Data Warehouse Solutions
Our Data Warehousing services include data architecture design and delivery and metadata architecture design and delivery for all types of repositories. Both data and metadata architectures will be tightly integrated with the integration architecture to realize scalability, flexibility, and expandability for the long term.
We support full implementation services for data warehouses, data marts, sandboxes/analytical environments, ad hoc environments, smart hubs with multi-layer functionality, and virtual environments.
Data architecture, design, and delivery begins with business uses
To define data architecture, design, and delivery, we first look at understanding the underlying data and how it is defined by the business to then create a data model and metadata that supports the business requirements. We ensure standards and consistency are defined and adhered to and provide the bridge between the conceptual, logical, and physical data architectures. Our expertise covers data environment layers for integration (sources, data changes, and output), presentation layer (data changes, table keys), data retention and archival, data security, and code management.
Metadata architecture, design, and delivery foundations
We use our understanding of the underlying data and data lineage — how it is defined and used across all lines of business for sharing and reuse — as the foundation for defining a metadata architecture. The goal, of course, is to support a consistent definition of key business subject areas, measures, metrics, and/or data (e.g., single view of customer).
A metadata solution should identify and prioritize metadata functions that are important to business users’ understanding, navigation and acceptance of the system. To that end, interviews and feedback from the business users feed the design of the solution and help us create the metadata exchange interfaces and user interfaces.
Our expertise covers multi-tiered approaches for peer-to-peer architecture, federated, and hub-and-spoke. We identify and define the metadata functions for data dictionary, logical data lineage, physical lineage, design mappings, impact analysis, data quality reporting, BI semantic presentation, and process metrics.
A fundamental enabler of efficient metadata architecture is that the tools utilized for all aspects of the data warehousing implementation (ETL, data modeling, BI and reporting, rules, spreadsheets, etc.) produce metadata at the appropriate points in the warehouse lifecycle in a manner and format that allows it to be easily referenced and integrated with the larger body of metadata.
As an organization’s data warehouse evolves and needs change or as the information management maturity level dictates, we assist our clients in leveraging emerging technologies such as high-end performance analytic databases, open source, virtualization and cloud computing.
