What is Data Integration? How it Works and Examples

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 can result in 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 5 data integration patterns

There are five basic patterns, or approaches, to implement data integration. They can be manually coded or built with a data integration tool.

1. Extract, transform, and load (ETL)

ETL is arguably the most popular data integration approach. Most data integration techniques have historically followed the ETL process:

• • Extract raw data from unstructured data pools and move it to a temporary hub.
  • Transform the data by structuring and converting it to match the target source.
  • Load the structured data to a data warehouse or database so it can be analyzed and used.

2. Extract, load, and transform (ELT)

ELT employs the same elements as ETL, but ELT loads the raw data into a warehouse or database first, where it is transformed from its raw form based on business needs.

3. Data streaming

Data streaming is a continuous and real-time flow of data from one system to another. When the data is transferred on an ongoing basis, it is immediately available for analysis and insights.

4. Data virtualization

Data virtualization provides a logical layer that allows organizations to access, manage, and integrate data from disparate sources without needing to duplicate, move, or store it. The virtualization layer acts as a single point for data access, providing a real-time view into unified data without the storage costs of replication.

Discover data virtualization for the cloud with CData Connect Cloud

5. Application integration

Application integration is when disparate applications are connected and made to work together to expand and enhance the functionality of the other. To facilitate this integration, teams can install a pre-built software connector instead of creating and maintaining one.

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.

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.

Learn more about the differences between ETL and ELT

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.

• 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 data models to compliance audits and expense reports, your teams can share data without any reservations.

• 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.

• 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.

• 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.

• Make stronger, well-informed decisions

Ultimately, integrating your data sets provides 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.

Challenges to data integration

While data integration is effective for many use cases, there are some limitations that you need to think of when evaluating solutions for your data strategy.

• Lack of standardized interfaces

Today’s data comes in diverse formats, and diverse systems often lack standardized APIs, making it challenging to establish smooth communication between systems. To combat these complexities, organizations should implement a data integration platform that provides a standardized language for connecting to multiple sources.

• Keeping up with growing business demands

Businesses are constantly adding tools and applications to their data ecosystem, and integrating those systems into their existing stack is often challenging when their data integration solution is built ad hoc. Out-of-the-box data integration tools help ease that burden, allowing data teams to simply configure the new connection with a few clicks.

• Integrating external data

Sharing data and information with external vendors, customers, and organizations proves tricky. Bringing data into your ecosystem from external sources may cause some issues with ingestion, formatting, and maintaining quality. Tools that can transform data to match an internal model or schema – whether in-flight or in-place after replication – can ease the burden of data teams looking to reconcile internal and external data.

Use cases of data integration

• Data ingestion

Organizations can ingest data from multiple sources into a central storage or processing system to make it accessible for downstream analytics, holistic reporting, and more.

• Data warehousing

Data integration allows for the transfer of data into a data warehouse – in the cloud or on-premises. Data warehousing allows organizations to create a holistic view of their organization for improved business insights.

• Business intelligence and reporting

After the data is ingested into a data warehouse, database, or data lake, organizations are empowered to query that data in the reporting application of their choice, allowing them to gain a comprehensive view of the information available.

• Cloud migration

Modern data integration tools facilitate the transfer of data from on-premises systems to cloud environments, transforming it to align with cloud-based structures and loading it into cloud databases.

• Artificial intelligence (AI) and machine learning (ML)

AI initiatives rely on massive amounts of comprehensive data to succeed. Data integration is a key tool in ensuring that quality, highly relevant data is available to train machine learning models.

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, get a free, 30-day trial of CData Sync today.