What Is Data Integration? Best Practices + Tools

What is data integration?

Data integration is the process of combining and consolidating data from multiple sources to provide a single holistic view.

The data integration process is often the beginning of many routine data processes, from transformation, mapping, and data analysis.

This process is usually one of the initial phases of a data supply chain. It’s fundamental that businesses get it right, as it can affect data processes and action further along the data pipeline.

Data integration had no single approach, but there are common elements consistent from case to case. These usually include a series of sources that are integrated using multiple techniques and processes.


Benefits of a high-quality integration system

In today’s connected and data-driven world, it’s rare to find a business that uses only a single data source. In fact, the average company will have several complex operations that require data from multiple sources to operate effectively.

Thus taking multiple data sources and combining them is a technical challenge in itself. A single company may be using data from external sources, their CRM and internal databases, marketing, and analytics tools, as well as customer-facing tools and applications, to name a few.

The combination of these data sources also presents a series of marginal gains. For example, the effect of inputting data into a single system reduces the workload and set up costs of managing multiple datasets and integrating them elsewhere.

Modern companies must be able to adapt and work with multiple datasets. In a typical company, the benefits could look like the following:


Time-saving and efficiency gains

Manual data integration can be a costly and time-draining process. Single task integrations can snowball into repeatedly run tasks, taking up resources each time it needs to be done.

Preparing and analyzing data becoming integrating it takes time and requires careful analysis. Building a robust data integration properly alleviates the stress on these resources at a future date. It removes the need for employees to make connections from scratch each time an integration needs refreshing.

Using an integration tool (such as Wult) can help companies to save even more time. This can reduce the need for coding tasks, saving even more time and resources, allowing resources to be allocated to other tasks, such as analysis.

A good system will also be timely, so data arrives as close to real-time as possible, making it possible for the company to react faster than the competition.


Fewer mistakes and errors

Keeping track of a companies data resources and how they are managed is a lengthy and complicated process. The correct documentation is needed; employees need to have the correct software and setups need to be consistent across teams that work with data integrations.

Also, without a dedicated tool or data integration process, this must be replicated whenever anything changes.


Improved collaboration

Data isn’t a static resource – in larger organizations, it’s shared between teams and can even require transportation across numerous countries and locations.

Companies, therefore, need a secure and reliable solution for integrating data and delivering it to a location that can be used effectively.

Alongside this process, the employees who are using this data will undoubtedly need to make changes and optimize data for their specific needs. A robust data integration system will allow these changes to be effectively tracked and managed so that innovation can be tracked across the business.


More power and usable data

Integration, when appropriately done, forces organizations to optimize and improve the data that is integrated. It facilitates improvements from multiple employees and departments into a single centralized location.

This means that the data is of a higher quality as quality issues are identified and improved. This ultimately means that the data is ready to use in a much better state than before the integration process and can form the basis of effective analysis.


Why data integration – some everyday use cases

The data integration process doesn’t look the same for every company and can vary significantly depending on several factors. Let’s look at some common examples of data integration to understand how it can benefit businesses.


Making business intelligence simpler and more accessible

A single, unified view of many data sources is a powerful BI tool. Businesses can get an overview and rapidly comprehend and analyze available datasets to maximize BI insights. This allows quick and practical insights into the current status of the company.


Creating centralized data containers to power multiple departments

For larger businesses, the integration process will precede the building of a database or data warehouse that is a combination of many data sources.

In these examples, the data will sometimes be relational and therefore, should be queryable, able to run reports, extract relevant analysis, and access data in a consistent form. Data should be integrated correctly and with the correct procedures to work most effectively.


Making use of big data

The more data sources a business uses, the larger the potential amount of data that will need to be ingested and integrated into their system. The amount of data being created is growing rapidly. For companies with a data generating product with a large number of users, the amount of data can grow quickly.

Alternatively, some organizations require big data sets of entire cities to be integrated and available for analysis. Therefore, the data integration effort requires higher sophistication – companies can ill afford it to break or have significant downtime, as this can lead to vast amounts of lost data.


Best practices

There are several different strategies for integrating data, and this choice is often based on many factors that are different for each company.

These are usually the amount of data, the number of data sources, the completeness of the data being integrated, and the characteristics of the data.

Today, these are the primary data integration methods for businesses:


Application-based integration

This data integration method involves an application that helps businesses set up connections to data sources and integrate the required data.

They will likely have a powerful interface to perform these tasks, allowing both developers and users with less technical experience to have an input into the data integration process. They are usually collaborative and will ensure that every stakeholder is on the same page.

Being out of the box, these processes can simplify the process of data integrations. They will most likely come with a suite of tools to assist with data tasks once the data have been integrated, making them excellent value for money and resources.


Manual data integration

This process is a more fundamental approach to data integration. Datasets are manually collected and formatted to match the desired end location.

Of course, this method is very time consuming and requires much manual work. For companies with any more than small amounts of data and numerous data sources, this is not recommended. There is also an increased chance of errors and other issues with the management and maintenance of inputs.


Middleware integrations

Middleware is similar to an application based integration – it sits in between the data source and the end location and usually manipulates and formats the data before sending it to the correct destination.

This is a slightly more manual process, but it can be useful with legacy systems that aren’t supported or data formatting issues arise.

It’s important to note that some modern applications that deal with data integration will have the ability to combine middleware into their workflow, simplifying the whole process.


Uniform access integration

This approach involves a front end system that can visualize data consistently from multiple sources. The data doesn’t leave the source and is stored there, but viewed elsewhere.

The benefits of this method are that the source data remains in different systems, and can be in multiple formats.



Legacy data

Companies need to think carefully about the types of data they are integrating and the format of any legacy data. Newer systems will likely have more identifiers and other fields that may be missing in legacy systems such as time and date.

In these situations, careful planning and using a method with the ability to modify and adjust legacy data as it is integrated can be invaluable.


New data types

As data becomes more widely utilized, new types and formats are being created all of the time. These might differ in type (unstructured, real-time) or source (location, IoT), for example.

Adapting your data integration solution to these changes is part of the process and will ensure that your business can continue to use data effectively and get the best results. Of course, a data integration application approach will be likely to support these new technologies quicker in a thoroughly tested environment.


Third-party data

External data should always be carefully vetted and assessed with regards to quality and accuracy. It can, however, be challenging to get a complete view of how the data was collected. There may also be governance issues to consider before integrating data, and this should be understood in this initial stage before the data is being used throughout an organization.


Integrations management

Data integration doesn’t end once data has been integrated. Managing the integrations must be considered, and in some cases, teams must update and adjust integrations to keep up with best practices.

Again this is simple in a modern application based integration tool. Management of integrations is simple and often collaborative, so your integrations are up to date and secure.


About Wult

Wult lets companies control their data pipeline. From integrations to transformation and governance. At every stage of the data lifecycle, Wult helps teams and organizations collect high-quality data and generate better outcomes from it.