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CloverETL Profiler beta

We are launching a new tool in the CloverETL family. The CloverETL Profiler, software for data profiling, works to examine data in existing data sources and collecting statistical information about said data.  

CloverETL Profiler public beta testing started in late October and lasted untill the mid December. The Profiler is going to be fully released in February 2012.


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What is the Profiler?

CloverETL Profiler is the fast, accurate way to examine the condition your data is currently in. Using statistical examination, the profiler exposes recurring patterns and anomalies as well as uncovers duplicate or missing data in the source.

Through this process, the profiler eliminates the guesswork in analysis, revealing an understanding of what data actually exists and what needs to be improved.




CloverETL Profiler User Interface

"Dirty" Data

When making business decisions based on data, it’s essential that it be correct. Flawed or incomplete data can lead to backtracking in a project and often, poor quality business decisions. This can be avoided from the outset if data is viewed with a critical eye--and a critical tool.

 

Profiling and Data Quality

CloverETL Profiler operates under the principle that clean data is the basis for making informed business decisions. By identifying these data inconsistencies, the profiler can help users better understand the nature of their data. From there, goals to improve the data can be developed, then implemented during a data quality job.

CloverETL Technology

Part of the CloverETL Data Integration family, the Profiler is an added tool to the enhanced toolset. Whether employed as a standalone job or part of a greater project, the CloverETL Profiler, as named, operates with the same Engine as CloverETL, offering high performance, speed, and easy deployment in a data environment.



Web-based Reporting Console


Time Unit Chart

Business Advantages

  • Assesses quality and consistency of data to allow for validation of certain assumptions about the data, its structure, and values.
  • Presents an objective picture of the data and its characteristics quickly and efficiently.
  • Informs decisions regarding data quality goals.
  • Helps users gauge issues in data entry occuring on the front end.
  • Used to continually monitor data in an ongoing process during projects involving said data.