Data Quality Technical Best Practices
Gain practical and immediate guidance from leading users, analysts and experts on how to successfully scope, approach and implement Data Quality projects and initiatives. Includes Data Quality implementation examples, guidance, checklists, mistakes and tips.
| Data Quality? Don't Waste your Time |
| A phenomenal look at British Telecom's company-wide data quality initiative which claims rdaddphp.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." |
| Nigel Turner & Dave Evans, British Telecom |
| Tom's Ten Data Tips |
| 10 Data Quality Assessment Tips: "1) Data are outdated as soon as they enter the database 2) Data conversion is a major source of DQ problems 3) Convincing the CEO a DQ problem exists is a challenge (and a must) 4) DQ assessment needs to be grounded in (financial) numbers 5) Establish DQ benchmarks across the company 6) Consider doing both objective and subjective DQ assessment 7) Manual reconfirmation is the royal road (to DQ) 8) Selecting validation samples is tricky business 9) Data redundancy drives quality 10) Ad hoc databases are priceless." |
| Tom Breur, XLNT Consulting |
| Data Quality Strategy |
| 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." |
| West Berkshire Council |
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