Reading Time: 11 minutes

The average enterprise has data in more than 1,000 applications, yet only 29% of them are connected. If disconnected data is slowing you down, you're in the majority: Nine out of 10 IT leaders say that having data in various silos creates business challenges that impact the

As businesses increasingly generate enormous amounts of data, it's often kept in disparate places. Plus, it's not always available to everyone in the company, nor is it always stored in formats that are meaningful or useful. 

Employees in every department in every organization globally are generating data. To improve enterprise-wide efficiency, that data must be readily available to the rest of the organization – but it often isn't. A strong data integration plan is key to resolving these challenges. 

Understanding the importance of data is the first step toward getting your organization on track for accurate, complete, and consistent data that is easily accessed and used for better business intelligence.

What is data integration?

Data integration is the process of aggregating data from multiple sources and combining it into a single view. Not only is the data unified and cohesive, it's also checked to identify errors and inconsistencies that are resolved before being integrated into a single dataset or data warehouse. 

By unifying data streams from disparate databases, applications, and platforms, organizations gain a comprehensive view of their operations and customers. This single, unified view uncovers patterns and insights that might not otherwise be apparent, allowing for more well-informed decisions and better customer experiences. 

Why is data integration important?

Data integration is essential because it allows businesses to make the best use of their data across the enterprise. Consolidating data from a wide range of sources lets users see a coherent, real-time view of business performance across the company. It's a first step toward shaping data into useful and meaningful information. It allows for better efficiency and more accurate, data-driven decision-making.

When data is not integrated, gathering meaningful information for analytical or operational needs takes many more time-consuming steps. You may need to log into multiple accounts on multiple sites, access data within various native apps, copy data, reformat it, and cleanse it – all before you can begin to consider analyzing it. 

Benefits of data integration

Data integration eliminates the need to manually gather data to run a report or build an application. Using the right tools to integrate data, instead of having to do it manually, saves time that can better be spent elsewhere, such as on the analysis and execution that makes an organization more competitive. 

There are many benefits of data integration. These include:

  • Easy and quick connections between various data stores
  • Easier data integration and accessibility for a more comprehensive view of your company
  • Improved ability to identify , patterns, and issues
  • Real-time business insights, intelligence, and analytics
  • Reduced costs because you don't need to maintain multiple data silos 

How does data integration work?

Data integration entails routing data from disparate sources to a target system and transforming it. There are three main approaches to integrating data, each with advantages and disadvantages. Choosing an approach means considering the specifics of your data integration, such as data quality, complexity, scalability, and cost.

  • Extract, transform, and load (): ETL is the traditional approach to data integration and represents extracting data from its sources, transforming it into your standard structure and format (e.g. making sure all dates are written the same way), and loading it to the target destination, such as a data warehouse or data lake.
  • Extract, load, and transform (ELT): This newer approach to data integration involves extracting data, loading it into the target destination first, and then performing the transformation on demand. 
  • Data virtualization: Data virtualization creates a virtual layer that integrates data from a source without moving or copying it. It provides a unified view to users. 

Data integration use case examples 

Several common data integration use cases can apply to various enterprise types:

  • Data consolidation: A single source of truth (SSoT) across all of your systems inside a data lake or warehouse
  • Data visualization: Dashboards that slice and dice data from the single source of truth for specific needs
  • Actionable insights: A 360-degree view of customers that surfaces relevant information. 

Data integration best practices

Integration is necessary to enable the connected experiences customers now expect. Yet 88% of businesses face integration challenges that get in the way; as the number of IT projects continues to increase year over year, barriers to integration will continue to block digital transformation.

As you determine the scope and shape of your  data integration project, consider several best practices:

  • Clean your data: The insights derived from your data are only as good as the data used to build those insights. Create a plan to sweep your data and update it periodically.
  • Appoint a data steward to ensure the data is kept clean, following ground rules about the data you collect and where it is stored.
  • Future-proof your systems: Your data integration system should be adaptable to future business changes. Less custom code leads to fewer headaches down the line. 
  • Establish consistent governance among APIs and connectors: This will ensure data quality and security.
  • Test processes and results regularly.
  • Plan to review and update your data integration processes regularly.

Challenges in building a data integration system

While it's fairly straightforward to determine the results you want from a data integration process, figuring out how to make that happen can be challenging. Successfully integrating data from various sources means knowing what data you need to collect and where it resides. 

Some questions to ask yourself include: 

  • Is some of your data stored in legacy systems and therefore missing markers, such as times and dates for activities, that newer systems generally have? 
  • Are you integrating data from videos, IoT devices, sensors, and the cloud? 
  • Does your data reside with external sources as well as internal ones? 
  • How will you keep up with your data integration after the system is up and running?

Data integration tools

While the idea of pulling all your data into one place may seem straightforward, it's a process that can be complex if you don't use the right tools.

There are various tools and technologies to conduct data integration. These include databases, data platforms, data pipelines, cloud services, and data integration software. Find the tools and technologies that work for your company to conduct data validation, data cleansing, data mapping, data transformation, data governance, data orchestration, and data security.