The data explosion continues to accelerate, putting pressure on organizations of all sizes to find better ways to source, access, and curate data so they can harness its power. Data integration consists of a broad category of practices, processes, and technologies for collecting, aggregating, and refining data to produce valuable data sets for a spectrum of requirements. Data integration is vital for provisioning single views of relevant data, enabling real-time data connectivity to applications, and fueling analytics and AI/ML.
As data demands grow, so does data integration’s complexity and volume. Suddenly there could be thousands of data pipelines, connectivity routines, ETL processes, and APIs in use, creating data management, governance, and performance headaches. The proliferation of data silos across hybrid multicloud environments makes data integration even more challenging. It is no surprise that TDWI research finds that most organizations surveyed exhibit strong interest in modernizing data integration through better technologies and practices.
In this presentation, we will look at current data integration challenges in TDWI research and discuss opportunities for solving them. Topics will include:
- Trends in automation and self-service capabilities
- Distributed data integration, including data virtualization, fabrics, and data mesh
- The role of data catalogs and semantic data integration layers
- Trends toward holistic observability and management of data integration