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Training Module 2.2 
 
 
 
Open Data & 
Metadata Quality 
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This presentation has been created by PwC 
 
Authors:  
Makx Dekkers, Nikolaos Loutas, Michiel De Keyzer 
and Stijn Goedertier 
 
Presentation 
metadata 
Slide 2 
Open Data Support is funded  by 
the European Commission 
under SMART 2012/0107 ‘Lot 
2: Provision of services for the 
Publication, Access and Reuse of 
Open Public Data across the 
European Union, through 
existing open data 
portals’(Contract No. 30-CE-
0530965/00-17). 
 
© 2014 European Commission 
Disclaimers 
1.
The views expressed in this presentation are purely those of the authors and may not, in any 
circumstances, be interpreted as stating an official position of the European Commission. 
The European Commission does not guarantee the accuracy of the information included in this 
presentation, nor does it accept any responsibility for any use thereof. 
Reference herein to any specific products, specifications, process, or service by trade name, 
trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, 
recommendation, or favouring by the European Commission. 
All care has been taken by the author to ensure that s/he has obtained, where necessary, 
permission to use any parts of manuscripts including illustrations, maps, and graphs, on which 
intellectual property rights already exist from the titular holder(s) of such rights or from her/his 
or their legal representative. 
2.
This presentation has been carefully compiled by PwC, but no representation is made or 
warranty given (either express or implied) as to the completeness or accuracy of the information it 
contains. PwC  is not liable for the information in this presentation or any decision or 
consequence based on the use of it.. PwC will not be liable for any damages arising from the use of 
the information contained in this presentation. The information contained in this presentation is 
of a general nature and is solely for guidance on matters of general interest. This presentation is 
not a substitute for professional advice on any particular matter. No reader should act on the basis 
of any matter contained in this publication without considering appropriate professional advice. 

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Learning objectives 
By the end of this training module you should have an understanding 
of: 
• What (open) data quality means. 
• The open data quality determinants and criteria. 
• Good practices for publishing high-quality (linked) open data. 
 
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Content 
This modules contains... 
• A definition of data quality; 
• An overview of the dimensions of data and metadata quality;  
• A selection of best practices for publishing good quality data and 
metadata. 
 
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What is data (and metadata) quality? 
Data is of high quality "if they are fit for their intended uses in 
operations, decision making and planning." 
 
Or more specifically: 
 
“High quality data are accurate, available, complete, conformant, 
consistent, credible, processable, relevant and timely.” 
 
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Metadata is data about data… 
“Metadata is structured information that describes, explains, locates, 
or otherwise makes it easier to retrieve, use, or manage an 
information resource. Metadata is often called data about data”   
-- National Information Standards Organization 
 
• We observe that metadata is a type of data. 
• The same quality considerations apply to data and metadata alike.  
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The data quality 
dimensions 
 What are the main dimensions to be taken into account for 
delivering good quality (meta)data? 
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Data quality dimensions 
•
Accuracy: is the data correctly representing the real-world entity or event? 
•
Consistency: Is the data not containing contradictions? 
•
Availability: Can the data be accessed now and over time? 
•
Completeness: Does the data include all data items representing the entity 
or event? 
•
Conformance: Is the data following accepted standards? 
•
Credibility: Is the data based on trustworthy sources? 
•
Processability: Is the data machine-readable? 
•
Relevance : Does the data include an appropriate amount of data? 
•
Timeliness: Is the data representing the actual situation and is it published 
soon enough? 
 
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Accuracy 
The accuracy of data is the extent to which it correctly represents the 
characteristics of the real-world object, situation or event. 
For example: 
•
Correct measurement of weather conditions (temperature, precipitation). 
•
Correct indication of re-use conditions of the dataset. 
 
Recommendations: 
•
Balance the accuracy of your data against the cost in the context of the 
application; it needs to be good enough for the intended use. 
•
Make sure that there is organisational commitment and investment in 
procedures and tools to maintain accuracy . 
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Accuracy by example 
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Higher accuracy 
 
Less accuracy 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
OpenStreetMap,  City of Utrecht, The 
Netherlands (2011 vs. 2007) 

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Consistency 
The consistency of data is the extent to which it does not contain 
contradictions that would make its use difficult or impossible. 
For example: 
•
A dataset that combines data from different sources that has been processed 
to detect conflicting statements which have been resolved. 
•
A description of a dataset that does not contain multiple licence statements 
or where the data of last modification is not before the creation date. 
 
Recommendations: 
•
Process all data before publication to detect conflicting statements and 
other errors (in particular if data is aggregated from different sources). 
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Consistency by example 
 
 
 
 
 
High consistency 
 
 
 
 
Less consistency 
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Availability 
The availability of data is the extent to which it can be accessed; this 
also includes the long-term persistence of data. 
For example: 
•
A Dataset that is identified by a http: URI that resolves persistently to the 
right resource (and does not give back 404 Not found). 
•
A description of the dataset that is included in the search engine of a data 
portal. 
 
Recommendations: 
•
Follow best practices for the assignment and maintenance of URIs. 
•
Make sure that responsibility for the maintenance of data is clearly 
assigned in the organisation. 
 
Slide 13 
See also: 
http://www.slideshare.net/OpenDataSupport/de
sign-and-manage-persitent-uris 

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Availability by example 
High availability 
    Less availability 
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Completeness 
The completeness of data is the extent to which it includes the data 
items or data points that are necessary to support the application for 
which it is intended. 
For example: 
•
A Dataset that includes spending data for all ministries enables a complete 
overview of government spending. 
•
A description of data that is generated in real time that includes the date and 
time of last modification. 
Recommendations: 
•
Design the capture and publication process to include the necessary 
data points. 
•
Monitor the update mechanisms on a continuous basis. 
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Completeness by example 
High completeness 
 
 
 
 
 
Less completeness 
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Conformance 
The conformance of data is the extent to which it follows a set of 
explicit rules or standards for capture, publication and description 
For example: 
•
A Dataset that expresses coordinates in WGS84 and statistics in SDMX. 
•
A description of a dataset according to the DCAT Application Profile. 
 
Recommendations: 
•
Apply the most used standards in the domain that is most relevant for 
the data or metadata. 
•
Define local vocabularies if no standard is available, but publish 
your vocabularies according to best practice (e.g. dereferenceable URIs). 
 
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Conformance by example 
High conformance 
 
 
 
 
 
Less conformance 
Slide 18 
See also: 
https://joinup.ec.europa.eu/asset/adms_foss/ne
ws/just-released-admssw-validator-verify-and-
visualise-rdf-software-metadata  

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Credibility 
The credibility of data is the extent to which it is based on trustworthy 
sources or delivered by trusted organisations. 
For example: 
•
A dataset that contains data from processes that can be independently 
verified, e.g. election results or parliamentary proceedings. 
•
A description of a dataset that is published by a government agency. 
 
Recommendations: 
•
Base data on sources that can be trusted or on explicit Service Level 
Agreements where possible and appropriate. 
•
Make appropriate attributions so that re-users can determine whether 
or not they can trust the data. 
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Credibility by example 
High credibility 
Data coming from the Publications 
Office of the EU: 
 
 
Less credibility 
Data coming from Lexvo: 
 
Slide 20 
Lingvoj/Lexvo data may not be of less quality than Publications Office data, but the 
Publications Office is an authoritative source, while Linvoj and Lexvo are initiatives of 
individuals. 

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Processability 
The processability of data is the extent to which it can be understood  
and handled by automated processes. 
For example: 
•
A dataset that contains coded information based on publicly available  
controlled vocabularies and code lists. 
•
A description of a dataset that expresses dates in W3C Date and Time Format 
(e.g. 2013-06-01) rather than as text (e.g. 1 June 2013). 
Recommendations: 
•
Identify the source of terminology and codes used in the data in 
machine-readable manner. 
•
Apply recommendations for syntax of data given in common standards 
and application profiles. 
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Processability by example 
Higher processability 
Less processability 
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Relevance 
The relevance of data is the extent to which it contains the necessary 
information to support the application. 
For example: 
•
A Dataset that contains temperature measurements rounded to degrees 
Celsius for climate calculations; a dataset with precision of a thousandth of a 
degree for chemical reactions. 
•
A description of a dataset that only contains temporal coverage data if 
necessary for its processing . 
Recommendations: 
•
Match coverage  and granularity of data to its intended use within 
constraints of available time and money. 
•
However, also consider potential future usages of the data. 
 
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Relevance by example 
High relevance 
 
 
 
 
Less relevance 
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Timeliness 
The timeliness of data is the extent to which it correctly reflects the 
current state of the entity or event and the extent to which the data (in 
its latest version) is made available without unnecessary delay 
For example: 
•
A dataset that contains real-time traffic data that is refreshed every few 
minutes. 
•
A description of a dataset containing annual crime statistics that is made 
available within days of publication of the dataset. 
Recommendations: 
•
Adapt the update frequency of data to the nature of the data and its 
intended use. 
•
Make sure that processes and tools are in place to support the updating. 
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Timeliness: examples 
High timeliness 
 
 
 
 
Less timeliness 
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Best practices 
Best practices for publishing high-quality data and 
metadata. 
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W3C: Best practices for publishing Linked Open 
Data 
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See also: 
http://www.slideshare.net/OpenDataSupport/th
e-linked-open-government-data-lifecycle 

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Opquast: 72 Open data good practices 
Some examples 
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See also: 
http://checklists.opquast.com/en/opendata 

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What are the common elements in the best 
practices 
•
Provide appropriate descriptions of data (i.e. metadata). 
•
Use standard vocabularies for metadata and data whenever such 
vocabularies exist. 
•
Specify the license under which the data may be re-used. 
•
Adhere to legal requirements concerning protection of personal and 
other sensitive data. 
•
Represent metadata and data according to the Linked Data 
principles using persistent URIs for identifying things. 
•
Provide information about the source of the data. 
 
Maintenance of metadata and data is critical! 
 
 
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See also: 
http://www.slideshare.net/OpenDataSupport
/introduction-to-metadata-management 

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Conclusions 
• The quality of data is determined by its fitness for (re-)use by data 
consumers. 
• Metadata is “data about data”, i.e. metadata is a type of data. 
 The same quality considerations apply to data and metadata alike.  
• Data quality has multiple dimensions and is about more than the 
correctness of data. 
 Accuracy, availability, completeness, conformance, consistency, 
credibility, processability, relevance, timeliness. 
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Group questions 
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In your opinion, which factors contribute the most to metadata 
quality? 
 
Improving quality can require time and resources. To which 
extent would your organisation be willing to invest in 
metadata quality? 
 
Could you provide an example of high quality metadata 
description for a dataset using the DCAT Application Profile? 
 http://www.visualpharm.com 
 http://www.visualpharm.com 
 http://www.visualpharm.com 
Take also the online test here! 

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Thank you! 
...and now YOUR questions? 
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References 
Slide 5:  
• Juran, Joseph M. and A. Blanton Godfrey, Juran's Quality Handbook, Fifth 
Edition, p. 2.2, McGraw-Hill, 1999 
Slide 6:  
• National Information Standards Organization, 
http://www.niso.org/publications/press/UnderstandingMetadata.pdf   
Slide 8-26: 
• Mark David Hansen. Zero Defect Data: Tackling the Corporate Data Quality 
Problem. 1991. http://dspace.mit.edu/handle/1721.1/13812 
• Kevin Roebuck. Data Quality: High-impact Strategies  - What You Need to Know: 
Definitions, Adoptions, Impact, Benefits, Maturity, Vendors. Emereo Pty Limited, 
2011. http://bit.ly/19Qb6Ov 
• Thomas R. Bruce, Diane Hillmann. The Continuum of Metadata Quality: 
Defining, Expressing, Exploiting. ALA Editions, 2004. 
http://www.ecommons.cornell.edu/handle/1813/7895 
• Sharon Dawes. Open data quality: a practical view.  Open Data Roundtable. 
October 2012. http://www.slideshare.net/cityhub/sharon-dawes-ctg 
• Joshua Tauberer. Open Government Data. Section 5.2 Data Quality: Precision, 
Accuracy, and Cost. June 2012.  http://opengovdata.io/2012-02/page/5-2/data-
quality-precision-accuracy-and-cost 
• Stefan Urbanek. Data Quality: What is It? January 2011. 
http://ckan.org/2011/01/20/data-quality-what-is-it/ 
• Amrapali Zaveri, Anisa Rula, Andrea Maurino, Ricardo Pietrobon, Jens Lehmann, 
Sören Auer. Quality Assessment Methodologies for Linked Open Data. Semantic 
Web Journal (unpublished), 2012. http://www.semantic-web-
journal.net/content/quality-assessment-methodologies-linked-open-data 
 
 
 
 
Slide 13: 
• ISA Programme. 10 Rules for Persistent URIs. 
https://joinup.ec.europa.eu/community/semic/document/10-rules-persistent-
uris 
Slide 14: 
• European Commission. Telecommunication services: Access to networks (1 000). 
http://open-data.europa.eu/  
Slide 28: 
• W3C. Best Practices for Publishing Linked Data. W3C Note 06 June 2013. 
https://dvcs.w3.org/hg/gld/raw-file/default/bp/index.html 
Slide 29: 
• OPQUAST. 72 Open data good practices. 
http://checklists.opquast.com/en/opendata 
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Further reading 
 
Joshua Tauberer. Open Government Data. http://opengovdata.io/ 
 
 
 
Juran, Joseph M. and A. Blanton Godfrey, Juran's Quality Handbook 
 
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Related projects and initiatives 
Best Practices for Publishing Linked Data. 
https://dvcs.w3.org/hg/gld/raw-file/default/bp/index.html 
OPQUAST. Open data good practices. 
http://checklists.opquast.com/en/opendata  
Eurostat. European Statistical System 
http://epp.eurostat.ec.europa.eu/portal/page/portal/ess_euros
tat/introduction  
 
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