Covering the world of big data and data governance. Monday, April 17, 2017. Avoiding the three common myths of big data. There are many common myths when it comes to big data analytics. Like the lost city of Atlantis and the Bermuda Triangle, they seem to be ubiquitous in the teachings of big data. You often see the discussion about big data transition right into how to solve any issue on Hadoop. Has been handling huge loads of data.

A search for the elusive spell book. 8211; Robert A Heinlein. If you torture data long enough, it will tell you anything you want! Thursday, January 29, 2009. By the way, the main reason why current taxonomies are usage-o.

The heart of DataCleaner is a strong data profiling engine for discovering and analyzing the quality of your data. Find the patterns, missing values, character sets and other characteristics of your data values. Profiling is an essential activity of any Data Quality, Master Data Management or Data Governance program. Monitoring is a central aspect with DataCleaner - to .

Posted November 13, 2008. Data Hygiene are principles and practices that serve to maintain accuracy in computer data. This term originated back around 1995 in the direct mail industry. Posted November 23, 2013.

A fast, easy to use, visual. Data profiling and transformation tool. Profile your data- find data quality issues. Powerful Excel import and export. After doing extensive research, Datamartist was the clear choice. An easy to use data profiling tool for analyzing format, types, completeness and value counts. Understand data quality issues clearly and quickly. Transform the data in a graphical ETL environment with a library of different data blocks. Export out to files or directly to databases.

Get your data migration checklist. The most comprehensive data migration project checklist and start planning your next migration with confidence. Learn practical tips for migrating to SAP SuccessFactors, featuring Miles Davies from DominusData. Promoting DQ Tools for Data Migration Projects.

A grassroots look at Data Quality. Something for the data quality analyst in the trenches. Tuesday, March 2, 2010. A gold DQ team! With the Olympic Games come and gone, and the NHL hockey season fast approaching the playoffs I can not help but take a moment, although brief, to state that a golden data quality team should resemble a golden hockey team. You need people with technical skills, people who can read and analyse data models with ease. The defenders on a data quality team, see the big picture.

A blog about data quality, data as a services and creating a single customer view. We value data, data has value to you - lets talk about it. I am not Eddy! Budget for Data Quality seems no problem. Seven tips for success in implementing CDI. What your data tells you about your processes. Posted on November 7, 2008.