For example, if the source systems are batch oriented, have no real-time interfaces, or have system load limitations – yet already provide data extracts at regular intervals – the transaction data may reside in an operational data store that is accessed by the Hub dynamically. On the other hand, if source systems possess real-time integration capabilities and are not under severe system loads, the transaction data may be harvested dynamically and cached within the hub for low latency and high availability, removing the need to unnecessarily duplicate source system data in a separate repository. This flexibility can dramatically reduce the volume of data required to be stored in the customer data hub, which reduces the total cost of ownership while increasing the system’s ability to flexibly deliver data in any form, on demand.
Data Model Choice Affects Scalability
Lastly, data model flexibility also has a significant impact on the scalability and performance of the CDI solution. Because the fixed data model solution usually tunes the model a priori to support the native applications, the performance and scalability parameters available to your organization are already constrained by the vendor’s need to support its application architecture. In contrast, to the flexible data model approach, once the model is configured, all the tuning and normalization is done to support the specific scalability and performance requirements of the master data hub project and related consuming applications – not just to support a single vendor’s application. This difference in approach to scalability can have significant effects across the entire data lifecycle of building, using, managing and extending a master data hub.
Recently, the myth that an adaptive solution requires a compromise on performance was dispelled with CDI Benchmarking Results, providing evidence of high performance and scalability – all within an adaptive, extensible architecture.
In summary, data model flexibility is a critical part of an adaptive CDI architecture. An enterprise looking to mitigate risk of their CDI implementation should consider the full effects of their data model choice across several factors outlined above. A wrong decision in this area can increase the project cost, reduce its manageability over time, or in the worst case, doom the entire initiative. About the AuthorAnurag Wadehra is the Vice President of Marketing at Siperian Inc. a leading customer data integration solution provider. The Siperian solution creates the most trustworthy and manageable customer master reference store possible from widely disparate internal and external data sources. It is the foundation for delivering accurate, relevant and actionable 360º customer views. Siperian’s highly manageable and extensible solution enables enterprises to cost-effectively provide trustworthy customer master data to any system or business user, resulting in more efficient and profitable customer relationships, reduced customer data operations costs and increased accuracy of regulatory compliance. Anurag can be contacted at .
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