What Is Data Integration and How Can It Move Your Business Forward in 2024?

What is data integration?

Data integration is the process of combining data from various sources into a single, unified view for:

• More efficient data management
• Extracting meaningful insights
• Gaining actionable intelligence

As the amount of data is growing, coming from different formats, and becoming more distributed as ever, good data integration tools should be able to aggregate data no matter the type, structure, or volume.

Data integration is an essential part of a data pipeline and includes data ingestion, data processing, transformation, and storage for easy retrieval.

Integration starts with the ingestion process and covers steps like cleansing, ETL (extract, transform, load) mapping, and transformation.

While there’s no universal approach to the process, data integration solutions typically include a few common elements, like a network of data sources, a master server, and clients accessing data from the master server.

In a typical data integration scenario, the client sends a request to the master server for data. The master server then aggregates the needed data from internal and external sources.

The data is then consolidated into a single cohesive dataset that is served back to the client, ready for use. Data integration platforms effectively provide analytics tools that foster actionable business intelligence.

When designing Whatagraph’s data integration feature, we had two types of users in mind:

• Small non-tech teams
• Marketing agencies that handle hundreds of clients, each with their own data stacks

For both groups of users, Whatagraph saves the time needed to load data from multiple sources to Google BigQuery.

We wanted to create a point-and-click data transfer process, so everyone can run it.

Whatagraph handles all coding and mapping internally so there's no room for error.

If you’re looking for an easy-to-use solution to connect data from various marketing sources, keep your marketing data safe, or want to visualize your BigQuery data, book a demo call and find out how Whatagraph can help.

5 types of data integration

1. Extract, transform, load (ETL)

The most prevalent data integration method is the extract, transform, and load, which is commonly used in data warehousing.

In an ETL tool, data is extracted from the source and run through a data transformation process that consolidates and filters data for analytics purposes.

The resulting datasets are then loaded into a data warehouse. The ETL method operates in batches involving bulk amounts of data.

2. Extract, load, transform (ELT)

The extract, load, and transform approach is often used in big data systems as an alternative to ETL. This process inverts the second and third steps, loading raw data into a destination system and filtering and transforming it as needed for individual analytics jobs.

Source

This option is popular with data scientists who prefer to do their own data preparation and want access to complete datasets for machine learning and predictive data modeling applications.

3. Change data capture (CDC)

CDC uses a type of real-time data integration that applies updates made to the data in source systems to data warehouses and other data stores. It also enables streaming data integration, which integrates real-time data streams and feeds the combined data sets into databases for operational and analytical use cases.

4. Data replication (DR)

This method copies data from one data source to another system to synchronize them for active, backup, and disaster recovery uses. It can operate in either real-time or batch mode.

5. Data virtualization (DV)

This method evolved from an earlier approach known as data federation. Instead of integrating data physically, data virtualization uses a virtual data layer to integrate data. As a result, business users and data analysts get an integrated view of different data sets on demand without having to enlist an IT team to load the data into a data warehouse.

Data virtualization can reinforce an existing analytics infrastructure for specific applications or become part of a logical data warehouse or data lake system that includes different platforms.

Different approaches to data integration

There are several ways businesses can integrate data depending on the enterprise size, needs, and available resources.

Manual data integration: Users manually collect necessary customer data from various sources, clean it up, and combine it into one warehouse. This method is extremely inefficient and inconsistent because they have to access each interface directly. This is why the manual method is unusable for all but small organizations with minimal data resources.

Middleware data integration: An approach where a middleware application functions as a mediator that helps normalize data and bring it to the master data pool. Think of it as an adapter for legacy applications that are incompatible with others. Middleware SaaS comes in handy when a data integration system is unable to access data from one of those legacy applications on its own.

Application-based integration: In this method, software applications locate, retrieve, and integrate data. For this to work, the software must make metadata from different systems compatible with one another, so that they can be transferred from one source to another.

Uniform access integration: This approach creates a frontend that makes data appear consistent when accessed from different sources. The actual data, however, stay within the source. With this method, teams can use object-oriented database management systems to make unlike databases appear uniform.

Common storage integration: With this data integration process, users can keep a copy of data from the source in the integrated system and process it for a unified view. This is in contrast with uniform access, in which data stays in the source. The common storage approach is the foundation of many on-premises data warehousing systems.

Benefits of data integration

Improves collaboration

Employees in different departments and sometimes in different physical locations often need access to the company’s data for collaboration or individual projects. IT, on the other hand, needs a secure solution for delivering data through self-service access across different lines of business.

Saves time

When a company is interested in integrating its data properly, it can significantly reduce the time it takes to prepare and analyze enterprise data. The automation initiatives eliminate the need for manually gathering data. In contrast, teams no longer need to build connections from scratch whenever they need to create a report or an application.

Reduces errors

When it comes to a company's data resources, there’s a lot to keep up with. Teams must know every data location and account they may need to explore. Not to mention all the necessary software they have to install beforehand.

If any team adds a data repository and other teams are unaware, they’ll end up with data silos. Also, without a data integration solution that provides data migration and synchronization, reporting needs to be redone to each account for any changes.

With automated updates, you can run reports efficiently in real time whenever they’re needed.

Produces more valuable data

Data integration efforts improve a business’ data quality over time. When data is integrated into a centralized system, quality issues are identified much sooner. This, in return, allows necessary improvements to take place, which results in high-performance decision-making and more accurate data.

Features of a good data integration tool

Data integration tools simplify the process beyond measure. If you need a data integration tool, look for:

• A lot of connectors: Your clients may use many different systems and applications. The more pre-built integrations your data integration tool has, the more time your team can save. Whatagraph has built-in integrations for:

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• Portability: This aspect is important because companies increasingly migrate to cloud-based solutions. With a portable tool, you should be able to build your data integrations once and run them anywhere.
• Open source: Open source apps usually provide more flexibility and help avoid vendor lock-in
• Ease of use: Data integration platforms should be easy to learn and easy to use, with a graphic user interface (GUI) that simplifies your data visualization.
• A transparent price model: Your data integration tool provider should not charge you for increasing the number of connectors or data volumes.
• Cloud compatibility: Your data integration tool should work natively in a single, multi-cloud, or hybrid cloud environment.

Whatagraph allows you to move vast amounts of your client’s business data to Google BigQuery for integration and storage.

BigQuery is a serverless data warehouse based on Google’s Cloud Platform that allows users to store and analyze structured and unstructured data from multiple sources.

Select an account, add your sources, pick data points you want to move, and your data transfer is on the way to storage.

From there, you can also use Whatagraph’s reporting function to visualize your data through rich interactive dashboards, which you can optimize to your needs.

Conclusion

Data integration is a key component of analytics, business intelligence, and gaining a competitive edge. Companies that plan to stay on top invest in solutions that grant them full access to every data set from any source.