“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

Posted: December 3rd, 2009

An in-depth look into data quality in oil rig data within the petroleum industry, including data management steps, data quality control and data quality reporting.

From the Resource:

“The amount of data collected in the information age has grown to amounts barely manageable. Currently available technologies are already capable of transmitting the readings of any sensor to worldwide locations at high frequencies and with nearly no time delay. With and ever-increasing flow of data the need for criteria to measure and evaluate data quality are more pressing than ever as this data forms the basis for many critical business decisions.”

Link to External Resource: Mastering Real-Time Data Quality Control – How to Measure and Manage the Quality of Rig Sensor Data

Source: Wolfgang Mathis, TDE Thonhauser Data Engineering

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

Posted: November 30th, 2009

A detailed look at a major data quality initiative at Dekamarkt – a German supermarket chain with 250 outlets. This Resource contains data quality data flow diagrams, business drivers, IT considerations and detailed 5-step implementation approach. Highly recommended.

Link to External Resource: A Practical Implementation of Data Quality Management

Source: Jan Matto, Mazars

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

Posted: November 27th, 2009

A valuable, 3-part look into key data quality principles and best practices. While focused on supply chain-related data quality, this Resource provides excellent examples and guidance that apply to all data quality initiatives. From the Resource:

“It is data that has, in part, helped to fuel impressive productivity gains that most industries have enjoyed over the past few years. It is data that is fundamental to all performance metrics. And it is data that workers and executives alike have come to rely on to make decisions. Yet few companies treat data as the valuable asset that it is and few acknowledge the risk that poor data quality presents.”

Highly recommended.

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 Best Practices

Posted: November 19th, 2009

“Our marketing effectiveness leads to our sales effectiveness, which leads to our service effectiveness. Data quality is key to the success of that. If you don’t have quality data, that whole chain breaks down.”

Link to External Resource: Hamstrung By Defective Data

Source: Chuck Scoggins, Hilton Hotels

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

Posted: November 18th, 2009

A deep, rich look into achieving data quality (and much more) through Model-Driven Data Governance. Examples, roles, models, and best practices. One of many valuable sections: Role of the Data Architect: How to gain Traction, Budget and Executive buy-in. Highly recommended.

Link to External Resource: Data Architecture for Master Data Governance

Source: Chris Bradley, IPL Group PLC

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

Posted: November 13th, 2009

Healthcare-centric data quality best practices:
1) Standardize data entry fields and processes for entering data,
2) Institute real-time quality checking, including the use of validation and feedback loops,
3) Design data element to avoid errors (for example, through the use of check digits and checking algorithms on numeric identifiers where human entry is involved and the use of well-designed user interfaces,
4) Develop and adhere to guidelines for documenting the care that was provided to the patient,
5) Review automated billing software, and
6) Build human capacity, including training, awareness-building, and organizational change.

Link to External Resource: Background Issues on Data Quality

Source: connectingforhealth.org

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

Posted: November 12th, 2009

“Data quality is corporate America’s dirty little secret.” A valuable look at how data quality can quickly and severely go wrong.

Link to External Resource: Data Quality Problems are Corporate IT’s Dirty Little Secret

Source: Paul Gillin

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

Posted: November 11th, 2009

Problems linked to the use of poor-quality data:

1) poor project, cost, and schedule estimation
2) poor project, cost, and schedule tracking
3) inappropriate staffing levels
4) flawed product architecture and design decisions
5) ineffective and inefficient testing
6) fielding of low quality products
7) ineffective process change.

Link to External Resource: Can You Trust Your Data? Establishing the Need for a Measurement and Analysis Infrastructure Diagnostic

Source: Mark Kasunic, James McCurley, and David Zubrow, Carnegie Mellon

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

Posted: November 10th, 2009

As part of a look into data governance, this Resource provides considerable context for the role and importance of data quality in the enterprise. From the Resource:
“Many people attribute the quality of data to the system from which it was retrieved. However, data must be initially entered into a system. Even derived data was calculated from base data that was, in most cases, entered into a system by a person. Thus, any governance discipline must take into account human factors. Many elements contribute toward poor data quality including lack of authority or the required time to correct data problems. There may be other motivational factors such as lack of interest, concerns regarding exposing errors on the part of others, or a lack of data ownership.”

Link to External Resource: Data Governance – Managing Information As An Enterprise Asset

Source: NASCIO

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

Posted: November 2nd, 2009