5 Requirements for Effective Self-Service Data Preparation

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Automated Processes

Any data analyst will tell you how difficult it can be to access, prepare and combine data from a variety of sources. But the real value of data discovery and advanced analytics tools comes only when the right information is brought together, in a timely and trusted manner. The value analysts bring to the business is in the insights they provide, not the mundane time spent on prep work.

Robust data prep technology can automate prep processes based on a predetermined schedule, or when source data changes or becomes available. Automating tasks and updates, such as data acquisition, prep and distribution, drives significant time and cost savings while reducing the potential error associated with redundant manual processes.

Business Intelligence (BI) and Big Data analytics tools have fundamentally transformed the way organizations operate. Business leaders across industries now use Big Data analytics technology for a wide range of processes, objectives and management needs. And the potential applications of modern BI tools are practically endless, as virtually every aspect of operational management and strategic oversight can benefit from more powerful and rapid insights.

But while the technology is there, studies have shown that return on investment (ROI) has been elusive at best for the vast majority of adopters. In fact, business analysts claim that 80 percent of their time is spent preparing data for analysis, and they still never seem to have the information they need.

Self-service data preparation (prep) is a critical, yet often overlooked, factor in the analytics process. In this slideshow, Datawatch Corp, has identified five essential self-service data prep capabilities every analyst must be able to access to derive maximum value from analytics solutions and help their organizations make more meaningful and timely decisions.

 

Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

 
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