Informatica Data Quality Blog

Posted by Tom Golden in: Data Quality > Best Practices ; Data Quality ; Data Quality > Management ; Data Quality > Monitoring > Metrics ; Data Quality > Monitoring ; Data Quality > Monitoring > Scorecards

It all got me thinking about a recent blog post by my esteemed colleague Garry Moroney. His post Mobilizing the Data Quality Army outlined the level of effort, thought and planning that the US Department of Education is putting into data quality.

As Garry points out dealing with data quality in a large, disconnected organization such as the US schools system is not a trivial exercise. But if you were to only read that one post you might be overwhelmed by the potential size of the data quality task in front of you.

Continue reading "Start small with monitoring, but always think big to achieve data quality goals" »

Posted by Tom Golden at 07:50 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Ivan Chong in: Data Quality > Best Practices ; Data Quality ; Data Quality > Management ; Data Quality > Technology

IT departments are highly motivated to increase the level of collaboration with their counterparts in the business. Nowhere is this more critical than in the area of data quality and the trend is causing a shift in the way companies are looking to solve their data quality issues. First generation data quality tools had a natural focus on technology, instead of business. Here are some of the differences between technology focused data quality solutions and business-focused data quality solutions.

Tools vs. Process
Technology focused data quality solutions provide tools that automate data processing. Evidence of this type of focus can be seen in the way that vendors will tout the sophistication and type of their algorithms over and above their ability to support ongoing data quality management processes. While technology is extremely important, its relevance cannot eclipse the overall data quality management process. Even if your data quality tool can automate the correction of 95 percent of the data, if the remaining five percent cannot be managed properly, you will continue to suffer from poor data quality.

Continue reading "Business and IT Collaboration is Essential for Data Quality" »

Posted by Ivan Chong at 12:05 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Larry English in: Data Quality > Benefits ; Data Quality > Best Practices ; Data Quality ; Data Quality > Management ; Data Quality > Monitoring

“The thing that they all have in common is a desire to cut corners and deal with quality later. It takes a lot of energy to be the information quality cheerleader, and I find it discouraging and overwhelming at times. Keep writing your articles and books to encourage all the people like me who are dealing with these issues every day.” P. G.

The discovery that P. G. has experienced is, unfortunately, the norm—not the exception. There are two critical elements in this experience.

Continue reading "Information Quality & Management Transformation" »

Posted by Larry English at 07:59 AM | Permalink | Comments (2) | TrackBacks (0)

Posted by Neil Gow in: Data Quality > Best Practices ; Data Quality ; Data Quality > Governance / Stewardship ; Data Quality > Management

Alice: Would you tell me, please, which way I ought to go from here?
The Cheshire Cat: That depends a good deal on where you want to get to
Alice: I don't much care where.
The Cheshire Cat: Then it doesn't much matter which way you go
– Lewis Carroll, Alice's Adventures in Wonderland

When confronted with the problem of how to address their data quality issues many organisations are faced with a similar dilemma to that which confronted Alice during her travels in Wonderland; “I know that I need to do something, but I don’t know where to start”. Knowing where to start and, equally importantly, the size of the problem as well as where an organisation needs to go are critical factors in ensuring that their data quality journey takes them where they need to be at the price they are prepared to pay.

When planning their “journey” organisations need to address the issue of data quality holistically by considering each of the three DQ pillars in turn; firstly “People”, then “Ideas” and finally “Technology”. Many DQ initiatives have failed as the primary focus has been on delivering a technical solution. However without the right framework in place and operated by the right people this approach will never deliver the results that organisations need. Time and time again within the IT industry it has been proved that the pure application of technology will never solve business issues, as technology in itself will never win the “war”, it is always the right people with the right ideas who use the technology in the right way.

Continue reading "Alice in “Qualityland"" »

Posted by Neil Gow at 06:20 AM | Permalink | Comments (1) | TrackBacks (0)

Posted by Garry Moroney in: Data Quality > Best Practices ; Data Quality ; Data Quality > Governance / Stewardship ; Data Quality > Management

I’ve just been reading a US Department of Education briefing document on improving data quality in education performance data. The report stresses the impact that low quality data can have on measuring the success of education programs. It discusses for example the numerous data quality problems identified in the “No child left behind” program established in 2001. The problems are typical – non-standardized data definitions, inconsistent data from different sources, data entry errors, lack of timeliness.

The briefing document outlines a broad set of data quality guidelines to be implemented right across the education system in the US – at State level, in Local Education Agencies (LEAs) and in schools themselves. The three foundation stones of the data quality framework outlined are:

• suitable technical infrastructure,
• a comprehensive dictionary of data definitions
• staff ownership, organization and training

Continue reading "Mobilizing the Data Quality Army" »

Posted by Garry Moroney at 07:27 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Garry Moroney in: Data Quality ; Data Quality > Management ; Data Quality > Technology ; Data Quality > Vertical Solutions

Because of these global standards and references, there is general agreement on what a complete, valid and correctly formatted address should look like - likewise person or business name, telephone number, date of birth, email address etc. So this means that if I am sharing my customer data with my business partners at least we have a common view of what high quality data should look like and the checks we need to make to assess the quality levels.

Another huge benefit is that third party service providers and technology vendors also understand the requirements and standards by which to measure and improve customer data and they know that these requirements are largely the same for all vendors. As a result large numbers of service bureaux and technology vendors are able to offer well developed, generic, out-of-the-box products and services to tackle customer data quality issues. These can deliver a lot of value with minimal or no customization and the effort to acquire and implement these solutions is small.

Continue reading "If all master data was like customer data . . ." »

Posted by Garry Moroney at 07:18 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Garry Moroney in: Data Quality > Benefits ; Data Quality > Best Practices ; Data Quality ; Data Quality > Management

For an organization trying to estimate the total returns across the enterprise from a data quality initiative, there are two difficult questions that must be addressed:

• How valuable is this dataset to the enterprise - assuming 100% data quality?
• How does its value decrease as quality erodes?

While these questions might at first seem unanswerable, it is worth noting that these are not unusual questions for a business to ask. In fact businesses need to be able to answer questions of worth and depreciation for all their tangible assets - property, stock etc.

Unfortunately data is one of those intangible assets where normal valuation approaches like recorded cost or replacement value are ineffective. But there are other intangible assets such as IPR, work-in-progress, customer and partner relationships (good will) where significant research has been done to develop effective valuation methodologies. It just might be possible to leverage these methodologies to value your data. For example, the value of customer data is directly related to the value of the customers themselves and so "customer lifetime value" methodologies should be applicable in estimating the value of customer data and the extent to which this value varies with data quality.

Have any of you out there attempted to put a real value on your company data in this way? Perhaps you'd be willing to share your experiences with us.

For more information on building a business case for data quality and calculating potential return on investment see the Informatica white papers: Data Quality Profiling Calculating ROI for Data Migration and Data Integration Projects
and The Data Quality Business Case—Projecting Return on Investment.

Posted by Garry Moroney at 03:36 AM | Permalink | Comments (3) | TrackBacks (0)

Posted by Larry English in: Data Quality ; Data Quality > Governance / Stewardship ; Data Quality > Management ; Data Quality > Technology

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?

Continue reading "Information Quality and Accountability" »

Posted by Larry English at 01:46 PM | Permalink | Comments (0) | TrackBacks (0)

Posted by Garry Moroney in: Data Quality ; Data Quality > Management

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…

Posted by Garry Moroney at 08:43 AM | Permalink | Comments (0) | TrackBacks (0)