The world of data is constantly evolving and becoming more and more proficient for us to use to our own benefit.
With this comes the equal evolvement of data integration. This is essentially the process of where data can be seamlessly moved to databases that are accessible either internally, externally or even both.
The technologies that are allowing data integration to move faster and more efficiently than ever before are allowing companies to thrive and use data to drive decision-making like never before.
So, what exactly is data integration? The very heart of it is the ability to combine a whole bunch of data from various technologies and sources and literally integrate it into one holistic overview.
It sounds super simple, but the behind-the-scenes in making it happen is actually quite robust and complex. Think about just the sheer volume of data out there, let alone having to organize and sort it in a way that does not put the sole pressure on humans to do it.
When data integration is done right, a robust and neutral overview of all the data captured by all the tools will be available, whether that be on internal databases or external databases. It also means that it can disperse data in an extremely advanced and automated way, making it easy for you to access and digest.
So what are the various types of data integration tools that you can use to achieve the seamless oversight of your data? We are going to share all the different types of data integration and lay out exactly what they do.
1. Integration Platform as a Service
For short, this is also referred to as iPaaS. It has been around for over a decade and adopted by numerous companies all over the world. The capabilities to integrate really vary from individual vendors—but they are all designed to perform based on the same set of triggers.
When we say trigger, we mean the signaling of a certain event taking place. This means it is kicked into action when it senses that an email was opened, someone signs up for a newsletter, someone clicks through an ad, and so on. When it is triggered, it will then send this data to the integrated platform that sets into motion a series of actions that are already established and set up.
This type of data integration also allows you to keep the coding simple and enable people on your team with minimal technical knowledge to use it in a user-friendly way. But they also have very set limitations and are often not as flexible with data integration that is sometimes needed by companies.
2. Customer Data Platform
The next type of data integration is known as a customer data platform. This is essentially designed to take all the data of all customers and integrate it into one unified location, which then sorts and categorizes the data and sends that information to designated destinations.
Customer data platforms are not only able to move data from place to place, but make collecting data even better. This has become extremely popular by a lot of companies in the last few years, as it helps many organizations not only integrate data but use that data to problem solve.
Customer data platforms are able to move data, but again it can be quite limited with flexibility and integration if it is through a third-party vendor. But if your customer data platform is actually also your data warehouse, then you will get all the benefits with minimal limitations!
3. ETL / ELT
Also known as “extract-transform-load”, this data integration process has actually been around since the late 1970s and really started to boom in the 1990s. The way ETL was designed is to take data from a first-party database and also external sources and use all that data to create data models that can be used by analysts and data scientists—with the final destination being secure storage in a data warehouse.
Nowadays, this has transformed into a more modern approach of “extract-load-transform”. By switching the touchpoints that data is sent to, with the data warehouse being before the distribution of the resulting data models, it enables a faster turnaround time and more flexibility in changing how the data is transformed. It is fast, reliable, and equally allows people with minimal knowledge of code to play a leading role in using this
4. Reverse ETL
This is also a data integration tool that ensures the integrity of data is upheld. With so much sensitivity around customer data, reverse ETL is a new and improved data integration tool that can transport data out of your warehouse and send it back to the tools that collected it in the first place and equally run your business operations.
Conclusion
Data integration is key for utilizing data for your benefit. What tool will you use?
