounting, and application or software data to get a full overview of their business. For example, one small business could use:

  • Salesforce for customer information and sales data
  • Google Analytics for customer tracking, user and website analytics
  • MySQL database for storing user information
  • Quickbooks for expense management

Because each data storage system is different, the data integration process includes data ingestion, cleansing/transforming data, and unifying it into a single data store. A complete data integration solution would not only integrate data, it’d allow this data to be readily available while maintaining data integrity and quality for reliable insights and better collaboration.

In this next example, we'll delve into enterprise data integration by using a Fortune 10 company - Walmart. Seamlessly integrating data across a large, enterprise retailer with 20,000 brick-and-mortar store locations, a massive online website, millions of items in inventory, mobile apps, global data, and 3rd party resellers becomes yet another level of complexity.

Not only do they need to collect data across every customer, store, warehouse, website, and application, they need real-time data integration in order to function properly at scale.

Each one of these systems stores its own repository of information related to the company’s operations. Because each data storage system is different, the data integration process includes data ingestion, cleansing/transforming data, and unifying it into one seamless stream of data.

Due to Walmart’s need for reliable, real-time data integration on mass scale, they turned to Apache Kafka to integrate data across globally distributed systems, process, analyze, and stream data in real-time to ensure accurate, real-time tracking, inventory management, analytics, and machine learning.

Learn more about how Walmart uses Apache Kafka for data integration at scale.

Try Confluent

Start integrating data at scale by downloading Confluent, the leading distribution of Apache Kafka and the most powerful enterprise data integration and real time data platform in the industry.