How to use the EU e-Competence Framework together with the Body of Knowledge Edison when implementing a new workforce model. In this use case Workforce Transformation, Curricula, Career paths and assessments. Human Resource Development tooling.

1. Case Study UWV
From Data Services to Data Science
using e-CF, Professional Profiles and Edison
Co Siebes, Workforce Transformation Consultant
April 2020
Working
together

2. About UWV:
UWV (Employee Insurance Agency) is an autonomous administrative authority (ZBO) and is
commissioned by the Ministry of Social Affairs and Employment (SZW) to implement employee
insurances and provide labour market and data services.
UWV has core tasks in four areas:
Employment – helping the client remain employed or find employment, in close cooperation with
the municipalities;
Social Medical Affairs – evaluating illness and labour incapacity according to clear criteria;
Benefits – ensuring that benefits are provided quickly and correctly if work is not possible, or not
immediately possible;
Data Management – ensuring that the client needs to provide the government with data on
employment and benefits only once.
UWV

3. UWV Organisation in 5 operational divisions
1. Client and Service (klant & Service) is responsible for all communication with our clients. K&S
makes it possible for all clients to easily find their way within UWV.
2. Public Employment service (Werkbedrijf) is engaged in job placement and re-integration. Our
aim is to help as many people as possible find work by bringing together supply and demand.
3. Social Medical Affairs (Sociaal Medische Zaken) is the expertise centre and service provider for
socio medical and work-related assessments and recommendations in the Netherlands. We use
our expertise to assess our clients' labour capacity and ability to take on workload and give
recommendations to promote recovery and reintegration.
4. Benefits (Uitkeren) this division is responsible for the prompt and correct handling of benefit
applications and the payment of benefits.
5. Data Services (Gegevensdiensten) compiles and manages data on wages, benefits and labour
relations of all insured persons in the Netherlands. UWV needs these data in order to
determine the height of benefits. But we also make these data available to third parties.
UWV Organisation

5. Workforce Transformation for UWV Data Services
The introduction of new technologies, new business management concepts, a different view
of management and responsibilities.
A new workforce model of Data Services in which professional development is used to
enable employees to prepare them selves for this Data Science transformation.
1

6. Workforce Transformation Model
TO BE
SPRINTS
AS IS
SURVEY
Assessment
Traninig
COMMUNICATION, COORDINATION, IMPLEMENTATION
WORKFORCE PLAN
1
3
4
5
6
Measurement of Performance
Unlock Knowledge
•COMPETENCES
•JOB PROFILES
•CURRICULA
•CAREER PATHS
2

7. Workshops to define job profiles and competences
Competence Profiles: first run > 6 new job profiles

8. 8
Plan A.6. Application Design Working accurately
Build B.1. Application Development Creativity
Build B.2. Component Integration Flexibility
Build B.3. Testing Result orientation
Build B.4. Solution Deployment Stress resilience
Build B.5. Documentation Production
Soft SkillsCompetences (e-CF)
1 4
DATA ENGINEER
2
0
1
3
3
2
Starter level
1 2
1 3
Expert level
Example DATA BUSINESS ANALYST and DATA ENGINEER
Plan A.1. IS/Business Strategy Alignment Analysing
Plan A.3. Business Plan Development Creativity
Build B.6. Systems Engineering Active Listening
Enable D.10. Info/Knowledge Management Judgement
Enable D.11. Needs Identification Persuasiveness
Manage E.5. Process Improvement
DATA BUSINESS ANALYST
4
3
3
0
4
4
Soft Skills
Starter level Expert level
0 4
0 4
3 3
Competences (e-CF)
2

9. 9
EDISON Data Science Framework
EDISON Body of Knowledge
5 Knowledge area groups
e.g. Data Management
23 Knowledge areas
e.g. Data Governance
171 Knowledge Units
e.g. Data Curation
All Knowledge Units mapped to the
standard of Computer Classification
System (CCS2012) AND existing
BOKs (DMBOK, BABOK, PMI-BOK,
SWEBOK and ACM-BOK)

10. 10

11. 11
Workshops to define new profiles and data knowledge: second run
Profile title
Summary statement
Mission
Core activities
Starter Expert
A.6. Application Design 1 3
B.1. Application Development 2 3
B.2. Component Integration 0 3
B.3. Testing 2 2
B.4. Solution Deployment 1 2
B.5. Documentation Production 1 3
4. Workng accurately* ✓ ✓
10. Creativity ✓ ✓
15. Flexibility ✓ ✓
32. Result orientation ✓ ✓
35. Stress resilience* ✓ ✓
Personal competences
(Government Competence
Guide)
* = core competences
DATA ENGINEER
Provision of data
Finds, manages and merges multiple data sources and ensures consistency of datasets.
Ensures asset protection through the provision of clean, consistent, quality assured data.
Maintains the integrity of data, stores and searches data and supports presentation of data
analysis.
Client/customer:
• Matching customer wishes
Production
• Design and develop
• Measure data quality
• Roll-out / transfer data solutiona
• Coordinating solutions / delivering functional requirements
• Monitoring and advising on market developments
• Loading data for developers
• Problem management
• Determining and accessing data sources
• Performing data analysis
• Conduct impact analysis
• Expert: Coaching BI and Data Engineers
Quality:
• Apply standards
• Review
• Propose process improvements
• Quality measurement of own work.
Seniority level
e-competences
(e-CF )
Data knowledge Data Management General Principles Passive Active
Data type registries, (PID) Persistent Identifier,
Metadata
✓
Data lifecycle management ✓
Data infrastructure and Data factories ✓
Ethical principle and Data privacy ✓
FAIR (Findable, Accessible, Interoperable) principles in
Data management
✓
Data Management Systems Passive Active
Data architectures; (OLAP) Online Analytical Processing,
(OLTP) Online Transaction Processing, Extraction
Transformation and Load (ETL)
✓
Data modelling, Databases and Database management
systems
✓
Data structures ✓
Data models and Query languages ✓
Database design and Models ✓
Data warehouses ✓
Data Management Architecture Passive Active
Data management, including Reference and Master data ✓
Data warehousing and Business intelligence ✓
Metadata, Linked data, Data provenance ✓
Data infrastructure, Data registries and Data factories ✓
Data backup ✓
Data anonymisation ✓
Data privacy ✓
Data Governance Passive Active
Data governance, Data quality, Data integration and
Interoperability
✓
Data management planning ✓
Data management policy ✓
Data interoperability ✓
Data curation ✓
Data provenance ✓
Business Analytics Passive Active
Business analytics and Business intelligence: Data,
Models (statistical) and Decisions
✓
Data driven Customer Relations Management (CRM),
User Experience (UX) requirements and design
✓
Data warehouses technologies, Data integration and
Analytics
✓
DATA ENGINEER

12. 12
Survey Data Knowledge (Knowledge Areas)
a = active knowledge needed, p = passive knowledge needed
green = knowledge present at right level, brown = no knowledge present at right level
3

13. 13
Data Management Principles
Low score, passive knowledge present, too little active knowledge.
Data Management Systems
Score ok, knowledge needed on “Data Base Design and Models”.
Data Management Architecture
Score on active knowledge for Data Business Analist and Data Engineer low.
Data Governance
Score on active knowledge for Data Engineer too low.
Business Analytics
Score ok except active knowledge Data Business Analyst.
Business Analytics Management
Score passive knowledge ok, no active knowledge.
Findings on Developing Data Knowledge (Knowledge Areas)

14. 14
Results survey and FTE Needed
score 2,0-2,5
and
preference 2
score 2,0-2,5
and
preference 1
score > -2,5
and
preference 2
score > -2,5
and
preference 1
FTE Needed
Product Owner
5 2 7 6 3-5
Surplus
Scrum Master
2 1 3 1 2-3
Balanced
Data Business
Analyst 1 6 4 3 10-16
Enough interest, development
necessary
Data Engineer
3 5 1 4 10-16
Balanced, development necessary
Data
Administrator 0 8 0 1 4-5
Surplus and develpment necessary
Tester
11 2 1 1 5-6
Second preference surplus but
overall development needed

15. 15
Curriculum 5
Personal Competences
Curriculum UWV Datawarehouse
∆ 21. Active Listening
Consultancy Skills - Communicating
Communicating in teams
Giving Feedback
∆ Data Management Systems
Data Awareness
Dimensional Modelling
ETL - Extract, Transform, Load
Operational Data Modelling
∆ Data Management Architecture
Master Data Management & Reference Data
Management
Agile Information Management
Business Intelligence Data Warehouse Concepts
Data Warehouse Concepts
∆ A.1. IS and Business Strategy Alignment
Enterprise Design Foundation
Management Development Program
∆ A.3. Business Plan Development
Business Case
Analysing techniques
∆ A.4. Product/Service Planning
Scrum Kick-start
∆ A.6. Application Design
Scrum Kickstart
∆ A.9. Innovating
Scrum Product Owner
Data Knowledge & Tooling Professional Competences
∆ B.2. Component Integration
DevOps Awareness
∆ Data Management General Principles
Introduction Data Modelling
∆ Data Governance
e.g. Hadoop Advanced Administration or
Hydra or HPCC or Google Big Query etc.
∆ Business Analytics Management
Agile Requirements
UML Fundamentals
Define & Refine Use Cases
Requirements Engineering - the life cycle
∆ B.1. Application Development
Scrum Kickstart
Software Engineering Track
MTA HTML5 Application Development
Fundamentals
∆ 23. Motivating others
Understanding Behaviour Patterns (REED 1)
Affecting Behaviour Patterns (REED 2)
Leadership and coaching
Strategic coaching
Train the trainer
∆ 25. Organisational awareness
Separate the people from the problem
Psychology in organisations
Essence in behaviour
∆ 4. Working accurately
Pyramid Principle
Working in teams
Working effectively in teams
Time management
Communicating in teams
Giving Feedback
∆ Business Analytics
UX Awareness
Customer Journey Design
∆ 20. Customer Focus
Client centricity
Consultancy Skills - Advising
∆ A.5. Architecture Design
Agile Architecture
Enterprise Design Foundation

16. 16
Actions
- Individual Assessment reports
- Individual and Management discussed and decide on career path
- Individual training plans
- Group training
- Extra capacity for maintaining current data warehouse
Extra
• Motivated employees by providing new services in Data Science to UWV stakeholders and
personal investment in people.
• Same approach will be used for other parts of Data Services and UWV
Current situation

17. ‘We offer people new
prospects of
participating in work and
society'

18. 18
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