![Understand the data for omnichannel Male store owner tries to understand the data for omnichannel](https://blogs.sas.com/content/datamanagement/files/2017/11/623458544-702x336.jpg)
Once you have a data strategy for omnichannel, what's next? Kim Kaluba explains.
Once you have a data strategy for omnichannel, what's next? Kim Kaluba explains.
David Loshin recommends enforcing governed standards to help avoid conflicting analytical results.
Clark Bradley explains how SAS can make Hadoop approachable and accessible.
When my band first started and was in need of a sound system, we bought a pair of cheap yet indestructible Peavey speakers, some Radio Shack microphones and a power mixer. The result? We sounded awful and often split our ear drums from high-pitched feedback and raw, untrained vocals. It took us years
Most people have logged on to a social media site, maybe to look up an old friend, acquaintance or family member. Some people play games, or post funny pictures or other information they want to share with everyone. Do you ever ask yourself what happens with this information? What if your business wanted to purchase this information and
I've been in many bands over the years- from rock to jazz to orchestra - and each brings with it a different maturity, skill level, attitude, and challenge. Rock is arguably the easiest (and the most fun!) to play, as it involves the least members, lowest skill level, a goodly amount of drama, and the
The data lake is a great place to take a swim, but is the water clean? My colleague, Matthew Magne, compared big data to the Fire Swamp from The Princess Bride, and it can seem that foreboding. The questions we need to ask are: How was the data transformed and
In The Princess Bride, one of my favorite movies, our hero Westley – in an attempt to save his love, Buttercup – has to navigate the Fire Swamp. There, Westley and Buttercup encounter fire spouts, quicksand and the dreaded rodents of unusual size (RUS's). Each time he has a response to the
A lot of data quality projects kick off in the quest for root-cause discovery. Sometimes they’ll get lucky and find a coding error or some data entry ‘finger flubs’ that are the culprit. Of course, data quality tools can help a great deal in speeding up this process by automating
Data profiling is essential. So why do so many data quality teams fail to get the most out of this crucial technique? In my short video, you’ll discover the answers to unlocking the full potential of your data profiling efforts. By broadening and deepening your knowledge of data profiling with