Kyvos + Impetus
An end-to-end solution that helps you migrate your existing data silos to cloud or Hadoop Big Data warehouse using automation, and build an analytics platform on it that provides instant and interactive enterprise business intelligence. The solution follows an incremental approach with five main stages to ensure a holistic EDW modernization to enterprise BI solution for maximum business benefit.
Each of these stages is integrated to ensure a holistic solution that drives immediate business benefits and provides a measurable return on investments. Automated migration removes the risks of manual errors and reduces the time for migration. Once data from diverse and multiple data sources are brought together, an enterprise BI consumption layer is built on top of the data warehouse to provision the data for business users by enabling fast, interactive, and self-service BI across the enterprise. The integrated solution helps in building a single source of truth for corporate-wide use with tight security protocols and good governance.
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Laying the foundation for a Unified Logical Data Model
Automated inventory and profiling of current workloads to identify the offloadable entities. Data-driven assessment to right-position your workloads in the Big Data environment for both cloud and on-premises.
Automatically move data to an optimized data model, translate logic into Big Data compatible code, and execute them on any standard Big Data environment.
Simplify and improve how you prepare, manage, and deliver critical information to the business. Integrate disparate heterogeneous sources, enrich data quality using analytical transformations, and maintain a centralized data lake on top of a consolidated, scalable platform.
Deploy a scale-out architecture that delivers very high performance at any scale, regardless of the number of concurrent users. Provision fine-grained access control and strong authentication for safe access across the enterprise.
Build an enterprise BI consumption layer on Big Data platform that enables business users to securely leverage Big Data for interactive, multi-dimensional analytics using their existing Business Intelligence and Data Science tools.
- On-premise/cloud deployment
The extensible architecture enables quick and easy migration, access and consumption of data across all leading big data storage platforms, both on the cloud and on-premise.
- Builds an enterprise-wide data model
Creates a single copy of a trusted source of data, made available for access and analysis for users across the enterprise. Visual tools for data preparation and analysis enable better correlation and understanding.
- Automates processes
Connects disparate heterogeneous sources and automatically catalogs, curates, and cleanses data for analytical consumption. Automated incremental builds update newly ingested data and truncate expired data.
- Ensures data governance
Tracks the entire journey of data, enriches and enhances its quality and makes it readily discoverable. Honors the existing enterprise security models and works with big data platforms to ensure data governance.
- Provisions self-service enterprise BI with controls
Enables user-friendly analytics by integrating with existing BI tools for rich visualizations. Integrates with enterprise authorization systems and provides inbuilt granular access control with column and row-level security.
- Makes operationalization easy
Allows measurement of operational variables empirically and quantitatively across business use cases. Enables advanced analytics on a variety of proliferating real-time data sources in a self-serve mode.
- Allows enterprise-wide data stewardship
Data steward group ensure the availability of centralized data dictionaries, MDM, data enrichment, data preparation, pre-prepared data models, and business rules. Unified metadata and services repository, Data profiling/ discovery-based recommendations, and curation/Collaboration enabled Workflows.
- Phased implementation
The phased approach ensures proper implementation of different technologies, improves success rates, reduces time to implement, and ensures user collaboration at every stage.