As organizations process more information at faster rates, there is increased pressure for faster and more efficient data integration programs.
Data integration is an intermediary function that is critical for downstream functions of data management and business operations to be successful.
Evolving business models and uses of data are growing rapidly at rates that often exceed the investments in data management and integration tools, and as a result there is often a gap between data availability and the business’s latency demands.
Identifying the right pattern for your data use cases is only part of the battle. More times than not, success in data integration is hinged on the performance of activities in development, architecture, governance, and quality.
Successful data integration solutions require more than just technology – they require design, planning, governance, and maintenance.
Impact and Result
Create a data integration program that supports the flow of data through the organization and meets the organization’s requirements around data latency.
Ensure that the necessary architecture, governance, MDM, and quality building blocks support your data integrations.
Build your data integration practice with a firm foundation in governance and reference architecture. Use best-fit reference architecture patterns and the related technology and resources to ensure that your process is scalable and sustainable.
Cloud is disrupting how traditional data integrations are performed; with new deployment methods and locations of data, new decisions around integration points and types of services must also be evaluated.
The business’s uses of data are constantly changing and evolving, and as a result the integration processes that ensure data availability must be frequently reviewed and repositioned in order to continue to grow with the business.