Big data analytics has quickly become one of the most important business processes. The insights company’s get from data enables them to enhance their customer service, products, services, and lower costs. Businesses rely on the real-time exchange of information to make critical business decisions. Data integration is the process that enables companies to get actionable information from disparate sources and format it so they can use it.
Data integration can be a lengthy and costly process. However, different platforms have varying benefits and hangups. There are many integration techniques, but we’ll narrow it down to the top four in this brief article.
Table of Contents
1. Data Virtualization
Sometimes, there’s one product or solution that stands out from the rest. Data virtualization is that tool when it comes to data integration processes. It makes data from differing source systems available in a centralized database. One of the great things about data virtualization is there is no data movement because everything remains in their source systems.
Virtualization provides speed and scalability that other integration strategies can’t match. Another great thing about this method is you don’t need to be a data scientist to use it. Virtualization tools are simple enough for business users to learn to utilize them without taking too much time from other essential processes.
Data virtualization is merely one of the many TIBCO integration solutions. TIBCO is an industry leader in data science, and its integration tools range from TIBCO cloud integration to their powerful virtualization tool.
2. Cloud Integration
Cloud integration is another of the most time and cost-efficient data integration strategies. Cloud integration is the integrating of data from disparate solutions to cloud servers.
Cloud integration is most useful for application programming interface (API) management. This integration style enables companies to extract data from local servers and make it available to cloud systems and remote teams. Cloud integration makes it easier for teams to collaborate on projects like building applications and data visualization projects.
3. Enterprise Application Integration
Enterprise application integration is invaluable for companies with a robust enterprise application infrastructure. The more business software applications you run, the more data you collect. In addition, the more data you collect, the harder it is to promote a real-time exchange of information between different applications.
Vast enterprise IT infrastructures often create the problem of data silos. Silos form when apps and other systems containing data in different locations can’t share their data. Enterprise application integration enables data to transcend these silos. This process ensures business users and data analysts have accurate data regardless of which app they’re currently using.
4. Manual Data Integration
Manual data integration is one of the older and more labor-intensive integration methods. It uses an extract, transform, load (ETL) tool and requires a dedicated team, time, and capital.
Data extraction is the first step in ETL processes. During this step, data scientists choose their data sources and begin the extraction process. Next, they format the data to make it uniform and discernable. Finally, a team member loads the data into the destination point.
Indeed, the ETL process requires great skill and patience. However, ETL has its strong suits. One of them is that it provides business users with greater control over the integration process.
Data integration is an integral operation for companies that rely on data-intensive processes. It requires skill, tools, and a cohesive team to implement a data integration project successfully. The most popular and effective integration methods are virtualization, manual integration, warehousing, and application integration. They all have their strengths, and the optimal selection is the one that best fits your company’s needs. So, which data integration strategy will your company employ?
