For decades, data quality experts have been telling us poor quality is bad for our data, bad for our decisions, bad for our business and just plain all around bad, bad, bad – did I already mention it’s bad? So why does poor data quality continue to exist and persist?
As the “Year of Statistics” comes to a close, I write this blog in support of the many statisticians who carefully fulfil their analysis tasks day by day, and to defend what may appear to be demanding behavior when it comes to data requirements. How do statisticians get this reputation? Are we
“The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.” ~ Warren G. Bennis The promise of digital manufacturing is drawing closer to
In my previous post, I used a game show metaphor for one aspect of metadata management, namely making sure table definitions are not ambiguously labeled. In this post, I will use name tags as a metaphor to discuss an important intersection of metadata management and master data management (MDM), an
Loraine Lawson recently used the Eight-Fold Path of Buddhism, in which practitioners are encouraged to pursue right views, intentions, speech, actions, livelihood, efforts, mindfulness and concentration, as inspiration for her blog post The Five-Fold Path for Ensuring Data = Information. The post offered five recommendations for ensuring that data is transformed into
Since tomorrow is How-long-has-it-been-since-you-used-this-data-ween, it’s time to review your organization’s preparedness for preventing the zombie data-pocalypse. (Please Note: This should not be confused with your organization’s preparedness for preventing the zombie apocalypse, for which check out the resources provided by the Centers for Disease Control and Prevention by ever-so-carefully clicking on
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
In the era of big data, Kenneth Cukier and Viktor Mayer-Schonberger noted in their book Big Data: A Revolution That Will Transform How We Live, Work, and Think, “we are in the midst of a great infrastructure project that in some ways rivals those of the past, from the Roman aqueducts
My aunt Susanne is an elderly lady, who lives at the countryside and looks forward to celebrating her 80th birthday soon. Since the 1960's she has had a telephone connection with her fixed line provider. At that time, and for many years later, in the country where my aunt lives,
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
I've written, talked and thought about data management for an entire decade now. In that time, I've collected examples of how data – and the processes for managing that data – can affect our everyday life. For years I used the "Have you ever gotten multiple pieces of mail from
Dylan Jones (@dataqualitypro) shares an example of why data quality should be a business-as-usual practice.
Are your data quality metrics making the important measurable instead of making the measurable important?
Dylan Jones (@dataqualitypro) explains how data overloading occurs -- and how to find a better solution.
Don’t worry! This is not an excerpt from a romantic love letter. The title of this blog post is an allusion to my talk on "Missing Values", at the A2013 conference in June in London. There is not much time for emotions: dealing with missing values in analysis is not
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
Our recent innovations into Data Visualisation have led us to be inundated and involved in lots of conversations with customers and prospects looking to benefit from the marriage of advanced analytics, visualisation, data management and simplicity. Greg and Minh delved into the benefits that this marriage can yield and why data visualisation should form an
Joyce Norris-Montanari declares this the perfect time to prepare your data for 2013. See what her plans entail.
Blogger Dylan Jones offers 3 tips for more effective data profiling.
Do null sets always spell bad news? @philsimon says no. Find out why.
This is a dramatic interpretation of an actual conversation I recently had with the CIO of one of North Carolina’s leading cities. We discussed his experience using data quality, data integration, business intelligence and analytics in the daily operation of the city. I may have taken some...well, a lot of
A women’s specialty retailer operating more than 1,000 boutiques, Chico’s FAS, Inc. was faced with a significant challenge: reducing the number of markdowns while more effectively targeting its middle- to high-income clientele. To achieve these goals, the company needed to optimize its prices and more effectively target its most valuable customers,
The downturn in the economy beginning in 2008 and continuing even to now has put tremendous pressure on local governments to do “less with less”. In the past when economic downturns caused service level cut backs the cry was to do “more with less”. The idea was to identify ways
The recent mail bomb attempt on US-bound cargo and commercial airliners is another reminder of the terrorist threat the United States continues to face. While there may be a lull in the news cycles from time to time, the threat is there every day. A key component in detecting and
What's the Fifth Law of Data Quality? Jim Harris explains.
I’ll admit I am particularly fond of a saying, “Begin at the beginning.” All too often we get ahead of ourselves when trying to tackle a problem. And without a clear understanding of the full scope of a problem, there’s always the risk of making it worse. Something like this
Data integration is not data warehousing, says Claudia Imhoff in a post that discusses data quality, master data management and operational data stores. According to Imhoff: None of these is a data warehouse project. They should stand on their own two feet as independent initiatives that just happen to make
