As a central repository and processing engine, data lakes hold great promise for raising return on data assets (RDA).  Bringing analytics directly to different data in its native formats can accelerate time-to-value by providing data scientists and business users with increased flexibility and…

Data Platform-as-a-Service (dPaaS) represents a new approach to efficiently blend people, processes and technologies.  A customizable dPaaS with unified integration and data management enables organizations to harness the value of their data assets to improve decision outcomes and operating performance.

dPaaS provides enterprise-class scalability enabling users…

Dynamic data integration incorporating data quality and master data management (MDM) assures consistency and reliability for upstream analytics and information sharing.  A pragmatic approach that treats all of a company’s data as big data will facilitate integration efforts.

In Part I, we stressed the importance of data quality.  In this post, we focus on MDM and the connection between the two as part of data governance strategy.

As financial institutions seek to become…

Distributed architectures with more fragmented data sets beget the need for a dynamic data integration platform that is flexible and scalable to bridge existing enterprise infrastructure to newer apps developed for cloud and mobility.  Data quality and master data management (MDM) are vital components of data integration to ensure consistency and reliability for upstream analytics. 

But in order to succeed, firms need to first establish and commit to an ongoing data governance program…