Data quality as a core component of Corporate/Business Performance Management: “As the appetite for consistent, reliable performance information continues to grow, accompanied by the demands of increasingly stringent compliance legislation, many organizations are recognizing the indisputable importance of data quality. If performance management solutions are to be effective, it is imperative they be built on a foundation of high-quality data that delivers a single version of operational and financial performance. This is a non-negotiable requirement and it is the first metric that is fundamental to successful performance management deployments.”

Link to External Resource: Data Quality is Key to Performance Management

Source: Ram Guduru, CXOtoday.com

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

Posted: October 29th, 2009

As part of an operating document describing the West Berkshire Council’s strategy for improving and maintaining the quality of data created and held by the authority, seven critical data quality success factors are shared:
1) Awareness: The need for quality data is recognised and all staff understand their role in achieving it.
2) Definition: All performance indicators are adequately defined and the reasons for their reporting is understood.
3) Input: Data should be entered in an accurate and timely manner.
4) Verification: The accuracy of data should be verified as close to the point of capture as possible.
5) Systems: All systems, electronic or otherwise, should be fit for purpose and the operation understood by staff entering and retrieving data.
6) Output: Performance information should be extracted and communicated in good time to ensure currency for decision making.
7) Presentation: Performance information and the way it is obtained should be presented in such a way as to be easily understood.”

Link to External Resource: Data Quality Strategy

Source: West Berkshire Council

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

Posted: October 28th, 2009

A short introduction to data quality from a survey-response perspective. From the Resource: “We all know what data quality is. It is good data, and that is that. Or is data quality that simple? When one looks closely at data quality, things get smokier and less focused, rather than clearer and more focused. It turns out that the very definition of data quality is quite unclear. At the heart of the matter is the question: When someone puts down an answer or a value, who are we to second-guess whether the person has put down a correct value? One school of thought says that we ought to go around and correct data values whenever and wherever we can.”

Link to External Resource: Elusive Data Quality

Source: Bill Inmon

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

Posted: October 27th, 2009

A rich, detailed look into how a quality management process is modeled and used in an international wholesale bank with branches, business units and subsidiaries all over the world. This lengthy Resource contains examples, scenarios, application samples and a detailed methodology review. From the Resource: “The Bank has set up a global IT and communication network in order to comply with the requirements resulting from its global presence. Managing the data and information flows of the heterogeneous systems and process landscapes involved is a challenging task. High data quality provides the basis for correct decisions and adequate risk management.”

Link to External Resource: Cross-Department Solving of Data Quality Weaknesses

Source: Andrea Piro, Gerhard Knoke, and Marcus Gebauer, WestLB AG

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

Posted: October 26th, 2009

A phenomenal look at British Telecom’s company-wide data quality initiative which claims $1.1 Billion in data quality-driven bottom line impact. This Resource is full of data quality-specific best practices including British Telecom’s Data Quality Methodology, data quality re-engineering and consolidation, lessons learned and much more. Highly recommended. From the Resource: “Data Quality Business Alignment Lessons Learned: 1) Link DQ to strategic objectives. 2) Know business ‘hot spots’ & drivers and connect. 3) Ride on existing initiatives. 4) Explain DQ problems in the language of the business. 5) Do stakeholder analysis. 6) DQ not an end in itself.”

Link to External Resource: Data Quality? Don’t Waste your Time | PDF

Source: Nigel Turner & Dave Evans, British Telecom

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

Posted: October 23rd, 2009

As part of adopting a data quality framework, the Bank of England shares the following definitions of data quality dimensions: “1) Relevance: Relevance is the degree to which statistics meet current and potential users’ needs. 2) Accuracy: Accuracy in the general statistical sense denotes the closeness of computations or estimates to the exact or true values. 3) Timeliness and Punctuality: Timeliness reflects the length of time between availability and the event or phenomenon described. Punctuality refers to the time lag between the release date of data and the target date when it should have been delivered. 4) Accessibility and Clarity: Accessibility refers to the physical conditions in which users can obtain data. Clarity refers to the data’s information environment including appropriate metadata. 5) Comparability: Comparability aims at measuring the impact of differences in applied statistical concepts and measurement tools/procedures when statistics are compared between geographical areas, nongeographical domains, or over time. 6) Coherence: Coherence of statistics is their adequacy to be reliably combined in different ways and for various uses.”

Link to External Resource: Assessing Data Quality

Source: Bank of England/Eurostat

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

Posted: October 22nd, 2009

“A major issue facing the Corporation is the quality of data. Without accurate data, none of us – staff assistants, investment officers, financial officers, or senior management – can make educated decisions or manage risks. Not having accurate and timely data puts all of us at great risk from both an operational and a reputational perspective. Some groups within IFC have tried to address this problem already and have created pockets of clean data; however, this is not enough. We must have clean data throughout the Corporation.” and “Quantifiable Results, in less than 2 years, $500k expense = $2 million in corrected fee income and $15 million in reimbursable expenses. Intangible Results: Reputation as an “honest broker”, Delivered value to business and IT, and Business consensus was documented into policies endorsed by Management.”

Link to External Resource: Data Governance: In Practice and over Time

Source: International Finance Corporation

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

Posted: October 21st, 2009

“Because of poor-quality data, our district faced having $15 million withheld from Survey #2. Although the data was corrected and the funds eventually received, this event served as a wake-up call for the district.”
A brief but impactful look at a large school district’s data quality challenge, approach and results. From the Resource: “In the past, OCPS had no way to tell schools that they had data problems, other than waiting until the survey data was shipped and the error reports were retrieved from DOE. The DOE reports were printed and sent to the schools to make corrections in the amendment system. The data was rarely corrected in the source system.”

Link to External Resource: Creating Data Quality in the K-12 Education Enterprise

Source: Orange County Public Schools

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

Posted: October 20th, 2009

In an extensive survey of 629 CFO-level executives, “Improving data quality/information integrity” retained its position as the most pervasive critical technology concern. Another data quality-related finding: Only about one in five financial executives are “highly satisfied” with their information integrity. See this 36-page report for additional insight.

Link to External Resource: 2008 Technology Issues for Financial Executives

Source: CSC & Committee on Finance and Information Technology

See more Resources like this one in this Data Quality Resource Guide Section: Data Quality – Data and Stats

Posted: October 19th, 2009

“Data is useful – high-quality, well-understood, auditable data is priceless.”

Link to External Resource: The Coming of BI Competency Centers

Source: Ted Friedman, Gartner

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

Posted: October 16th, 2009