On Operational Data Integration
Today operational data integration is one of the fastest-growing practices in data integration, according to TDWI research.
Actually, operational data integration stands for “implementations, projects, or initiatives commonly described as the consolidation, collocation, migration, upgrade, or synchronization of operational databases.” In other word, it stands for data integration between applications.
Today, as most organizations make efforts to implement business intelligence initiatives enterprise-wide, there is a growing demand for different operational data integration techniques and tools, including enterprise application integration (EAI), enterprise information integration (EII), extract, transform, and load (ETL), etc. Operational data integration is needed to address several challenges companies the work of which depends on data face:
Redundant data in multiple non-standardized databases across different companies departments is a serious challenge which leads to increased IT costs and hinders unified visibility into company’s business processes. To avoid redundancy and synchronize and standardize the data such solutions are needed as database consolidations, migrations, and so on.
Lots of non-standardized applications throughout an enterprise may also provide redundant data. So, some form of operational data integration is required to migrate or consolidate their databases to prevent data redundancy.
In other words, as enterprises are heading for more agility and real-time data integration to increase efficiency and accelerate business responsiveness, the demand for operational data integration is growing.