D A T A I N T E G R A T I O N
Operational Data Integration
Many Operational Data Integration solutions are hand-coded legacies that need to be replaced by modern solutions built atop vendor tools. They are in serious need of improvement or replacement.
Operational Data Integration addresses real-world problems and supports mission-critical applications. Therefore, you should make sure it succeeds and contributes to the success of the initiatives and projects it supports.
Valor will help build Operational Data Integration solutions to exchange data among
various applications, whether in one enterprise or across multiple ones.
Areas of Operational Data Integration that Valor can help:
- Migration – Abandon an old platform in favor of a new one. E.g. - migrate data from a legacy hierarchical / non-corporate standard database to a modern relational one.
- Consolidation – Consolidate multiple databases into a single database.
- Upgrade – Upgrade a packaged application when the users have customized the application and its databases.
- Collocation – Data Harmonization; Collocate several datamarts / databases before eventually consolidating them.
Business to Business Data Exchange
- Synchronize data across multiple systems.
- Exchange data with partnering businesses.
- Build B2B data exchange solutions atop vendor tools for data integration with support for data quality, master data, services and business intelligence.
Analytic Data Integration
Analytic Data Integration provides a unified view of the business data that is scattered throughout an organization. This unified view can be built using a variety of different techniques and technologies.
Building an Analytic Data Integration system is challenging because it must live with the business requirements as well as the formats and deficiencies of: the source data, the existing legacy systems, the skill sets of available staff, and the ever-changing (and legitimate) needs of end users.
Valor can help your organization to:
- Understand the trade-offs of various back-room data structures.
- Analyze and extract from heterogeneous data sources.
- Build a comprehensive data-cleaning subsystem.
- Implement Business / Technical / ETL-Generated Metadata layer.
- Implement Data Flow Auditing.
- Structure data into dimensional schemas for effective information delivery.
- Deliver data effectively to centralized and distributed data warehouses.
- Build a scheduling and support system.
Links: www.tdwi.org The Data Warehouse Institute