Currently, businesses are constantly competing on how fast and well they can glean actionable insights from their data sets to deliver better products, services, and finally delightful experiences. It is the quality of experiences that business customers take into account to decide whether they will stay engaged or buy from competitors.
The faster a company can access the insights from the data, the faster it can make decisions and deliver the promised value to customers.
But how does a company garner insights when it is handling enormous volumes of big data, multiple data sources, multiple systems, and applications?
The answer lies in data integration.
In this blog post, you’ll find how data integration enables businesses to deliver value in detail.
What is Data Integration?
Data integration entails the process of consolidating different data sources to make the data more useful. It enables businesses to implement new data connections with customers to facilitate faster transactions and value generation. Companies can leverage data integration solutions to gain a 360-degree view of customer data. That is much more useful to the business than just having to deal with separate data points that don’t seem to connect in each of the systems.
What Are Different Types of Data Integration?
Manual Data Integration: Manual data integration solutions require IT teams to write long custom codes and perform extensive data mappings to implement data connections with customers. In this technique, IT integrators need to connect the disparate data sources, collect the data, clean it, etc., sans automation.
Middleware Data Integration: In this technique, the software is used to connect applications and transfer data between those applications and databases. It’s especially handy when a business is consolidating legacy systems with newer ones. That’s because a middleware can act as an interpreter between these systems.
Application-Based Integration: In this specific technique, software applications perform the task of integration. They find, retrieve, cleanse, and integrate data from various data sources. By offering such compatibility, businesses can easily move data from one source to the other.
Uniform Access Integration: This technique leverages data from different data sets and stores it uniformly. The stored data also stays in its original location.
Data Warehousing: This is quite similar to uniform access. The only difference is that it involves creating and storing a copy of the data in a data warehouse. This causes more versatility in the ways companies can leverage data, which makes it one of the most popular forms of data integration.
Self-Service Data Integration: In this technique, self-service data integration empowers non-technical business users to implement new data connections with speed and scale. Meanwhile, IT is freed to focus on more high-value tasks.
What Are the Challenges of Data Integration?
Legacy data integration solutions require IT to create custom codes and carry out complex EDI mapping processes to integrate and use complex, bi-directional data streams. That takes weeks or months of calendar time. While IT takes a lot of time to implement connections, customers are forced to connect with business workers. That slows down value generation and revenue. Also, legacy solutions take a lot of time to onboard customers, which again negatively impacts the value-generation process.
How Can Self-service Integration and Artificial Intelligence Overcome the Challenges?
Self-service data integration brings non-technical business users to the forefront. In doing so, it empowers non-techie users to implement new business connections much more quickly. At the same time, IT need not implement custom codes and EDI mapping and become free to drive other important business priorities.
In addition, AI-enabled solutions also play a central role in transforming data mapping outcomes by making them much faster, more secure, and easier than ever before.