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

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

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

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

Workforce Transformation Model

TOBE

SPRINTS

ASIS

SURVEY

Assessmen

t

Traninig

COMMUNICATION, COORDINATION, IMPLEMENTATION

WORKFORCEPLAN

1

3

4

5

6

MeasurementofPerformance

UnlockKnowledge• COMPETENCES

• JOBPROFILES

• CURRICULA• CAREERPATHS

2

Workshops to define job profiles and competences

Competence Profiles: first run > 6 new job profiles

8

Plan A.6.ApplicationDesign WorkingaccuratelyBuild B.1.ApplicationDevelopment CreativityBuild B.2.ComponentIntegration FlexibilityBuild B.3.Testing ResultorientationBuild B.4.SolutionDeployment StressresilienceBuild B.5.DocumentationProduction

SoftSkillsCompetences(e-CF)

1 4

DATAENGINEER

201

332

Starterlevel

1 21 3

Expertlevel

Example DATA BUSINESS ANALYST and DATA ENGINEER

Plan A.1.IS/BusinessStrategyAlignment AnalysingPlan A.3.BusinessPlanDevelopment CreativityBuild B.6.SystemsEngineering ActiveListeningEnable D.10.Info/KnowledgeManagement JudgementEnable D.11.NeedsIdentification PersuasivenessManage E.5.ProcessImprovement

DATABUSINESSANALYST

4

330

44

SoftSkillsStarterlevel Expertlevel

0 40 43 3

Competences(e-CF)

2

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

11

Workshops to define new profiles and data knowledge: second run Profiletitle

Summarystatement

Mission

Coreactivities

Starter Expert

A.6.ApplicationDesign 1 3

B.1.ApplicationDevelopment 2 3

B.2.ComponentIntegration 0 3

B.3.Testing 2 2

B.4.SolutionDeployment 1 2

B.5.DocumentationProduction 1 3

4.Workngaccurately* ü ü

10.Creativity ü ü

15.Flexibility ü ü

32.Resultorientation ü ü

35.Stressresilience* ü ü

Personalcompetences(GovernmentCompetence

Guide)

*=corecompetences

DATAENGINEERProvisionofdata

Finds,managesandmergesmultipledatasourcesandensuresconsistencyofdatasets.Ensuresassetprotectionthroughtheprovisionofclean,consistent,qualityassureddata.Maintainstheintegrityofdata,storesandsearchesdataandsupportspresentationofdataanalysis.

Client/customer:•MatchingcustomerwishesProduction•Designanddevelop•Measuredataquality•Roll-out/transferdatasolutiona•Coordinatingsolutions/deliveringfunctionalrequirements•Monitoringandadvisingonmarketdevelopments•Loadingdatafordevelopers•Problemmanagement•Determiningandaccessingdatasources•Performingdataanalysis•Conductimpactanalysis•Expert:CoachingBIandDataEngineersQuality:•Applystandards•Review•Proposeprocessimprovements•Qualitymeasurementofownwork.

Senioritylevel

e-competences(e-CF )

Dataknowledge DataManagementGeneralPrinciples Passive ActiveDatatyperegistries,(PID)PersistentIdentifier,Metadata

ü

Datalifecyclemanagement ü

DatainfrastructureandDatafactories ü

EthicalprincipleandDataprivacy ü

FAIR(Findable,Accessible,Interoperable)principlesinDatamanagement

ü

DataManagementSystems Passive ActiveDataarchitectures;(OLAP)OnlineAnalyticalProcessing,(OLTP)OnlineTransactionProcessing,ExtractionTransformationandLoad(ETL)

ü

Datamodelling,DatabasesandDatabasemanagementsystems

ü

Datastructures ü

DatamodelsandQuerylanguages ü

DatabasedesignandModels ü

Datawarehouses ü

DataManagementArchitecture Passive ActiveDatamanagement,includingReferenceandMasterdata ü

DatawarehousingandBusinessintelligence ü

Metadata,Linkeddata,Dataprovenance ü

Datainfrastructure,DataregistriesandDatafactories ü

Databackup ü

Dataanonymisation ü

Dataprivacy ü

DataGovernance Passive ActiveDatagovernance,Dataquality,DataintegrationandInteroperability

ü

Datamanagementplanning ü

Datamanagementpolicy ü

Datainteroperability ü

Datacuration ü

Dataprovenance ü

BusinessAnalytics Passive ActiveBusinessanalyticsandBusinessintelligence:Data,Models(statistical)andDecisions

ü

DatadrivenCustomerRelationsManagement(CRM),UserExperience(UX)requirementsanddesign

ü

Datawarehousestechnologies,DataintegrationandAnalytics ü

DATAENGINEER

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

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

Results survey and FTE Needed

score2,0-2,5andpreference2

score2,0-2,5andpreference1

score>-2,5andpreference2

score>-2,5andpreference1

FTENeeded

ProductOwner5 2 7 6 3-5

Surplus

ScrumMaster2 1 3 1 2-3

Balanced

DataBusinessAnalyst 1 6 4 3 10-16

Enoughinterest,developmentnecessary

DataEngineer3 5 1 4 10-16

Balanced,developmentnecessary

DataAdministrator 0 8 0 1 4-5

Surplusanddevelpmentnecessary

Tester11 2 1 1 5-6

Secondpreferencesurplusbutoveralldevelopmentneeded

15

Curriculum 5Personal Competences

Curriculum UWV Datawarehouse

∆ 21. Active Listening

Consultancy Skills - CommunicatingCommunicating in teams

Giving Feedback

∆ Data Management Systems

Data AwarenessDimensional Modelling

ETL - Extract, Transform, LoadOperational Data Modelling

∆ Data Management Architecture

Master Data Management & Reference Data Management

Agile Information ManagementBusiness Intelligence Data Warehouse Concepts

Data Warehouse Concepts

∆ A.1. IS and Business Strategy Alignment

Enterprise Design FoundationManagement Development Program

∆ A.3. Business Plan Development

Business CaseAnalysing 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 orHydra or HPCC or Google Big Query etc.

∆ Business Analytics Management

Agile RequirementsUML Fundamentals

Define & Refine Use Cases Requirements Engineering - the life cycle

∆ B.1. Application Development

Scrum KickstartSoftware Engineering Track

MTA HTML5 Application Development Fundamentals

∆ 23. Motivating others

Understanding Behaviour Patterns (REED 1) Affecting Behaviour Patterns (REED 2)

Leadership and coachingStrategic coaching

Train the trainer

∆ 25. Organisational awareness

Separate the people from the problemPsychology in organisations

Essence in behaviour

∆ 4. Working accurately

Pyramid PrincipleWorking in teams

Working effectively in teamsTime management

Communicating in teamsGiving Feedback

∆ Business Analytics

UX AwarenessCustomer Journey Design

∆ 20. Customer Focus

Client centricityConsultancy Skills - Advising

∆ A.5. Architecture Design

Agile ArchitectureEnterprise Design Foundation

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

‘We offer people new prospects of

participating in work and society'

18

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