Geneva, 27-28 June 2013
Data quality at UNODC
Enrico Bisogno
Statistics and Surveys Section

Two frequent statements:
•´There is no data´ or ´Data are very scarce´
•´There is a lot of data out there´
A possible synthesis:
•There is lack of good quality data
A preamble

1. Relevance
2. Accuracy
3. Timeliness and punctuality
4. Coherence and comparability
5. Accessibility and clarity
Dimensions of data quality

1. Decision to collect and produce data
2. Data collection
3. Data processing
4. Data dissemination and analysis
Dimensions and their application

Existence of a mandate (who does what)
Consultation with interested parties (data producers and data users)
Resources: financial, skills, infrastructure
1. Decision to collect and produce statistical data

Do the homework:
- define the object of data collection,
- concepts and definitions
- look around: international and national standards
Administrative data: understand regulatory and operational context
Survey data: develop methodology in line with good and previous practices
2. Data collection

Some of the international standards
•International classification of crime for statistical purposes (ICCS - under
development, by 2015)
•UNODC-UNECE Manual on victimisation surveys
2. Data collection (cont.)

An example: intentional homicide, as the ´unlawful death purposefully inflicted on a
person by another person´
2. Data collection (cont.)

Develop and implement all possible consistency checks: errors have to
disappear; also, data processing should not introduce errors (IT compliant)
Decisions on collected data may be needed:
•Suspect figures
•Inconsistent data from various sources
•Inconsistencies across time and space
Not a recipe, but a toolbox to take decisions:
•Analyse metadata (consistency with concepts defined previously)
•Disaggregate data, put in context
•Consultation, internal and external
•Tend to be ´conservative´ (bad data do not die)
3. Data processing

Specific concern in international agencies: official vs. non-official sources
•A long-term process to move from ´data officiality´ to ´data quality´
•However, trade-off between data officiality and data ownership
•Need to increase awareness about importance of data quality to keep
countries favourably engaged
3. Data processing (cont.)

•Data publicly disseminated as they are available
•All data users are treated in the same way
•Data release calendar
•Transparency on methods and sources
•Define internal data publication policy
•Use of ´intelligible´ statistical methods
•Data analysis vs. policy analyses/recommendations
4. Data dissemination and analysis

Data that I can defend
•mandate to produce them
•broad consultation, good allies
•data thoroughly processed and checked
•data are publicly available and well communicated
•transparency on methodology, sources (incl. weaknesses)
To conclude