Data is arguably the greatest resource available to modern enterprises, but this is only true when it’s correctly organized, stored, and analyzed. Monetizing data gives organizations the power to increase sales, boost customer satisfaction, improve their supply chains, and create better business processes. This is difficult, if not impossible, to do, however, if all your data is separated into different sources. Data integration is an integral part of any digital transformation, and without it, you’ll be unable to use some of your potentially greatest assets.
What is data integration?
Modern enterprises deal with large data sets that are frequently too big to be handled by traditional data processing software, at least in a timely manner. Such data sets are frequently called big data, and modern integration and analytics solutions are needed to process these data sets in order to make efficient use of them. Put simply, the data integration process consists of connecting once disparate data sources into a single database that provides a unified view of the entire organization.
An example of a data integration solution could be a data warehouse that collects business intelligence from a variety of source systems. Such a data warehouse may collect information from your sales team’s applications, the customer support team, and secondary data sources (like financial reports) to help produce better marketing strategies. Of course, the benefits of these connections aren’t limited to any single department.
How does a data integration system work?
The greatest challenge for these application integration systems is ensuring that they can make sense of the different environments that data operates in and is collected from. These days, data is sent over a wide variety of apps, software solutions, through the cloud, over IoT devices, and more. In order to collect all of this data in one source, the different environments need a way to communicate with each other.
Consider how the internet works, as an example. Different computers, mobile devices, and apps communicate with each other across the United States and multiple other world regions, typically producing results in near real-time. This level of connection and high performance is thanks to internet protocol (IP). IP is a set of rules that governs how data is transmitted over the network, so it can reach the correct destination. All of these devices can communicate with each other because they’re using the same language, so to speak.
Unfortunately, not all applications or business intelligent devices are able to communicate directly with each other since they don’t necessarily use the same application programming interface (API). Without compatible APIs, data from disparate sources won’t be able to be used together, and your integration efforts will be over before they begin. This is where data virtualization and the extract, transform, load (ETL) processes come in.
What is data virtualization?
Data virtualization software works as a sort of bridge between separate software solutions and data sources. As the name suggests, this process creates a virtualized view of different data environments without the need to physically move data sets from one software solution to another. This manual process of transferring data is extremely time-consuming, not to mention prone to human error. This process allows for the extraction of data from different sources where it can then be transformed from one structure to another. Once the transformation process is complete, all data sets will be able to be integrated with each other.
Once your systems are fully connected with a data integration tool, you’ll be able to share raw data between each integrated platform and analyze the data in real-time. Applications include reacting instantly and making adjustments to marketing campaigns, using customer information to improve service and sales, and finding inefficiencies in your routes and supply chain, just to name a few. Proper data integrity is the key to taking your enterprise to the next level.
