What Is Data Integration? Examples & Use Cases

Your organization likely captures data from many sources and in many formats, with one thing in common: It all has business value. Bringing diverse data together in a way that supports decision-making and helps delight customers can deliver a major competitive advantage. In fact, 91% of executives responsible for data strategy say their firms delivered measurable business value from data and analytics investments in 2023, according to advisory firm NewVantage Partners.

High on the list of worthy investments is an integration process that leaves no data behind. After all, you never know which bits of information will spark your next multimillion-dollar idea. The challenge is that data may be structured, unstructured, in the cloud, locked in a local spreadsheet or legacy application, or held by a third party. How can you unify it for decision-making in an affordable way?

What Is Data Integration?

Data integration is the process of extracting data from a variety of sources and loading it into a centralized repository in a format that is usable by the tools your decision-makers depend on, including analytics tools and ERP and CRM systems. Data integration is what enables leaders to make informed choices based on a holistic view of data.

Three key business systems depend on integrated data and comprise the most common enterprise use cases:

• Holistic analytics systems power data-driven decision-making using a unified view of organizational information, including on customer behavior. By analyzing consolidated data, analytics systems tease out patterns that would be difficult or impossible to spot using isolated data sources. And, since they’re based on your data, these insights are unique to your organization, meaning analytics tools are a prime source of inspiration for growth opportunities. Once a product or service is launched, you can use the system to track key performance indicators and to discover both anomalies and additional opportunities.
• Customer relationship management (CRM) systems store customer data generated by sales, marketing, and customer service teams. With data integration, organizations can gain a complete view of customer and prospect preferences, interactions with the company, and behavior. Integrated customer data is key to the personalized marketing campaigns that are proving popular lately and to improving overall buyer satisfaction.
• Enterprise resource planning (ERP) systems manage a wide range of business processes, including ecommerce, finance, human resources, inventory, and supply chain. Data integration allows these systems to work from, and feed into, a common data repository. In ERP, this common data repository lets sales easily see available inventory, shows customer support agents ship dates, and lets finance teams project revenue for a given timeframe—a full view of what the business is doing now.

Data integration is commonly achieved via data warehouses, which are large repositories of structured and integrated data from various sources. A warehouse may be on-premises or in the cloud; the key is ensuring connectivity and data synchronization across both environments. That is often easier in the cloud, where data collection can happen 24/7/365 from internal and external sources. It’s a more scalable model as well.

Data integration is vital for the effective decision-making, efficient operations, and enhanced customer experiences that deliver a competitive edge.

Data Integration FAQs

What are examples of data integration?

An example of data integration is using customer information from various channels, such as website visits, order history, social media, and customer service interactions, to craft a personalized marketing campaign that delivers a better return on investment than a generic offer. Internally, integrating data from different business systems, like sales, supply chain, production, and finance, lets decision-makers craft more accurate budgets. Connecting CRM software with an accounting system ensures consistent and up-to-date customer information.

What is data integration vs. ETL?

Data integration and ETL (extract, transform, and load) both address combining data from diverse sources. However, data integration refers to the overall process of combining data from multiple sources and making it accessible and usable across an organization. ETL refers to the technical process of extracting data from different sources, transforming it into a consistent format, and loading it into a database or data warehouse. ETL focuses on the technical aspects of data movement and transformation that are critical to data integration.