Big data quality – Part 1
Jim Harris addresses some of the most common questions and challenges big data poses for data quality.
Tag: data quality
Overcoming the IT-Business Divide in an era of big data, Part 3
.@philsimon on bridging the IT-business divide once and for all.
SAS Data Management helps organizations boldly go where no one has gone before
As a youngster in the 70s and 80s, Star Trek inspired my imagination and fostered a great love for science, technology and reading. (See the embedded Star Trek infographic for some interesting factoids – did you know that there were 28 crew member deaths by those wearing red shirts?) Captain Kirk and the
Data integration – Job skills required for success
Data integration, on any project, can be very complex – and it requires a tremendous amount of detail. The person I would pick for my data integration team would have the following skills and characteristics: Has an enterprise perspective of data integration, data quality and extraction, transformation and load (ETL): Understands
Tips for successful big data integration
Integrating big data into existing data management processes and programs has become something of a siren call for organizations on the odyssey to become 21st century data-driven enterprises. To help save some lost time, this post offers a few tips for successful big data integration.
Data quality to "DI" for
There is a time and a place for everything, but the time and place for data quality (DQ) in data integration (DI) efforts always seems like a thing everyone’s not quite sure about. I have previously blogged about the dangers of waiting until the middle of DI to consider, or become forced
Data quality on Hadoop: The easy way
Bigger doesn’t always mean better. And that’s often the case with big data. Your data quality (DQ) problem – no denial, please – often only magnifies when you get bigger data sets. Having more unstructured data adds another level of complexity. The need for data quality on Hadoop is shown by user
What Zappos' bold moves teach us about data governance
.@philsimon on whether companies should apply some radical tactics to DG.
Struggling with data governance alignment? Look to history.
If your organization is large enough, it probably has multiple data-related initiatives going on at any given time. Perhaps a new data warehouse is planned, an ERP upgrade is imminent or a data quality project is underway. Whatever the initiative, it may raise questions around data governance – closely followed by discussions about the
Data policies and data governance
In recent years, we practitioners in the data management world have been pretty quick to conflate “data governance” with “data quality” and “metadata.” Many tools marketed under "data governance" have emerged – yet when you inspect their capabilities, you see that in many ways these tools largely encompass data validation and data standardization. Unfortunately, we