Understand the data for omnichannel
Once you have a data strategy for omnichannel, what's next? Kim Kaluba explains.
Tag: Data Profiling
Naming things revisited
David Loshin recommends enforcing governed standards to help avoid conflicting analytical results.
Four ways SAS makes Hadoop easy
Clark Bradley explains how SAS can make Hadoop approachable and accessible.
4 steps to get started with data governance
Data governance seems to be the hottest topic at data-related conferences this year, and the question I get asked most often is, “where do we start?” Followed closely by how do we do it, what skills do we need, how do we convince the rest of the organisation to get
Self-service data preparation transforms data professionals into data rock stars
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
Beyond the boundaries of structured data: Part one
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
SAS MDM new release brings harmony to big data discord
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
Provisioning data for advanced analytics in Hadoop
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
SAS Data Loader for Hadoop helps your data heroes navigate the fire swamp of big data
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
Lack of knowledge and the root-cause myth
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
A simple technique for improving data accuracy
Dylan Jones says one way to improve data accuracy is to increase the frequency and quality of reality checks.
A data governance primer, part 3: Data quality analysis and the diamond in the rough
The third part of my data governance primer series addresses data quality analysis. Don’t even start a data quality analysis until you have completed the first two steps of your root cause analysis: investigate and prioritize any potential causative factors, then start your metadata assessment. Otherwise, you may be misled
A data governance primer, part 2: minding your metadata
The second part of my data governance primer series addresses ways to "mind your metadata." I can just hear the collective groans, and perhaps a stifled yawn. Sorry, but metadata collection is one of those necessary evils that may not be fun, but having it available as a resource to
Making information accuracy BAU
Dylan Jones (@dataqualitypro) shares an example of why data quality should be a business-as-usual practice.
Getting schooled on measurement
Are your data quality metrics making the important measurable instead of making the measurable important?
Where to start with overloading
Dylan Jones (@dataqualitypro) explains how data overloading occurs -- and how to find a better solution.
20 encounters of the information management kind - #2 SAP as the source system of choice
I have encountered quite a few companies that are now anticipating the move of as many of their source systems as possible into SAP. I think this is probably a good decision for quite a few of these organizations. However, in doing so, we must keep or create data management
Getting ready to be a better company
Joyce Norris-Montanari declares this the perfect time to prepare your data for 2013. See what her plans entail.
3 Tips for more effective data profiling
Blogger Dylan Jones offers 3 tips for more effective data profiling.
The value of zero
Do null sets always spell bad news? @philsimon says no. Find out why.