We develop and execute architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise. In today’s highly distributed, multi-platform world, the diversity of data sources challenges the traditional manual approach to data analysis that requires prior collection, storage, and integration. Data virtualization serves as an alternative by offering transparent access to a wide variety of data sources.
Corporate Data Quality Management
The whole set of activities intended to improve corporate data quality (both reactive and preventive). Main premise of CDQM is the business relevance of high-quality corporate data. CDQM comprises with following activity areas:
Strategy for Corporate Data Quality
As CDQM is affected by various business drivers and requires involvement of multiple divisions in an organization; it must be considered a company-wide endeavor.
Corporate Data Quality Controlling
Effective CDQM requires compliance with standards, policies, and procedures. Compliance is monitored according to previously defined metrics and performance indicators and reported to stakeholders.
Corporate Data Quality Organization
CDQM requires clear roles and responsibilities for the use of corporate data. The CDQM organization defines tasks and privileges for decision making for CDQM.
Corporate Data Quality Processes and Methods
In order to handle corporate data properly and in a standardized way across the entire organization and to ensure corporate data quality, standard procedures and guidelines must be embedded in company’s daily processes.
Data Architecture for Corporate Data Quality
The data architecture consists of the data object model - which comprises the unambiguous definition and the conceptual model of corporate data - and the data storage and distribution architecture.
Applications for Corporate Data Quality
Software applications support the activities of Corporate Data Quality Management. Their use must be planned, monitored, managed and continuously improved.
Integrated data management
String Services use Integrated data management (IDM) as a tools approach to facilitate data management and improve performance. IDM consists of an integrated, modular environment to manage enterprise application data, and optimize data-driven applications over its lifetime.
IDM's purpose is to:
- Produce enterprise-ready applications faster
- Improve data access, speed iterative testing
- Empower collaboration between architects, developers and DBAs
- Consistently achieve service level targets
- Automate and simplify operations
- Provide contextual intelligence across the solution stack
- Support business growth
- Accommodate new initiatives without expanding infrastructure
- Simplify application upgrades, consolidation and retirement
- Facilitate alignment, consistency and governance
- Define business policies and standards up front; share, extend, and apply throughout the lifecycle