Data Quality Resource Guide

Solvency II: Will Your Data Let You Down?

Data Quality — Tue, 02 Feb 2010 17:05:48 +0000

This Resource includes an interesting look at how the banking industry handled the data quality requirements of Basel II. From the Resource: “The most common mistake made by many banks in preparing for the adoption of Basel II was to underestimate significantly the scale of data quality problems and the level of effort required to fix them. All too often the focus of attention was on perfecting the capital adequacy algorithms with little attention given to the quality of the data that would ultimately feed them. Many banks have had to revisit their existing processes for data management as these were not sufficiently robust to meet the requirements. Tight timescales have compelled many to force through short-term tactical fixes and, as a result, many continue to face ongoing data clean-up exercises today. These unresolved issues have not only driven up dayto- day operating costs but have also required the banks to hold a higher level of capital to compensate for the increased uncertainty arising from unresolved data issues – thereby directly hitting the bottom line.”

Link to External Resource: Solvency II: Will Your Data Let You Down?

Source: Detica

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality User Success Stories and Case Studies

NSSRS Data Quality Training Initiative

Data Quality — Tue, 02 Feb 2010 16:01:04 +0000

“If an education agency is spending time cleaning data, then the processes are wrong.”

Link to External Resource: NSSRS Data Quality Training Initiative

Source: Nebraska Department of Education

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Quotes by Users

Steps for Ensuring Data Quality

Data Quality — Mon, 11 Jan 2010 14:50:44 +0000

A visual 1-page representation of a multi-step data quality program. While not a detailed plan, this Resource provides interesting aspects to data quality planning. From the Resource:

“Data quality is more than accuracy and reliability. High levels of data quality are achieved when information is valid for the use to which it is applied and when decisionmakers have confidence in and rely upon the data. Implement these steps organization-wide to increase and maintain data quality.”

Link to External Resource: Steps for Ensuring Data Quality

Source: US Department of Education & ESP Solutions Group

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Best Practices

How Good is Your Supply Chain Data Quality?

Data Quality — Thu, 31 Dec 2009 01:05:27 +0000

“How do companies assess data quality? That’s the problem, many do not. Few have a formal method for tracking data quality; they base their assessment on gut feel or may have looked at it as part of a major IT project. Most, however, do not know if they even have a problem.”

Link to External Resource: How Good is Your Supply Chain Data Quality?

Source: Kate Viasek, SupplyChainDigest

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Quotes by Experts

Organizing for Data Quality

Data Quality — Mon, 28 Dec 2009 14:17:28 +0000

The Cost of Poor-Quality Data:

Sales/Marketing: Low customer satisfaction, Many address change requests, No trust/agreement in reporting.
Finance: Budgets take forever to get “right”, Big budget/actual discrepancies, No trust/agreement in reporting.
Supply Chain: “Out-of-stock” situations, Poor-quality products, No trust/agreement in reporting.
IT: Large IT projects fail, Low usage of applications, No trust/agreement in reporting.”

Link to External Resource: Organizing for Data Quality

Source: Andreas Bitterer, Gartner

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Costs & Benefits

UNLV Data Governance

Data Quality — Wed, 16 Dec 2009 13:28:23 +0000

An overview of UNLV’s data governance organization, roles and structure, including a data governance council and data stewards.

Link to External Resource: UNLV Data Governance [Powerpoint]

Source: University of Nevada – Las Vegas

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Implementation Plans and Documents

Tom’s Ten Data Tips

Data Quality — Wed, 16 Dec 2009 13:24:16 +0000

10 Data Quality Assessment Tips:

Data are outdated as soon as they enter the database
Data conversion is a major source of DQ problems
Convincing the CEO a DQ problem exists is a challenge (and a must)
DQ assessment needs to be grounded in (financial) numbers
Establish DQ benchmarks across the company
Consider doing both objective and subjective DQ assessment
Manual reconfirmation is the royal road (to DQ)
Selecting validation samples is tricky business
Data redundancy drives quality
Ad hoc databases are priceless

Link to External Resource: Tom’s Ten Data Tips

Source: Tom Breur, XLNT Consulting

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Best Practices

Developing and Supporting P-20 Education Data Systems: A State of the States

Data Quality — Wed, 16 Dec 2009 13:15:08 +0000

A look into the proactive project initiated by the National Center for Educational Achievement to improve the collection, availability and use of high-quality education data and to implement state longitudinal data systems to improve student achievement. This presentation highlights record matching as a key component of data quality and gives several examples of the initiative’s implementation.

Link to External Resource: Developing and Supporting P-20 Education Data Systems: A State of the States [PDF]

Source: Data Quality Campaign

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality User Success Stories and Case Studies

How to Build A Compelling Business Case For Data Quality

Data Quality — Fri, 04 Dec 2009 14:56:06 +0000

A valuable and impressive data- and research-intensive presentation on why data quality matters, strategies for data quality management and how to make the business case for a data quality initiative.

Link to External Resource: How to Build A Compelling Business Case For Data Quality | PDF

Source: Prof. Dr. Peter Chamoni, University of Duisburg-Essen

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Explored

Red Light, Green Light – Playing the Risk Game

Data Quality — Thu, 03 Dec 2009 20:54:23 +0000

“The common denominator in all successful projects was that the initial cleansing process was the most painful element in each effort. Firms were surprised at how low their initial data quality measured prior to cleansing. This is also the area executives and project sponsors pushed hardest on the people responsible for delivering the projects. The issues being raised by executives around project approval generally stem from the time it takes to do this process versus the cost of the effort. Data managers frequently have to push back against delivering bad data quickly, versus taking the time and effort to deliver accurate data. In some instances, that push back is made easier after the data manager lets the Business Intelligence experience pain from the bad data. Ultimately, the Return on Investment (ROI) is data quality and lower risk.”

Link to External Resource: Red Light, Green Light – Playing the Risk Game | PDF

Source: Adam D. Honore, Aite Group

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality Best Practices