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

Steven Vale - UNECE Statistical Division Slide 2

What is quality?

How can we measure quality?

How should we report and communicate quality?

Contents


Which is the Best Quality?


Definition of Quality

International Standard ISO 9000/2005 defines quality as;

'The degree to which a set of inherent characteristics fulfils requirements.’


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)


Quality Criteria


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


Relevance Are the statistics that are produced

needed? Are the statistics that are needed

produced? Do the concepts, definitions and

classifications meet user needs?


Accuracy

The closeness of statistical estimates to true values

In the past: Quality = Accuracy Now accuracy is just one part of

quality


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


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)


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


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


Accessibility Should Include:

Communicating Marketing Interpreting “Story-telling” Informing Educating


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


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


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


Other Considerations Cost / efficiency Integrity / trust Reputation of the organization Professionalism

• Adherence to international standards (e.g. UN Fundamental Principles of Official Statistics)


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


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!


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


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?


Measuring Quality

Quantitative methods • E.g. confidence intervals

User surveys Self evaluation Benchmarking


Quantitative Measures

The tops of the bars indicate estimated values and the red lines represent the confidence intervals surrounding them.


UNECE Database User Survey

Launched each autumn on database web site

10 questions 150 responses

(target 100)


Design a user survey with up to 10 questions for users of your web site

20 minutes

Exercise


1. Type of user

2. Frequency of use

3. Location (country)

4. Type of data

5. Database relevance

6. Timeliness

UNECE User Survey Questions


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%


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


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


Comparing data values or data production processes between two sources

Differences can be studied to try to find ways to improve quality

Benchmarking


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


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


Should cover all components of quality

Should be written for the user

Should be easily accessible

Should follow a standard template

Detailed Quality Reports


Exercise What should be covered in a

detailed quality report? • List the topics that should be included

10 minutes


Introduction to the statistical process and its outputs

Relevance Accuracy Timeliness Punctuality Accessibility Clarity

ESQR Contents (1)


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)


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


Which is the Best Quality?

It depends what the user needs!

measuring and communicating data quality · measuring and communicating data quality . steven vale...

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