1. UKSG Conference
April 2013
Phil Nicolson

2. Data Governance
 What is Data Governance
 What is Data Quality
 The challenges
 Data governance programme
 A publisher approach
 The outcome: Book author example
 ICEDIS
 Summary

3. Data governance
“I think that the key issue here, is that the
information is probably incorrect, inaccurate and in a
form that almost certainly shouldn't have been used”
Dr John Thomson cardiologist at Leeds General Infirmary,
Sky News 30/3/2013

4. Data Governance – a definition
 Data governance is defined as the processes, policies,
standards, organisation, and technologies required to manage
and ensure the availability, accessibility, quality, consistency,
auditability, and security of data

5. Data Quality - definitions
 Data are of high quality "if they are fit for their intended uses
in operations, decision making and planning"
 Data are deemed of high quality if they correctly represent
the real-world construct to which they refer

6. Data Quality
 Data quality attributes:
 Accurate
 Reliable
 Complete
 Appropriate
 Timely
 Credible
 Up-to-date

7. The challenge: Data Sources
 Multiple data sources – ‘system’ data silos
 Multiple locations – ‘geographic’ data silos
 Data entered through multiple channels
 Data entered by different people

8. The challenge: Data Sources
Typical publisher systems: Data can be entered by:
 Financial system  Organisation staff
 CRM/Sales database  Authors
 Authentication system  Society members
 Fulfilment
 Agents in the supply chain
 Usage statistics
 3rd party organisations
 Submissions system
 …..
 Author database
 …..

9. The challenge: Institutions
 UCL:
 University College London (UK)
 Université Catholique de Louvain (Belgium)
 Universidad Cristiana Latinoamericana (Ecuador)
 University College Lillebælt (Denmark)
 Centro Universitario Celso Lisboa (Brazil)
 Union County Library (USA)
 NPL:
 National Physical Laboratory (UK)
 National Physical Laboratory (India)
 York Uni.
 University of York (UK)
 York University (Canada)
 Northeastern University:
 Northeastern University (Boston, USA)
 Northeastern University (Shenyang, China)

10. The challenge: Individuals
How can we uniquely identify individuals? Of the 700,000
individuals known to the RSC in 2012 there were:
 Smith:
 ~1,500
 Jones:
 ~1,000
 Li:
 >10,000

11. Consequences of poor data

12. Biggest obstacle(s) to data quality
improvement in your organization?
Lack of accountability and responsibility for data quality 55.4%
Too many information silos 51.8%
Lack of awareness or communication of the magnitude of data quality problems 51.4%
Lack of common understanding of what data quality means 50.2%
Lack of awareness or communication of the opportunities associated with high quality data 45.0%
Lack of senior leadership in tackling data quality issues 44.2%
Lack of data quality policies, plans, and procedures 42.2%
Perception that data quality is an IT issue only rather than an organisation wide issue 41.8%
The State of Information and Data Quality 2012 Industry Survey& Report, (IAIDQ)
Understanding how Organizations Manage the Quality of their Information and Data Assets.
Pierce, Yonke, Malik, Nagaraj

13. Data Governance – why it is vital
“processes, policies, standards… ensure quality and consistency”
 Increase consistency and confidence in our decision making
 Maximise the income generation potential of our data
 Provide excellent customer service
 Designating accountability for information quality
 Minimising or eliminating re-work
 Optimise staff effectiveness
 Decreasing the risk of regulatory fines
 Improving data security
Data is one of the most valuable assets within an organisation

14. Data governance – a new culture

15. Data governance programme

16. Plan & prioritise
 Sponsorship: director level sponsor?
 Program management: business or IT driven?
 Organisational structure: local, national, international?
 Scope: focus on the most important data?
 Ownership: who are the business owners of critical data?
 New system implementation: protect investment

17. Plan & prioritise
 Resources: dedicated staff?
 Funding: which area of the business will fund the program?
 Business drivers: what are the major business drivers?
 Barriers: what are the main barriers (cultural, funding,
resources, priorities etc.) and can they be mitigated

18. Audit & Analyse
 Audit existing data quality
 Review all relevant systems
 How poor is it?
 Incomplete data
 Invalid
 Out of date
 ….

19. Clean existing data
 Prioritise
 Quick wins
 Highlight progress
 What can be automated?
 Introduce unique identifiers

20. Identifiers available
 People  Organisations
 International Standard Name  International Standard Name
Identifier (ISNI) Identifier (ISNI)
 Open Researcher and  Ringgold ID
Contributor ID (ORCID)  DUNS Number (D&B) and
 Scopus Author Identifier other business and finance
 ResearcherID IDs
 MDR PID Numbers and other
marketing IDs
 Library of Congress MARC
Code List for Organizations

21. ISNI
ISNI is designed ISNI Number ISNI Number
to be a “bridge
identifier”
Party ID 1 Party ID 2
Proprietary Proprietary
Information and/or Information and/or
Metadata Metadata

22. Author IDs
 ORCID is designed to persistently identify and disambiguate
scholarly researchers and attach them to research output
 ORCID identifiers utilize a format compliant with the ISNI ISO
standard
 ISNI has reserved a block of identifiers for use by ORCID, so
there will be no overlaps in assignments
 Recorded as http://orcid.org/0000-0001-2345-6789
http://about.orcid.org/
http://www.isni.org/

23. Use cases
 Disambiguation of researchers
and connection to all their
research
 Links to contributors, editors,
compilers and others involved
in the research process
 Embed IDs into research
workflows and the supply
chain
 Integrate systems

24. Institutional IDs
 Ringgold is an ISNI Registration Agency
 Unique institutional ID number maps data across systems
 ISNI numbers should be used across the scholarly supply
chain to:
 Disambiguate institutional records
 Eradicate duplication of data
 Map institutions into their hierarchy
 Link systems using the institutional ID as the lynchpin

25. Minimising the impact of data silos
 Standard identifiers (both individual and institution) can be
used to breakdown silos by enabling better system linking:

26. Improve data capture
 Data quality policy
 Web forms
 Closer collaboration with 3rd parties to encourage use of
industry standard identifiers such as ISNI or ORCID

27. Data capture - data quality policy
 Design to ensure accuracy, quality and consistency
 Individual responsibilities:
 All staff are responsible for the accuracy and consistency of data
 Capture data in such a way that it is uniquely identifiable and easily
shared within the organisation and with 3rd parties
 Records relating to individuals
 Records relating to institutions
 Reporting of inaccuracies to Data Owners
 Data owners responsibilities:
 All source data systems must have a designated Data Owner
 Data owner retains overall responsibility for all records within their
source data system

28. Improve data capture – web forms
 Required fields
 Validation
 Address validation – postcode lookup
 Institution validation – institution lookup
 ‘Internal’ and ‘external’ web form consistency
 Language barriers
 Help and hints
 Free-text fields

29. On-going monitoring
 Dashboards
 Regular audits
 Metrics – Institutional
Linking Rate
 Staff awareness
 Reporting of errors

30. A publisher example
 Develop a Data Governance Programme
 Data ‘champion’
 Engagement – at all levels
 Ownership – at all levels
 Allocate necessary resources
 Guidelines/Policy - Data quality policy
 Processes put in place
 Education - raise awareness
 New staff – training on Data Governance and their wider impact
 Change of culture

31. A publisher example
 Ringgold and DataSalon client
 All institutional records contain Ringgold Identifiers
 System linking via Individual and Institutional identifiers
 Data (both good and bad) visible to all via MasterVision
 Use of data governance dashboards
 Tidying of existing data
 Simple reporting of incorrect data across organisation
 New data captured correctly

32. Author database
1. Create a data governance dashboard to
monitor problem areas:
• Book authors with no related institution
• Unknown book authors
• Author records without an affiliation entry
• Author records with commas in the
affiliation entry
• Book authors without an email address
• Book authors with an invalid email address
2. Correct problem records in existing data
• Dashboard clearly highlighted all records of
concern and these records were corrected

33. Author database
3. Ensure new records are created correctly
• Raise staff understanding of the importance of capturing data correctly and
the impact it has across the organisation as a whole (data silos)
• Training covering data governance
4. Ensure appropriate Ringgold coverage
• Where institutions were discovered in the Author database that didn’t exist
within Identify these were reported to Ringgold. This not only means that
individual authors can be linked to the new institution but that any
individuals in other data sources at the same institution can be linked. This
benefits all users of our data and potentially highlights new sales
opportunities.
5. Monitor data quality on an on-going basis
• Books data governance dashboard update on a weekly basis.

34. Author database – results
100.00% 10% will never link:
• Missing data (old records)
95.00%
• Institution no longer exists
90.00% • Retired author
85.00% • Genuinely no related institution
All data sources
ANKO 80.00%
75.00%
End of process:
70.00%
• 15% increase in authors linked to
institutions - information
valuable in supporting all areas
of the business
• Ready for data migration

35. ICEDIS
 The international standards organization EDItEUR is working to
encourage improvements in the ways that "party" information is
communicated
 Some parts of the supply chain continue to send unstructured name &
address records, making matching, disambiguation and automatic ingest
near impossible
 ICEDIS has collaborated with EDItEUR to develop a highly structured
data model for exchanging names, addresses and standard identifiers.
 The group has recently been validating the model by means of a "paper
pilot", using a small library of about 100 name & address types
 An XML schema and HTML documentation are freely available
www.editeur.org
www.editeur.org/138/Structured-Name-and-Address-Model
info@editeur.org

36. Summary
 Your data is a very valuable asset when managed correctly
 Establishing a data governance programme will enable you to
gain maximum benefit from that data
 Data governance is as much about changing the culture of an
organisation as it is about processes and procedures
 It will take time but the benefits can be enormous

37. Phil Nicolson
Data Manager
Ringgold Inc.
phil.nicolson@ringgold.com