by Chris Cingrani on December 21, 2006 – 5:11 pm
In my last blog post, I discussed the topic of building the business case for data quality. As such, one of the points I mentioned was the need to highlight resulting issues. Since my last post, I have had a number of discussions with clients and prospects on this topic. At the core of these discussions is the same fundamental question – what is the best way to package up the issues we uncover? In answering this, I often discuss the six dimensions of data quality (Completeness, Conformity, Consistency, Accuracy, Integrity, and Duplicates) and how to use a data quality scorecard to present the information in a meaningful way that it can be shared with key stakeholders within the organization. Although my response to this question remains the same, a conversation I overheard at the airport a couple of weeks ago made me look at the need for a DQ scorecard a little differently.
While grabbing a bite to eat prior to a flight, I overheard two gentlemen who were both retired from the newspaper business discussing how people don’t really take the time to read a newspaper like they used to. They were lamenting that people today preferred quick sound bites of information – whether it be from television or from reading one of the various news sites on the Internet.
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by Larry English on December 19, 2006 – 1:46 pm
All he wanted for Christmas was anything but what he got. Jeffrey Skilling, former Enron CEO moved to his new residence at the Federal Correctional Institution in Waseca, Minnesota, where his sentence calls for him to live for the next 24 years for his role in fraud, conspiracy, insider trading and other crimes leading to the collapse of Enron. These crimes led to the loss of thousands of jobs, more than $60 billion in company stock and more than $2 billion in employee pension plans.
But Mr. Skilling will have a new job as well. He will probably work as a food service helper, painter or plumber. While this is not the cush job he had as CEO at Enron where he earned $151.7 million over the three years during the time he perpetuated his fraud, he will get from 14 to 40 cents per hour. At the top pay, Skilling could earn $832 per year. At that rate it would take 74.5 million years to pay back the stock and pension losses he foisted on the stakeholders.
So what is the point here? Read More »
by Larry English on December 18, 2006 – 1:32 pm
What would happen if your knowledge workers returned from the holidays and when they “opened” their data marts, they found nothing but high quality information? No missing information to have to hunt for. No wrong information to have to correct. No misleading information to cause them to make the wrong decision.
Imagine what it would be like if people could do their value work without hunting for, correcting, or recovering from failure caused by poor quality
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by Garry Moroney on December 15, 2006 – 3:55 am
There is no doubt that data quality and data integration are intrinsically symbiotic activities, but where does one end and the other begin? Is data quality just part of data integration? Are they separate but linked or one in the same? Some industry observers are convinced that data quality is purely a component of data integration, and that it only makes sense to deploy data quality as an integral part of the data integration process.
As head of Similarity Systems, a company that provided pure data quality solutions prior to its acquisition by Informatica, I would have disagreed strongly with this point of view. For me data quality and data integration were linked but separate disciplines. Data quality technology is required in many different parts of an organization and is often controlled by different owners than those who require data integration.
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by Garry Moroney on December 8, 2006 – 8:43 am
It’s coming up to the end of my first year as Head of Informatica’s Data Quality Division and what a year it’s been. Data Quality has long been an obsession of mine – long before Informatica acquired the data quality software company I headed up, Similarity Systems, and even before my colleagues and I founded Similarity six years earlier.
We set up Similarity Systems in 2000 because of our absolute belief that data quality was on the cusp of making a breakthrough as one of the critical performance drivers for large businesses and organizations everywhere.
Over the years since then we have seen data quality move rapidly up the agenda. Data was once the sole preserve of IT – but today boardroom executives already have found reason to talk about and care about data and data quality – Good data quality can be the foundation for success, while poor data quality is a root cause of failure in many of the key initiatives for today’s businesses and government organizations. These executives know customer service is a data quality issue, compliance is a data quality issue, supply chain automation is a data quality issue … I could go on, but there will lots of time for that later.
My goal in writing this blog is to share views and experience with others who are passionate about data quality. I see it as a forum for widening understanding of the enormous business value that can be generated through active, effective data quality management.
My days revolve around meeting with data quality customers, meeting with partners and working with our own product development and implementation specialists. Through this blog I hope to share some of the insights and experience garnered from this day-to-day interaction with these groups. And hopefully my conversations with these groups will be influenced by the feedback I receive from you through this blog.
I have set myself only two guidelines. I’ll be aiming to stick to unerringly to them:
• Keep it short (because I’m busy and you probably are too)
• Keep it business focused (because data quality is a business problem opportunity)
Until next time…
