Written by Tamr
Business analysts and application developers are under increasing pressure to generate clean, comprehensive datasets for analysis to drive business value.
Without high quality, unified data, downstream analytics are ineffective and, in most cases, time is of the essence when generating insight. Markets and opportunities move very quickly and businesses need the capability to respond even faster with insights that matter. A failure to do so results, at a minimum, in slower growth and foregone profit.
Unfortunately, the current operating environment of most enterprises isn’t architected to meet these requirements for a variety of reasons, including:
+ Amount of manual effort required to integrate sources: Integrating disparate data sources into a unified, clean dataset requires significant manual labor. Programmers are needed to interrogate individual datasets, map them to target schemas, and eliminate duplicate records. This slows down the process of acquiring needed datasets and creates a large backlog for IT.
+ Previous work is rarely reused: Whether it’s transformations written by IT or similar projects created by colleagues, completed work is rarely reused in today’s environments. This inability to discover and leverage existing work hinders time to value and forces analysts to recreate existing assets.
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