What does data integration really mean

Data integration architects develop data integration software programs and data integration platforms that facilitate an automated data integration process to connect and route data from source systems to target systems. This can be achieved through a variety of data integration techniques, including:

• Extract, Transform, Load (ETL): Copies of datasets are collected from disparate sources, harmonized, and loaded into a data warehouse or database.
• Extract, Load, Transform (ELT): Data is loaded as-is into a big data system and transformed at a later time for specific analytical purposes.
• Data Change Capture: Identifies real-time data changes in databases and applies them to a data warehouse or other repositories.
• Data Replication: Data from one database is replicated to other databases to keep information synchronized for operational and backup purposes.
• Data Virtualization: Data from different systems is virtually combined to create a unified view instead of loading data into a new repository.
• Streaming Data Integration: A method of real-time data integration where different data streams are continuously integrated and fed into analytical systems and data storage.”

How Data Integration Works

One of the greatest challenges organizations face is trying to access and comprehend the data that describes the environment in which they operate. Every day, organizations capture increasingly more data in a variety of formats from an ever-growing number of data sources. Organizations need a way for employees, users, and customers to extract value from that data. This means organizations must be able to gather relevant data wherever it resides to support reporting and the organization’s business processes.

However, the required data is often distributed across applications, databases, and other data sources hosted on-premises, in the cloud, on IoT devices, or provided by third parties. Organizations no longer maintain data in just one database; instead, they maintain traditional master and transactional data, as well as new types of structured and unstructured data, across multiple sources. For instance, an organization may have data in a flat file or may want to access data from a web service.

The traditional approach to data integration is known as the physical data integration approach. This involves physically moving data from its source system to a staging area where cleansing, mapping, and transformation occur before the data is physically moved to a target system, such as a data warehouse or data center. The other option is the data virtualization approach. This approach involves using a virtualization layer to connect to physical data stores. Unlike physical data integration, data virtualization entails creating virtualized views of the underlying physical environment without the need to physically move data.

A common data integration technique is Extract, Transform, Load (ETL), where data is physically extracted from multiple source systems, transformed into a different format, and loaded into a centralized data warehouse.

What Is Big Data Integration

Big data integration refers to advanced data integration processes developed to handle the enormous volume, variety, and velocity of big data and combine this data from sources like web data, social media data, machine-generated data, and Internet of Things (IoT) data into a single framework.

Big data analytics platforms require scalability and high performance, emphasizing the need for a common data integration platform that supports data profiling and quality and provides insights by delivering the most comprehensive and up-to-date view of the business to the user.

Big data integration services employ real-time integration techniques that complement traditional ETL technologies and add dynamic context to continuously streaming data. Best practices for real-time data integration address its messy, moving, and temporal nature: more upfront modeling and testing are required, real-time systems and applications must be adopted, users must implement parallel and coordinated ingestion engines, resilience must be established at each phase of the pipeline in anticipation of component failures, and data sources must be standardized with APIs for better insights.”

Why is Data Integration Important

If companies want to remain competitive and relevant in their market, they need to embrace big data processes and all their benefits and challenges.

Data integration supports querying these massive datasets, benefiting virtually every aspect of business. From business intelligence and customer data analysis to data enrichment and real-time information retrieval.

One of the most crucial uses enabled by custom data integration solutions is managing your commercial data and customer data.

For instance,

By consolidating and managing your customer information in a structured manner, you can automatically provide better customer service by gaining essential insights for prospect and existing customer management.

Customer Data Integration (CDI) can help you create a more efficient data management system, allowing your team to easily access and query customer data as needed for business purposes.

This data integration provides a valuable tool for businesses to analyze key performance indicators (KPIs), financial risks, manufacturing operationalization, and even their supply chain and distribution.

Therefore, it’s highly recommended to be part of your web analytics strategy.

¿What Do Data Integration Tools Do?

There are multiple data integration software and platforms that have been developed to harness the use of information with techniques that enable automated data integration.

These software solutions enable connecting data from its source to its destination and can have various objectives:

• Extract, Transform, Load (ETL): Collecting and streamlining copies of datasets from various sources into a database.
• Extract, Load, Transform (ELT): Taking data as it is distributed in a big data system and transforming it later for web analytics with specific delineated characteristics.
• Change Data Capture: A tool that identifies real-time updates and changes in data and consolidates them directly into a data warehouse.
• Data Replication: Data that is replicated from one database to another to keep information up-to-date and synchronized for operational use and backups.
• Data Virtualization: Data that is located in different systems and virtually combined for loading into a unified database without the need to create new repositories or databases.
• Real-Time Data Integration: As the name suggests, this method allows loading and updating data located on different servers, continuously integrating it according to changes happening at that moment into an analytics system or data warehouse.”

5 Methods for Data Integration

As mentioned earlier, the ability to integrate data has gradually grown in recent times, as multiple tools have been developed that now allow access to data located on different servers and composed of billions of pieces of information (known as Big Data).

Let’s explore 5 ways to perform data integration:

1. Manual Data Integration

As the name suggests, data administrators manually handle all integration phases, from retrieval to consolidation and presentation.

2. Middleware Data Integration

Middleware represents software systems that facilitate communication between legacy systems and modern ones to expedite data integration, monitoring, and operations.

3. Application-Based Integration

Software applications identify, locate, retrieve, and integrate data, making data from different sources and systems compatible with each other.

4. Uniform Access Integration

This technique retrieves and consolidates data to be viewed uniformly without the need to migrate it to a single location, leaving it in its original source.

5. Common Storage Integration

An approach that retrieves and displays data uniformly, similar to the uniform access integration mentioned earlier, but also makes a copy of the data and stores it in a destination location.

Conclusion

As can be observed, data integration is a capability that is becoming increasingly robust, allowing companies to make use of information contained in big data systems.

It can be applied to gain deeper insights into customer behaviors, business intelligence, marketing strategy design, understanding financial risks, and overall data consolidation for effective information use.

Data integration facilitates the optimization of goal-reaching processes, whether they are related to business or other areas such as healthcare research, finance, customer management, etc.

It holds special relevance today, and it’s likely that more tools will continue to be developed, enabling faster and more accessible web data integration actions.