1.
Paraskevi Zerva
Cognition & Knowledge Representation Lead
p.zerva@elsevier.com
Supporting GDPR Compliance through effectively governing
Data Lineage & Data Provenance

2.
Context
❖ Introductions
❖ Definitions
❖ EDG Metadata Governance Platform & Use Cases
❖ EDG Showcase Data Lineage
❖ GDPR in a Nutshell
❖ How governing effectively Data Lineage supports GDPR Compliance
❖ GDPR Use Case for Time Limits of Personal Data Erasure – Data Retention
❖ GDPR Policies and Compliance
❖ GDPR Compliance Use Case
2

3.
Introductions
❖ WhoAmI?
▪ Paraskevi Zerva
▪ Cognition & Knowledge Representation Lead (Entellect, Elsevier)
▪ Previously worked as an Information Architect for the Enterprise Data Governance at
JP Morgan & Chase.
▪ PhD in ``Provenance of Data for Compositions of Services’’.
❖ What’s my focus?
▪ Work on the the data governance strategy for Elsevier Entellect to support effective data
governance across Entellect’s software development life-cycle.
▪ Build a common representation for analysis & validation of Elsevier Entellect’s data.
▪ Consolidate data lineage & provenance information with other data assets to provide
a unified data governance ecosystem.
❖ What I am going to talk about ?
▪ How governing effectively data lineage/provenance supports compliance for GDPR within
the Enterprise Data Governance Platform.
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4.
Definitions
4
❖ Data governance:
▪ is a set of processes that ensures that data assets are efficiently managed and
enables gaining control and have a better understanding of your data,
▪ ensures that data can be trusted and organizations can show accountability about
their data assets with regards to data quality, retention, data lineage etc.,
▪ describes an evolutionary process for a company setting up the processes to handle
information so that it may be utilized by the entire organization,
▪ encompasses data/metadata collection, analysis and validation of rules involving
data (e.g., business (domain) rules, standards, data quality, entitlements, SOR, etc.)
❖ Data lineage refers to capturing the sequence of data flows involving a data element - it
can be represented visually to discover the movement of data artefact from its source to
its destination to understand where this originates from.
❖ Data provenance refers to the recording activity for the processing activities data (e.g.,
through provenance loggers).
❖ GDPR is the General Data Protection Regulation.

5.
Enterprise Data Governance
❖ Unified platform for Corporate Technology to support the efficient data governance and
metadata management.
❖ Team’s mission:
✓ Integrate CT metadata from various sources in one place in a common way (RDF), regardless
of the input format
✓ Consolidates lineage/provenance information together with other metadata.
❖ EDG ingests different formats like XML, JSON, CSV (Collect)
❖ EDG translates the data/metadata into a common language format (RDF) (Standardize)
✓ Schemas are expressed as OWL ontologies.
✓ SHACL (shapes constraint language) is used for interface building.
and different user’s representation with the same underlying core schema.
✓ SPIN is used for transformation.
❖ We form a connected graph data structure queryable
across all internal and external reference datasets (Connect)
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Collect
Standardize
Connect
Refine

6.
Enterprise Data Governance Ecosystem
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✓ Enterprise
Metadata
✓ LDMs/PDMs
✓ Req Reports
✓ Glossaries
✓ Taxonomies
✓ Codelists
✓ External
Standards
Ingestion
✓ Data Models
✓ Provenance
Logging
✓ Movement
✓ Feedback
Unified
Data/Metad
ata Hub
(EDG)
Sources
People, Processes, Tools, Services,
Conformed Data
✓ RACI
(roles)
✓ APIs
✓ Discovery
✓ Reporting
Uses

7.
Data Governance Business Cases
❖ Capture/manage governance requirements for the complete portfolio of CT applications.
❖ Support the software development lifecycle, compliance/regulatory requirements (GDPR).
❖ Demonstrate Data Lineage* where the different data sources originated from, to showcase
accountability on control & understanding of the data for regulatory purposes.
❖ Exhibit Data provenance** of how the data is processed/transforms across the platform.
❖ Track data movement/data transfers between applications (Traceability***).
❖ Provide contextual alignment with firm-wide standards, taxonomies and glossaries.
❖ Provide validation capabilities for data quality and data accuracy.
❖ Exhibit accountability with regards to entitlements (by effectively governing data provenance).
❖ Ontologies are extended to provide crosswalks between models and ecosystems so we can
answer questions such as :
✓ Which applications contain (S)PI data affected by GDPR? (Regulatory Reporting)
✓ S. Arabia has changed its retention policy – what applications are impacted? (Reporting)
✓ What are the owners of particular data requirements documentation? (RACI)
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* Data lineage refers to capturing the sequence of data flows involving a data element - it can be represented visually to
discover the data flow/movement from its source to its destination.
** Traceability indicates the ability to track a data construct back to the construct it was derived from e.g., the original
system where this was created
*** Data provenance refers to the recording activity (through provenance loggers)

8.
EDG – Metadata Governance Model
❖ The diagram depicts how conceptually metadata from various sources is connected in EDG.
❖ Data Lineage flow connects the following artefacts:
➢ Business Terms (Data Dictionary Metadata)
➢ Data Requirements
➢ Logical Data Model Artefacts
➢ Physical Data Model Artefacts
➢ Application/Deployment (Technical) Metadata
❖ Data Traceability indicates the ability to
be able to track the links to another artefact.
❖ Data Provenance allows to track:
➢ Ownership/Entitlements/Access Control Metadata
➢ Business Capability/Process Metadata
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Data Lineage
Data Traceability
Data Provenance

9.
EDG – Showcase Data Lineage
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10.
Data Assets
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11.
Logical Data Model
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12.
Physical Data Model
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13.
Physical Database Realization
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14.
Link to Technical Metadata
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15.
Logical Data Model
15

16.
Logical Entity
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17.
Logical Attribute
17

18.
Link to Data Requirement
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19.
Link to Standard Glossary
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20.
Physical Data Model
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21.
Physical Table
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22.
Physical Column
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23.
Data Lineage Diagram Logical/Physical
Data Elements
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Mapping to
Technical Asset
LDM Artefacts
PDM Artefacts
Mapping to
Data Requirement
Mapping to
Standard Glossary
PDM to LDM
mapping

24.
Data Lineage Logical Data Elements
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LDM Artefacts
Mapping to
Data Requirement
Mapping to
Standard Glossary
PDM to LDM
mapping

25.
Data Lineage Physical Data Elements
25
Mapping to
Technical Asset
PDM Artefacts

26.
GDPR in a Nutshell
❖ GDPR is a new set of EU guidelines governing how organizations handle personal data
replacing the current Data Protection Act (DPA) and was enforced from 25 May 2018.
❖ According to GDPR personal data should be processed:
➢ Fairly/lawfully
➢ Must retain accurate/kept up to date
➢ Kept no longer than is necessary (retention period)
➢ Processed in a secure way
❖ Controller and processor terms are used in GDPR to describe the parties involved in
processing personal data (PI).
❖ Controller: the party that decides what data is extracted, the purpose used, who is
involved in the processing.
✓ should be able to demonstrate compliance (accountability metrics).
✓ should be able to report on the purposes of processing/the categories PI it controls.
❖ Processor: the party responsible for processing the data on behalf of the controller.
✓ should maintain records of the categories of processing activities of PI & the means
in which it’s processed.
✓ should be able to report on the data transfers of personal data to a third country or an
international organization and can be held responsible for a data breach (requirement
for breach notification).
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27.
How governing Data Lineage Supports GDPR
Compliance
❖ GDPR Challenge:
➢ Record of personal data processing are required for evidencing/demonstrating compliance.
➢ Organizations are required to record every point where processing activities of personal data
takes place and showcase accountability.
❖ Solution:
➢ GDPR makes data governance even more critical on the lineage aspect.
➢ Governance of data lineage enables the understanding of your data-flow activities & to
identify and document legal justification for each type of activity.
➢ When data lineage is represented visually it allows discovery of the data flow/movement
from its source to destination via various changes and how the data is transformed.
➢ On top of that the GDPR requires to evidence records of personal data processing that
implies the need for Data Provenance.
➢ Data Provenance refers to the recording activity of how the data were derived/generated
and processed. It allows to verify that the process and steps used to obtain a result complies
with a set of given requirements.
➢ In our business case the given requirements are GDPR regulatory requirements therefore
data lineage and provenance become the tools to showcase accountability with regards to
GDPR compliance.
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28.
GDPR Governance Use Case
❖ GDPR Article 30 Data Requirement
➢ Provide time limits for erasure of the different categories of data required per record
retention policy.
❖ Regulatory requirement translated to the creation of a report and accountability
metrics that:
➢ Returns applications in scope of GDPR for Corporate Technology.
➢ Returns the record class code in scope applications based on the record retention
policies per country.
➢ Notifies application owners in case there are changes on record retention updates and
verifies compliance of new changes with regards to GDPR regulatory requirements.
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* Record class codes are used to determine how long to keep each record for each jurisdiction.
**A record class code (RCC) is a category used to group similar types of records in JPMC’s master record retention schedule.
*** Record retention requirements are categorized by record class code by county and in some case by the business function
of the record.

29.
Retention Conceptual Model
29
ia.jpmc: SEAL_103249 a
edg:BusinessApplication
ia.jpmc.gov:GRM_FUN1030 a
ia.jpmc:RecordRetentionClass
ia.jpmc:application
RecordRetentionClass
ia.jpmc.gov:GRM_FUN10300-AE
a ia.jpmc:dataRetentionPolicy
ia.jpmc:dataRetentionPolicy
SOR: SEAL
SOR: Retention Manager Record Retention Policy Ontology
Technical Standard Ontology

30.
GDPR In Scope Business Application
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GDPR compliance information
Provenance information
GDPR in Scope – contains pi
OBK1060 | Payroll Services (GRM) PAY100 | Employee Compensation Contribution (GRM)record retention class
Record Retention Class

31.
Record Retention Code
31
Data Retention Record Class
data retention
policy

32.
Record Retention Policy
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country:
retention
period:
disposition:
retention
event:
retention
period unit:

33.
EDG – RCC Code Diagram
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data
retention
policy

34.
EDG – RCC Policy Diagram
34
country:
disposition:
retention
period unit:
retention
period:
retention
event:

35.
Query RCC Data for GDPR in scope Apps
prefix rdfs: <http://www.w3.org/20000/01/rdf-schema#>
prefix edg: <http://www.edg.topbraid.solutions/model/>
prefix ia.jpmc: <http://ia.jpmc.com/dg/>
SELECT DISTINCT ?appId ?appName ?gdprScopr ?lob ?rccClassCode ?rccLabel ?rccPolicy
FROM <urn:x-evn-master:seal>
FROM <urn:x-evn-master:grm>
WHERE
{
{
?app a edg:BusinessApplication .
?app edg:name ?appName .
?app edg:identifier ?appId .
?app ia.jpmc:inScopeForGDPR ?gdprScope .
?app ia.jpmc:lineOfBusiness ?lob .
FILTER regex(?gdprScope, “YES”)
FILTER regex(?lineOfBusiness, “CT”)
}
}
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appId appName gdprScope lob rccClassCode rccPolicy
35632 Payroll Application YES CT GRM_AUD_PAY_1060 Payroll Services
38537 KPMG Link – Global
Business Travel
YES CT GRM_AUD_PAY_1080 Payroll Accounting

36.
SPIN Rules (1) - Inferencing
#STEP 401: Create Record Classes
CONSTRUCT
{
?recordClassCodeU a ia.jpmc:DataRetentionRecordClass .
?recordClassCodeU rdfs:label ?rccLabel.
?recordClassCodeU edg:identifier ?rccClassCode.
?recordClassCodeU edg:name ?rccName.
?recordClassCodeU edg:description ?rccDescription.
?recordClassCodeU ia.jpmc.go.dataRetentionPolicy ?rccPolicyU.
}
WHERE
{
?this a RetentionExport:RetentionExport.
BIND (spl:object (?this, RetentionExport:recordClassCode) AS ?rccClassCode .
BIND (spl:object (?this, RetentionExport:country) AS ?country .
BIND (spl:object (?this, RetentionExport:countryCode) AS ?countryCode .
BIND (spl:object (?this, RetentionExport:recordClassName) AS ?rccName .
BIND (spl:object (?this, RetentionExport:recordClassDescription) AS ?rccDescription.
BIND (ia.jpmc:BuildDataRetentionPolicyClassURI (?recordClassCode) AS ?recordClassCodeU.
?countryU country:countryId ?RDICountryCode .
BIND (str(?RDICountryCode) AS cntryCodeLabel).
FILTER (?countryCode = ?cntryCodeLabel) .
BIND (fn:concat(?recordClassCode, "|", ?recordClassName, "(GRM)") AS ?rccLabel).
BIND (ia.jpmc:BuildDataRetentionPolicyRecordURI(?recordClassCode, ?RDICountryCode) AS ?rccPolicyU).
}
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37.
SPIN Rules (2) - Inferencing
#STEP 402: Create Record Retention Policy
CONSTRUCT
{
?rccPolicyU a edg:DataRetentionPolicy .
?rccPolicyU rdfs:label ?policyLabel.
?rccPolicyU edg:identifier ?policyIdentifier.
?rccPolicyU edg:name ?policyName.
?rccPolicyU ia.jpmc.go:retentionDisposition ?disposition.
?rccPolicyU ia.jpmc.go:retentionEvent ?retentionEvent.
?rccPolicyU ia.jpmc.go:retentionPeriod ?retentionPeriod.
?rccPolicyU ia.jpmc.go:retentionPeriodUnit ?retentionPeriodUnit.
?rccPolicyU edg:country ?country.
}
WHERE
{
?this a RetentionExport:RetentionExport.
BIND (spl:object (?this, RetentionExport:policyIdentifier) AS ?policyIdentifier.
BIND (spl:object (?this, RetentionExport:retentionDisposition) AS ?disposition .
BIND (spl:object (?this, RetentionExport:retentionEvent) AS ?retentionEvent .
BIND (spl:object (?this, RetentionExport:retentionPeriod) AS ?retentionPeriod .
BIND (spl:object (?this, RetentionExport:retentionPeriodUnit) AS ?retentionPeriodUnit.
BIND (spl:object (?this, RetentionExport:country) AS ?country .
BIND (ia.jpmc:BuildDataRetentionPolicyClassURI (?recordClassCode) AS
BIND (fn:concat(?policyIdentifier, "|", ?policyName, "(GRM)") AS ?policyLabel).
BIND (ia.jpmc:BuildDataRetentionPolicyURI(?policyIdentifier, ?country, ?policyName) AS ?rccPolicyU).
}
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38.
SHACL Property Constraint
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39.
GDPR POLICY & COMPLIANCE
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40.
GDPR POLICIES & COMPLIANCE
❖ Policies define guidelines for handling and implementing specific security or
regulatory issues.
❖ With focus on the policy requirements for data protection we have built a
policy/compliance model:
➢ Aiming on validating GDPR compliance for the compliance objects under policy target.
➢ Showcasing accountability with regards to GDPR policy requirements.
Objectives
❖ Merge the gap between GDPR legislation obligations and operational level
technology controls using semantic modelling to model the critical policy and
compliance aspects.
❖ Use inferencing to preserve accountability of processing activities that
handle PI data subject to regulatory compliance.
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41.
GDPR RETENTION COMPLIANCE
41
edg:Policy
edg:DataPolicy
rdfs:subclass
rdfs:subclass
edg:ComplianceAspect
edg:Policy
Requirement
ia.jpmc:DataRetention
RecordClass
ia.jpmc:categorized
ByCountry
ia.jpmc:categorized
ByBusinessFunction
ia.jpmc:Country ia.jpmc:Business
Function
edg:compliesWith
edg:DataRetention
Policy
rdf:type
ia.jpmc:dataRetentionPolicy
edg:RequirementAsset
rdf:typeedg:hasRequirement
edg:GDPRRegulatory
Requirement
rdfs:subclass

42.
GDPR REGULATORY COMPLIANCE EXAMPLE
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43.
Thanks for attending ☺
Q & A
Name: Paraskevi Zerva
Email: p.zerva@elsevier.com
Linkedin: linkedin.com/in/paraskevizerva_Profile_URL
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44.
BACKUP SLIDES
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45.
Extending Data Lineage to Data Provenance
45
prov:Activity
Non-Linear Activity Linear Activity
Workflow
Pipeline
rdfs:subclass rdfs:subclass
rdfs:subclass
composedOf/contains
Software Program
Software Program
Execution
computationOf
executionOf
Software Program
Computation
runsOn
prov:Agent
prov:Server
rdfs:subclass
prov:Entity
rdfs:subclass
Processable
Processable
input
subproperty of
prov:wasUsed
output
subproperty of
prov:wasGeneratedBy
prov:wasDerivedFrom