Next, we need to bring our expertise and experience to our customers as we attempt to address their business challenges. Organizations continue to encounter issues managing inconsistent data and unifying the tools and approaches needed to take control of today’s data beast. New products and technologies make their way into the market at a rapid pace, and an agile data architecture must be designed in a way that it can integrate both best-in-class and innovative technologies. Take TIBCO® Any Data Hub as an example, the framework offers necessary capabilities to support the demand for accurate and consistent data across the organization with trust and control, aligning IT and the business.
Once we identify key products and technologies that can help address the gaps, we then proceed to define the architecture principles and choose a reference architecture. Having a clearly documented approach for decision-making within the design and implementation process help eliminate any miscommunication or misalignment.
Examples of Data Management design principles are the various patterns or integration styles identified in the industry such as Registry, Consolidation, Co-Existence or Centralized Hub.
We can also find opportunities to save time by leveraging on existing data architectures, if business requirements are not unique to the organization. Some existing reference architectures are data warehouse architecture, data lake architecture, logical data warehouse architecture and unified data delivery platform. Source: Read more on Data on Demand