by Luc Leblanc | April 06, 2022

What is Data Integration?

Data integration is the process of uniting data from multiple sources into a single source of information. This data management solution allows organizations to move and transform raw data from disparate applications and systems to a data storage solution to allow end users to access data from a single source. As a result, teams can easily distill their data into relevant, actionable insights.

Enterprise data can often become fragmented and unorganized. Data teams must navigate these challenges to make the most of the massive volumes of data stemming from the modern data sources used today.

Data integration unlocks hidden opportunities to make better business decisions, gain holistic views of customers, and streamline operations. By integrating their data to connect islands of information, organizations are empowered to do more in less time and money.

Why is Data Integration Necessary?

A typical business uses hundreds of systems and applications to store and process enterprise data. Teams often need to cross-analyze the data from across the organization to gain a holistic picture of their own performance. This may include CRMs, ERPs, web traffic, marketing operations systems, and more.

But a series of technical issues – such as system incompatibility, data formatting inconsistencies, and data sprawl – divide data into isolated, gatekept islands. Together, these roadblocks lead to dark, stale, or missing data. Ultimately, lost time and lost data builds to missed opportunities.

How Data Integration Solves Data Silos

Data integration connects systems and moves transformed data across your organization. Its goal is to preserve your master data sets while relocating and consolidating critical information to downstream databases.

Data integration allows you to:

  • Share massive amounts of data with thousands of users with enterprise-grade security and efficiency
  • Generate usable and high-value information to gain new insights and solve problems
  • Gather data from different departments for operational or analytical needs

How Does Data Integration Work?

Data integration combines data from multiple sources into a consolidated view through a variety of techniques. The key steps depend on how you process, connect, and route your data from source systems to target storage solutions.

The process typically includes replicating, cleansing, mapping, transforming, and migrating your data to a data warehouse, database, data lake, or data lakehouse.

The most common data integration approaches are ones where teams either:

  • Extract, Transform, and Load (ETL), or
  • Extract, Load, and Transform (ELT) their data.

Most data integration techniques have historically followed the ETL process. This data integration technique typically requires your data team to:

  1. Extract raw data from unstructured data pools and move it to a temporary hub.
  2. Clean data to ensure its quality before transforming it.
  3. Transform data by structuring and converting it to match the target source.
  4. Load the structured data to a data warehouse or database so it can be analyzed and used.
  5. Analyze the data by pulling it from storage and visualizing it within your analytics tool of choice.

ELT vs. ETL: Why Some Organizations “Load” First

While ETL is still a popular data integration method, ELT has emerged in recent years as a fresh approach. Extract, Load, Transform (ELT) carries the same steps as ETL, but ELT transforms raw data after loading it to your warehouse or database.

ELT is useful for unstructured, high-volume databases since you can load straight from the source. It also requires minimal planning for storage and data extraction. As a result, ELT streamlines transformation for high-volume databases.

Advantages of Data Integration 

Data integration streamlines the process of sharing and using siloed information. In practice, this solution provides many benefits that save your organization time, money, and energy.

Successful integration unlocks new opportunities to repurpose your resources for better experiences — for employees, customers, and decision-makers alike.

1. Better collaboration between departments

Data integration breaks down barriers between departments by bringing transparency across your organization.

For instance, a central view of all data means Marketing doesn’t have to collect customer information that’s already available in the Sales department’s CMS. From Customer 360, to compliance audits and expense reports, your teams can share data without any reservations.

3. Stay in control of governance and security

While data integration increases transparency, a good integration tool also places a heavy emphasis on data security. Sensitive data continues to live exactly where it originates, while your integration tool handles data replication and transit.

You’re free to sustain security initiatives know that your data pipeline is secure across any blend of on-premises and cloud data sources.

2. Boost efficiency and cut costs

Transparency, security, and collaboration work in tandem to create a data-driven, efficient organization.

Instead of losing countless hours seeking and compiling data, all necessary data is always within reach and ready to use. Your teams can easily gather their data for reporting and analysis, without jumping through hoops to find the right information. Out-of-the-box integration tools with built-in connections also eliminate the need for IT to spend time and resources manually programming data integrations.

4. Manage massive amounts of customer and business data

Reliable, accessible data is the key to success in the modern data landscape. By consolidating data from disparate applications, systems, and data sources into a single pool, you give your team the ability to support business intelligence, enterprise reporting, and advanced analytics.

Processing large volumes of data manually can be time-consuming, error-prone, and expensive. Ensure all your data is accurate and complete by synchronizing large datasets in near-real-time.

5. Make stronger, well-informed decisions

Ultimately, integrating your data sets gives employees a 360-degree view of key business areas. This allows your team to drill down for a transparent look into:

  • Supply chain and manufacturing operations
  • Key performance indicators (KPIs)
  • Customer demographics
  • Financial risks and opportunities
  • Compliance efforts and benchmarks
  • Other key aspects of your business

Many organizations have adopted data integration solutions to unlock valuable insights from their data stack. Effective integration helps teams easily share, access, understand, and act on the collective power of their data ecosystem.

Choosing Your Data Integration Tool Provider

Organizations are increasingly opting for ELT and ETL tools built and maintained by specialized providers. These data connectivity platforms help you map out your data movement needs, and provide ready-built data integration solutions that require no custom coding or maintenance for your team.

No-code data integration tools, such as CData Sync, democratize data pipeline design to allow even non-technical users to easily consolidate their data in the cloud or on-premises. Data consumers can then easily access and analyze their data without the need for IT intervention. To get the most from your data integration solution, seek a provider that enables everyone across your organization to partake in data workflow design.

CData Sync empowers your team to build data pipelines to easily consolidate and work with your enterprise data. Sync offers connections to over 250 popular data sources, allowing you to replicate data from anywhere and automatically replicate and move that data across your enterprise to the storage solution of your choice.

If you’re looking for a reliable, low-code data integration solution, contact CData anytime to find out what we can do for you.