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.
LTI’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.
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.
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.