Measuring and Communicating Data Quality · Measuring and Communicating Data Quality . Steven Vale...
Transcript of Measuring and Communicating Data Quality · Measuring and Communicating Data Quality . Steven Vale...
United Nations Economic Commission for Europe Statistical Division United Nations Economic Commission for Europe Statistical Division
UNECE Training Workshop on Dissemination of MDG Indicators and Statistical Information
Astana, Kazakhstan 23 – 25 November 2009 Steven Vale, UNECE
Measuring and Communicating Data Quality
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What is quality?
How can we measure quality?
How should we report and communicate quality?
Contents
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Which is the Best Quality?
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Definition of Quality
International Standard ISO 9000/2005 defines quality as;
'The degree to which a set of inherent characteristics fulfils requirements.’
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What Does This Mean? Whose requirements?
• The user of the goods or services A set of inherent characteristics?
• Users judge quality against a set of criteria reflecting the different characteristics of the goods or services
So quality is all about providing goods and services that meet the needs of users (customers)
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Quality Criteria
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Quality Criteria for Statistics Different statistical organisations use
different criteria - but lists of criteria are quite similar
UNECE list: Relevance Comparability
Accuracy Clarity Timeliness Accessibility Punctuality
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Relevance Are the statistics that are produced
needed? Are the statistics that are needed
produced? Do the concepts, definitions and
classifications meet user needs?
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Accuracy
The closeness of statistical estimates to true values
In the past: Quality = Accuracy Now accuracy is just one part of
quality
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Timeliness The length of time between data
being made available and the event or phenomenon they describe
Punctuality The time lag between the actual
delivery date and the promised delivery date
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Comparability The extent to which differences are
real, or due to methodological or measurement differences • Comparability over time • Comparability through space (e.g.
between countries / regions) • Comparability between statistical domains
(sometimes referred to as coherence)
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Accessibility The ways in which users can obtain or
benefit from statistical services (pricing, format, location, language etc.)
Clarity The availability of additional material
(e.g. metadata, charts etc.) to allow users to understand outputs better
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Importance of Accessibility
Not just about making data available on the Internet or in a book • Passive accessibility
Accessibility is about bringing data to users in an understandable way, opening a dialogue with those users, and ensuring that their information needs are met • Active accessibility
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Accessibility Should Include:
Communicating Marketing Interpreting “Story-telling” Informing Educating
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Accessibility and Visualization
Good visualizations make data accessible to many more users
Bad visualizations are unhelpful / misleading “Self-service” visualization needs to be
simple, with guidance to help users get meaningful results
“Ready-made” visualizations can be more complex, tailored to specific data sets
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Is it more cost-effective to: • develop “ready-made” graphics, or • offer users more “self-service” functionality?
Many users don’t have the time or knowledge to produce good visualizations
Advanced users have access to their own visualization and analysis tools
Accessibility and Visualization
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Importance of Clarity
Clarity is all about explaining data Do current explanatory notes help?
• Often written by specialists for specialists • Full of jargon • Too long • Too boring!
Simplified, plain-text versions needed
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Other Considerations Cost / efficiency Integrity / trust Reputation of the organization Professionalism
• Adherence to international standards (e.g. UN Fundamental Principles of Official Statistics)
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Quality is not just about outputs
To have good outputs we need to have good inputs and processes, so we need to think about the quality of these as well
Input Process Output
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Quality of Inputs
Timeliness Completeness – are there any
missing units or variables? Comparability with other sources Quality check survey? Knowledge of the source is vital!
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Quality of Processing
Quality of matching / linking Outlier detection and treatment Quality of data editing Quality of imputation Keep raw data / metadata to refer
back to if necessary
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Quality of Outputs
Are the users satisfied? Are the outputs comparable with
data from other sources? What is the impact on time series? Are the outputs cost-effective? Quality reports to measure and
communicate differences?
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Measuring Quality
Quantitative methods • E.g. confidence intervals
User surveys Self evaluation Benchmarking
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Quantitative Measures
The tops of the bars indicate estimated values and the red lines represent the confidence intervals surrounding them.
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UNECE Database User Survey
Launched each autumn on database web site
10 questions 150 responses
(target 100)
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Design a user survey with up to 10 questions for users of your web site
20 minutes
Exercise
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1. Type of user
2. Frequency of use
3. Location (country)
4. Type of data
5. Database relevance
6. Timeliness
UNECE User Survey Questions
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Continued...
7. Clarity (metadata)
8. Overall data quality
9. User interface
10. Other comments and questions
Results: Type of user
International organization
/ NGO
Student
Academic / research
National government
National Statistical
OfficeOtherIndividual
Private businessMedia
Results: Frequency of use
Results: Location
Results: Data quality
Poor1%
Very poor1%
Average17%
Excellent18%
Good63%
Results: User interface
Poor1%
Very poor1%
Average23%
Excellent15%
Good60%
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Improving Our Services
Better timeliness of data New “Country Overview” data cube to give
quick access to key indicators More content in Russian Improved user interface More and better metadata Statistical literacy
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Relatively quick and cheap Is it sufficiently objective? Needs a standard framework to ensure
comparability of quality assessments • Eurostat DESAP check list:
http://epp.eurostat.ec.europa.eu/portal/page/portal/quality/documents/desap%20G0-LEG-20031010-EN.pdf
Self-evaluation
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Comparing data values or data production processes between two sources
Differences can be studied to try to find ways to improve quality
Benchmarking
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Benchmarking Between Countries Fairly cheap and easy way to get ideas
on how to improve statistical processes Mutual benefit - “win - win” Helps to improve international
cooperation May lead to joint development projects
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Quality Reports • Summary – “traffic light” indicator
Red – Serious quality issues, read the quality report before using
Orange – Caution, do not use for important decisions without reading the quality report
Green – Good quality • Intermediate – short quality report
(1000 words maximum) • Detailed – full quality report
Communicating Quality
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Should cover all components of quality
Should be written for the user
Should be easily accessible
Should follow a standard template
Detailed Quality Reports
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Exercise What should be covered in a
detailed quality report? • List the topics that should be included
10 minutes
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Introduction to the statistical process and its outputs
Relevance Accuracy Timeliness Punctuality Accessibility Clarity
ESQR Contents (1)
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Comparability Trade-offs between quality components Assessment of User Needs and
Perceptions Performance, Cost and Respondent Burden Confidentiality, Transparency and Security Conclusion
ESQR Contents (2)
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Quality is all about meeting user needs There are many different aspects to
quality, some of which may be in conflict • E.g. Timeliness versus Accuracy
There are various ways of measuring quality; user views are important
Quality should be communicated to users in a way they can understand
Summary
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Which is the Best Quality?
It depends what the user needs!