Enterprise Data Grid
For the past decade, many enterprise IT systems have adopted service-oriented architecture (SOA), and high-scale databases. But such systems struggle to handle large volumes of data, generated from disparate sources. Advent of new technologies such as in-memory databases, change data capture software, big data storage, analytics, and complex event processing, necessitates the creation of enterprise data grids. These grids help consolidate siloed information into a single unit, which is updated in real-time, and is accessible by many applications with very low latency.
LTIMindtree’’s Enterprise Data Grid is a one-stop solution, providing a set of structured services, with the ability to access, alter & transfer large datasets across geographies, and serving as a single go-to place for all business-ready information. This Enterprise Data Hub provides an enhanced experience across the value chain, through a data-driven sense & response system.
Highlights
- Decoupling the source, and providing driverless data ingestion mechanism, and detaching the metadata from data curation.
- Automated data ingestion and validation process.
- Enabling the solution to consume streaming information.
- Expediting implementation irrespective of the source type.
- Apification of data curation process.
- Implementation of enterprise-enabled rules.
- Business signal sensing, by enabling data discovery and formulating business moments. A collaboration-driven approach to identify business-critical moments for functions on formulation, demand planning, taxation, inventory, manufacturing, etc.
- Defining persona maps and insight brain map to implement the data fabric.
Benefits
- 60% reduction in speed-to-market for integrated data availability.
- 20% reduction in spend across various initiatives, for data collection and curation.
- Uni-process of assessing any transactional data across the application landscape.
- Simplified monitoring of quality, productivity, etc., in the manufacturing value chain.
- Real-time analytics, by putting enterprise information in motion, and identifying meaningful events and patterns in streaming data to add context to content.
