Data Warehousing and Business Intelligence
Data warehousing used to be IT's weapon of choice for corralling the "islands of data" and bringing order to the decentralized information chaos. However, shifting business priorities and the emergence of a new set of technology solutions have changed the landscape and the complexity of managing the abundance of enterprise data.
As the complexities of the business change it has become paramount for organizations across the globe not to just capitalize on their ability to create, collect and store information, but to also know how they can exploit the business intelligence in the most advantageous way. Some of the factors that are driving the business houses towards deploying a Near Real Time reporting and analytical architecture are as follows:
- Rapidly changing business scenarios
- Decision making occurs more frequently and at all levels in the organization
- Trend towards democratization of information and broadening of decision-making responsibilities
- Near real time reporting and analytical architectures are no longer a "nice to have"
- The need of the hour is to unlock the value within the organization and understand the customer better.
Each data warehouse is unique because it must adapt to the needs of business users in different functional areas, whose companies face different business conditions and competitive pressures. Nonetheless, four major approaches to building a data-warehousing environment exist. These architectures are generally referred to as:
- Top-down
- Bottom-up
- Hybrid, and
- Federated
Most organizations, wittingly or not, follow one or another of these approaches as a blueprint for development.
The current trend is towards active data warehousing, which is rapidly changing the landscape for deployment of decision-support capability. The challenges of supporting extreme service levels in the areas of performance, availability, and data freshness demand new methods for DW construction. Particular attention is paid to architectural topologies for successful implementation and the role of frameworks for enterprise application integration (EAI). This approach provides as a means for incrementally delivering business value in the path toward advanced decision-support capability.
Data access and delivery technologies such as EII (enterprise information integration), EAI (enterprise application integration), and ETL (extract, transformation, and load) are offering companies ways to be clever and more deliberate about delivering data to systems and users more effectively. And with the emergence of customer data integration (CDI) and master data management (MDM) solutions, there's an entirely new set of offerings to consider when integrating corporate information from across packaged applications, core platforms, and legacy systems.
IPCS provides best practices and technical expertise to clients highlighting the importance of generating right information at the right time for the right person. Our approach to active data warehousing implementations involve:
- Active Data Warehouse (DW) definitions and framework
- Evolutionary steps toward active DW deployment
- The architecture of an active DW
- The standard alternatives for data integration
- EAI, EII and ETL - and how they're different
- How data integration solutions and metadata co-exist?
- How CDI and MDM solve the problem?
- Architectures that work
- Implementing extreme performance, data freshness, and availability
- CRM and the active DW
- Role of enterprise application integration (EAI)
IPCS has implemented variety of projects in data warehousing and business intelligence with the focus in generating "Real Time Information" by reengineering the processes within the applications, implementing architectural topologies with workflow and rules technology and EAI.
