EDISON Data Science Framework:
Building the Data Science Profession
Introduction to discussions
Yuri Demchenko, EDISON
University of Amsterdam

EDISON Data Science Champions
conference July 2016
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

13 July 2016, New Forest, Brockenhurst, UK

Outline•

Background and motivation––•

Demand for Data Science and data related professions
European initiatives related to Digital Single Market (DSM) and demand to data relatedcompetences and skills

EDISON Data Science Framework– From Data Science Competences to Body of Knowledge and Model Curriculum•

Data Science Competence Framework (CF-DS)–•

Data Science Professions family and competence profiles (DSP)–•

Profiles definition and linking to CF-DS

Data Science Body of Knowledge (DS-BoK)••

Essential competences – Suggested use – Discussion questions

Knowledge areas – Suggested use – Discussion questions

Data Science Model Curriculum (MC-DS)–

Learning Outcomes and Academic disciplines – Suggested use – Discussion questions

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Demand for Data Science and data related
professions•

McKinsey Global Institute on Big Data Jobs (2011)
http://www.mckinsey.com/mgi/publications/big_data/index.asp– Estimated gap of 140,000 - 190,000 data analytics
skills by 2018•

UK Big Data skills report 2014
– 6400 UK organisations with 100+ staff will have implemented
Big Data Analytics by 2020
– Increase of Big Data jobs from 21,400 (2013) to 56,000 (2017)•

IDC Report on European Data Market (2015)– Number of data workers 6.1 mln (2014)– increase 5.7% from 2013
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply 509,000 (2014) or 7.5%•

HLEG report on European Open Science Cloud (2016) identified need fordata experts and data stewards– Recommendation: Allocate 5% from grant funding for Data management andpreservation– Estimation: More than 80,000 data stewards (1 per every 20 scientists)– Core data experts need to be trained and their career perspective improved

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Recent European Commission Initiatives
Digitising European Industry: Reaping the full benefits of a Digital Single Market. COM(2016)
180 final, Brussels, 19.4.2016•

The need for new multidisciplinary and digital skills is exploding, including such as (Data Scientist)
combining data analytics and business or engineering skills.
–

Expected rapidly growing demand will lead to more than 800 000 unfilled vacancies by 2020

New skills agenda for Europe. Working together to strengthen human capital, employability andcompetitiveness, COM(2016) 381 final, Brussels, 10.6.2016• Address the need for digital and complementary skills, ensure young talents flow into data
driven research and industry, recognition and career development• (Re-) Launch the Digital Skills and Jobs Coalition (end of 2016)• Develop comprehensive national digital skills strategies by mid-2017
European Cloud Initiative - Building a competitive data and knowledge economy in Europe,COM(2016) 178 final, Brussels, 19.4.2016•

European Open Science Cloud (EOSC) and European digital research and data infrastructure––•

To offer 1.7 million European researchers and 70 million professionals in science and technology open and seamlessservices for storage, management, analysis and re-use of research data
Create incentives for academics, industry and public services to share their data

Growing demand and shortage of data-related skills and lack of recognition of their valueChampions Conf 2016

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EDISON Data Science Framework (EDSF): Creating theFoundation for Data Science Profession
CF-DS

DS-BoK

MC-DS

Taxonomy and Vocabulary
Data Science
Framework

EDISON Online
Educational Environt

Edu&Train Marketpltz
and Directory

DS Prof Family
ServicesFoundation & Concepts

Roadmap &Sustainability• Community
Portal (CP)• Professional
certification• Data Science
career & prof
development

Biz Model

EDISON Framework components

Other components and services

•
•
•
•

• EOEE - EDISON Online Education Environment• Education and Training Marketplace andResources Directory• Data Science professional certification andtraining• Community Portal (CP)CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSP - Data Science Professions family andprofessional competence profiles• EOEE - EDISON Online Education Environment

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EDISON Data Science Framework5CF-DS – Data Science Competence Framework• Introduction to CF-DS– Background standards– How it was made– 5 main Data Science competences groups– Skills, tools and languages• How it can be used
• Discussion questions

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Background Frameworks and Standards• NIST SP1500 – 2015: Big Data Interoperability Framework (Volume 1-7)
– Definitions of Data Science by NIST Big Data WG• e-CFv3.0 - European e-Competence Framework for IT– Structured by 4 Dimensions and organizational processes• Competence Areas – Competences - Proficiency levels - Skills and Knowledge• CWA 16458 (2012): European ICT Professional Profiles Family Tree
– Defines 23 ICT profiles for common ICT jobs

• ESCO (European Skills, Competences, Qualifications and Occupations)framework– Standard for European job market since 2016
– Intended inclusion of the Data Science occupations family – end of 2016

• ACM Classification of Computer Science – CCS (2012)– ACM Computer Science Body of Knowledge (CS-BoK) and ACM and IEEE Computer
Science Curricula 2013 (CS2013)

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7How it is made: Jobs market analysis andCommunity survey
Demanded Data Science Competences and Skills• Initial Analysis (period Aug – Sept 2015)– IEEE Data Science Jobs (World but majority US)• Collected > 120, selected for analysis > 30

– LinkedIn Data Science Jobs (NL)
• Collected > 140, selected for analysis > 30

– Existing studies and reports + numerous blogs & forums– Automatic job market survey tool: to be operational in Fall 2016

• Analysis methods– Using data analytics methods: classification, clustering, expert evaluation– Research methods: Data collection - Hypothesis – Artefact - Evaluation• Validation and community input– Survey on the general Data Science competences based on CF-DS• Domain related competences yet to be surveyed– Workshops and community feedback
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8Data Scientist definition by NIST
Definitions by NIST Big Data WG (NIST SP1500 - 2015)•

A Data Scientist is a practitioner who has sufficient knowledge
in the overlapping regimes of expertise in business needs,
domain knowledge, analytical skills, and programming andsystems engineering expertise to manage the end-to-end
scientific method process through each stage in the big data
lifecycle.

•

Data Lifecycle in Big Data and Data Science•

Data science is the empirical synthesis of actionableknowledge and technologies required to handle data from raw
data through the complete data lifecycle process.

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EDISON Data Science Framework[ref] Legacy: NIST BDWG
definition of Data Science9Identified Data Science Competence Groups•

Commonly accepted Data Science competences/skills groups include
–
–
–

•

Data Analytics or Business Analytics or Machine Learning
Engineering or Programming
Subject/Scientific Domain Knowledge

EDISON identified 2 additional competence groups demandedby organisations[ref] Legacy: NIST BDWG
definition of Data Science– Data Management, Curation, Preservation– Scientific or Research Methods and/vs Business Processes/Operations•

Other skills commonly recognized aka “soft skills” or “social intelligence”–•

All groups need to be represented in Data Science curriculum and training programmes–•Inter-personal skills or team work, cooperativeness

Challenging task for Data Science education and training: multi-skilled vs team based

Another aspect of integrating Data Scientist into organisation structure–––

General Data Science (or Big Data) literacy for all involved roles and management
Common agreed and understandable way of communication and information/data presentationRole of Data Scientist: Provide such literacy advice and guiding to organisation

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10Data Science Competence Groups - Research
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Operations
•
•
•
•
•

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Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design

11Data Science Competences Groups – Business
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Process
Operations/Stages
•
•
•
•
•
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DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design12Identified Data Science Competence Groups (Updated)
Data Science Analytics Data Management(DSDA)(DSDM)

Data Science Engineering Research/Scientific Methods(DSENG)(DSRM)

0

Use appropriate
statistical techniques
and predictive
analytics on available
data to deliverinsights and discover
new relations

Develop andimplement data
management
strategy for datacollection, storage,
preservation, andavailability forfurther processing.

Use engineering
principles to research,
design, develop andimplement new
instruments andapplications for datacollection, analysis andmanagement1

DSDA01
Use predictive
analytics to analyse big
data and discover new
relations2

DSDA02
Use statistical techniq
to deliver insights

DSDM01Develop andimplement data
strategy, in particular,
Data Management
Plan (DMP)DSDM02Develop data models
including metadata

DSENG01
Use engineering principles
to design, prototype data
analytics applications, ordevelop instruments,
systems
DSENG02
Develop and apply
computational solutions

3

DSDA03Develop specialized …

DSDM03
Collect integrate data

4

DSDA04
Analyze complex data

5

DSDA05Use different analytics

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Data Science Domain
Knowledge, e.g. Business
Processes (DSDK/DSBPM)
Create new understandings andUse domain knowledge
capabilities by using the scientific (scientific or business) tomethod (hypothesis,
develop relevant data analytics
test/artefact, evaluation) orapplications, and adopt generalsimilar engineering methods toData Science methods todiscover new approaches todomain specific data types andcreate new knowledge andpresentations, data and processachieve research ormodels, organisational roles
organisational goals
and relations
DSRM01Create new understandings andcapabilities by using scientific/
research methods or similar domain
related development methods

DSBPM01
Understand business and provide
insight, translate unstructured
business problems into an abstract
mathematical framework
DSBPM02
Participate strategically andtactically in financial decisions

DSENG03
Develops specialized tools

DSRM02
Direct systematic study toward afuller knowledge or understandingof the observable facts
DSRM03
Undertakes creative work

DSDM04Maintain repository

DSENG04
Design, build, operate

DSRM04
Translate strategies into actions

DSBPM04
Analyse data for marketing

DSDM05Visualise cmplx data

DSENG05
Secure and reliable data

DSRM05Contribute to organizational goals

DSBPM05
Analyse optimise customer relatio

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DSBPM03
Provides support services to other13

Identified Data Science Skills/Experience Groups•

Group 1: Skills/experience related to competences
–
–
–
–
–
–

•

Group 2: Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Big Data Analytics platforms
Mathematics & Statistics applications & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
Cloud based platforms and tools

Group 3: Programming and programming languages and IDE–•

Data Analytics and Machine Learning
Data Management/Curation (including both general data management and scientific data
management)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods or Business Process Management
Application/subject domain related (research or business)
Mathematics and Statistics

General and specialized development platforms for data analysis and statistics

Group 4: Soft skills or Social Intelligence–

Personal, inter-personal communication, team work, professional network

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Data Science Skill Groups related to Competences
Data Analytics
and Machine
Learning

Data
Management/
Curation1

Artificial
intelligence,machine learning

2

Machine Learning
and Statistical
Modelling

Manipulating andanalyzing complex,
high-volume, highdimensionality datafrom varying sources
Data models anddatatypes

3

4
5

Machine learning
solutions andpattern recognition
techniquesExperience ofworking with large
data sets

Supervised andunsupervised
learning
Data mining(non)relational and(un)-structured data

Markov Models,Conditional
Random Fields
Logistic Regression,
7 Support Vector
Machines
Predictive analysis
8 and statistics(including Kaggle
platform)(Artificial) Neural
9 Networks
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Cloud based data
storage and data
management
Data management
planning
Metadata annotationand management
Data citation,
metadata, PID (*)

Data Science
Engineering(hardware andsoftware)Design efficientalgorithms foraccessing andanalyzing large
amounts of data
Big Data solutions
and advanced data
mining tools

Scientific/ Research
Methods
Analytical, independent,
critical, curious andfocused on results
Confident with large
data sets and ability toidentify appropriate
tools and algorithms
Flexible analytic
approach to achieve
results at varying levelsof precision

Multicore/distributed
software, preferably
in a Linux
environment
Databases, database Interest in data science,systems, SQL andexceptional analytical
NoSQL
skillsStatistical analysis
languages and tooling
Cloud powered
applications design

Personal/Interpersonalcommunication,team work
Communication
skillsApplication/subjec
t domain (researchor business),
examplesRecommender or
Ranking system

Inter-personal intra- Data Analytics forteam and external
commercial
communication
purposes
Network ofcontacts in Big Data
community

Data sources andtechniques forbusiness insight andcustomer focus
Mechanism Design
and/or Latent
Dirichlet AllocationGame Theory
Copyright and IPR

Mathematics foundation:
Knowledge of mathematics, calculus,
probability theory and statistics

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Identified Big Data Tools and Programming Languages
Big Data Analytics
platforms

Math& Stats tools

Databases

Data/ applications
visualization

Data Management andCuration platform

1

Big Data Analytics platforms Advanced analytics tools(R, SPSS, Matlab, etc)

SQL and relational
databases

Data visualization Libraries(D3.js, FusionCharts, Chart.js,
other)

Data modelling and related
technologies (ETL, OLAP,
OLTP, etc)2

Big Data tools (Hadoop,
Spark, etc)

Data Mining tools:
RapidMiner, others

NoSQL Databases

Visualisation software (D3,Processing, Tableau, Gephi,
etc)

Data warehouses platform
and related tools

3

Distributed computing tools
a plus (Spark, MapReduce,
Hadoop, Hive, etc.)

MathlabNoSQL, Mongo, Redis

Online visualization tools(Datawrapper, Google Charts,
Flare, etc)

4Real time and streaming
analytics systems (likeFlume, Kafka, Storm)

PythonNoSQL, Teradata

Data curation platform,
metadata management (ETL,
Curator's Workbench,
DataUp, MIXED, etc)
Backup and storage
management (iRODS, XArch,
Nesstar, others

5

HadoopEcosystem/platform

R, Tableau R

Excel

SpotfireSAS

7

Azure Data Analytics
platforms (HDInsight, APS
and PDW, etc)

Scripting language, e.g.
Octave8

Amazon Data Analytics
platform (Kinesis, EMR, etc)

Statistical tools and data
mining techniques

69

Other cloud based Data
Analytics platforms, e.g.
HortonWorks, Vertica
LexisNexis HPCC System

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Other Statisticalcomputing andlanguages (WEKA,KNIME, IBM SPSS, etc)

Highlighted:
Cloud based and online data analytics
and data management platforms

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Suggested Practical Application of the CF-DS•

Basis for the definition of the Data Science Body of Knowledge (DS-BoK) andData Science Model Curriculum (MC-DS)– CF-DS => Learning Outcomes (MC-DS) => Knowledge Areas (DS-BoK)– CF-DS => Data Science taxonomy of scientific subjects and vocabulary•

Data Science professional profiles definition– Extend existing EU standards and occupations taxonomies: e-CFv3.0, ESCO, others•

Automatic job market monitor and survey tool
– To be operational in Fall 2016•

Professional competence benchmarking (including CV and training programmes
matching)– For customizable training and career development•

Professional certification– In combination with DS-BoK and professional competences benchmarking•

Vacancy construction tool for job advertisement (for HR)– Using controlled vocabulary and Data Science Taxonomy

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CF-DS - Discussion Questions• DQ1: Collecting contribution from domain areas and experts– EDISON Survey for general Data Science competences and for target
communities• DQ2: Contributing to current standardisation activities– CEN e-CF workshop and CEN TC428 on standardisation of ICT
competences and profiles• Extended mandate to define curriculum requirements/model• Part of New Skills Agenda for Europe– Extending CF-DS with dimensions on proficiency levels and skills andknowledge

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DSP – Data Science Professional Profiles• Introduction to DSP– Data Science Professions family and ESCO taxonomy• ESCO – European– New 5 occupation/professional groups and 19 professional profiles• How it can be used
• Discussion questions

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19Data Science Professions FamilyManagers: Chief Data Officer (CDO), Data Science (group/dept) manager,
Data Science infrastructure manager, Research Infrastructure manager
Professionals: Data Scientist, Data Science Researcher, Data Science
Architect, Data Science (applications) programmer/engineer, Data
Analyst, Business Analyst, etc.
Professional (database): Large scale (cloud) database designers andadministrators, scientific database designers and administrators
Professional and clerical (data handling/management): Data Stewards,
Digital Data Curator, Digital Librarians, Data Archivists
Technicians and associate professionals: Big Data facilities operators,
scientific database/infrastructure operators
Icons used: Credit to [ref] https://www.datacamp.com/community/tutorials/data-science-industry-infographic

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20Data Science Occupations:
Extension for the ESCO (2016) taxonomy (1)Professionals
Science andengineering
professionals

Data Science
Professionals

Data Science professionals
not elsewhere classified

DSP04
Data Scientist

Large scale (cloud) data
storage designers andadministrators

DSP05
Data Science Researcher
DSP08(Big) Data Analyst
DSP07
Data Science (Application)
Programmer
DSP09
Business Analyst
DSP14
Large scale (cloud) database
designer*)

19 DSP# Enumerated Data Science
profiles defined by EDISON
Framework
Database and network
professionals

Database designers andadministrators

Information andcommunications
technology
professionals

Data Science
technology
professionals

DSP15
Large scale (cloud) databaseadministrator*)
Database and network
DSP16
professionals not elsewhere Scientific databaseclassified
administrator*)
Data handling professionals DSP12
not elsewhere classified
Digital Librarian
DSP13
Data Archivist
DSP10
Data Steward
DSP11
Data curator

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21Data Science Occupations:
Extension for the ESCO taxonomy (2)Technicians and associate professionals
Science andengineering associate
professionals

Data Science Technology
Professionals

Data Infrastructureengineers and technicians

Database and network
professionals not elsewhereclassified

DSP17
Big Data facilities Operators
DSP18
Large scale (cloud) data storage
operators
DSP19
Scientific database operator*)

Managers
Production andspecialised services
managers

Data Science/Big Data
Infrastructure Managers
Research InfrastructureManagers

DSP01/DSP02
Data Science/Big Data
Infrastructure Manager
DSP03
RI Manager
DSP03
RI Data storage facilities
manager

Digital Archivists andLibrarians

DSP12*
Digital Librarian

Clerical support workers
General and keyboard
clerksData handling support
workers (alternative)

Data and information entry
and access

DSP13*
Data Archivist
DSP10*
Data Steward
DSP11*
Data curator
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EDISON Data Science Framework22• Example mapping DSP profiles
to competences– To be revised by experts andpractitioners

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Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE
Data Science or Data Management Group/Department>> Reporting to CDO/CTO/CEO

•
•
•
•
•

• Providing cross-organizational services• Maintaining Data Value ChainGroup Manager
Data Science Architect
Data Analyst
Data Science Application programmer
Data Infrastructure/facilities administrator/operator: storage,
cloud, computation• Data stewards

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24Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE
Data Science or Data Management Group/Department>> Reporting to CDO/CTO/CEO• Providing cross-organizational services• Maintaining Data Value Chain

•
•
•
•

(Managing) Data Science Architect (1)Data Analyst (1)Data Science Application programmer (2)Data Infrastructure/facilities administrator/operator: storage,
cloud, computing (1)• Data stewards, curators, archivists (3-5)

Estimated: Group of 10-12 specialists for research institution of
200-300 research staff.
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Discussion Questions• DQ1: Mapping DSP profiles to competences– Collecting input from different professional groups to define specific competences– Using CF-DS competences and running targeted surveys• DQ2: Including DSP family into the next version of ESCO
• DQ3: Mapping DSP profiles to MC-DS and academic disciplines– Link to offered education and training and required certification

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DS-BoK – Data Science Body of Knowledge• Introduction to DS-BoK– Knowledge Area Groups• How it can be used
• Discussion questions

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Data Science Body of Knowledge (DS-BoK)
DS-BoK Knowledge Area Groups (KAG)• KAG1-DSA: Data Analytics group including
Machine Learning, statistical methods,
and Business Analytics• KAG2-DSE: Data Science Engineering group
including Software and infrastructure engineering• KAG3-DSDM: Data Management group including data curation,
preservation and data infrastructure• KAG4-DSRM: Scientific/Research Methods group• KAG5-DSBP: Business process management group• Data Science domain knowledge to be defined by related expert groups

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KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide for
performing data management”– 11 Knowledge Areas(1) Data Governance(2) Data Architecture(3) Data Modelling and Design(4) Data Storage and Operations(5) Data Security(6) Data Integration andInteroperability(7) Documents and Content(8) Reference and Master Data
(9) Data Warehousing and BusinessIntelligence(10) Metadata(11) Data Quality

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Other Knowledge Areas motivatedby RDA, European Open Data
initiatives, European Open Data
Cloud(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data,
Open Access, ORCID(15) Responsible data use•

EDISON Data Science Framework

Highlighted in red: Considered
Research Data Management literacy(minimum required knowledge)
29Research Data Management Model Curriculum –Part of the EDISON Data Literacy Training
A. Use cases for data management and stewardship•
Preserving the Scientific Record
B. Data Management elements (organisational and individual)•
Goals and motivation for managing your data•
Data formats•
Creating documentation and metadata, metadata for discovery•
Using data portals and metadata registries•
Tracking Data Usage•
Handling sensitive data•
Backing up your data•
Data Management Plan (DMP) - to be a part of hands on session

To be supported by RDA WG on RDM Literacy•
BoF at RDA8 16 Sept 2016, Denver•Contribution: Europe, US, AP•
Modular, customizable•
Localised: resources and languages•
Open Source under Creative Common
Attribution

C. Responsible Data Use Section (Citation, Copyright, Data Restrictions)
D. Open Science and Open Data (Definition, Standards, Open Data use and reuse, open government data)•
Research data and open access•
Repository and self- archiving services•
ORCID identifier for data•
Stakeholders and roles: engineer, librarian, researcher•
Open Data services: ORCID.org, Altmetric Doughnut, Zenodo
E. Hands on:a) Data Management Plan designb) Metadata and toolsc) Selection of licenses for open data and contents (e.g. Creative Common and Open Database)Champions Conf 2016

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MC-DS – Data Science Model Curriculum• Introduction to MC-DS– Learning Outcomes based on CF-DS– Academic disciplines and courses based on CCS2013• How it can be used to design a curriculum• Discussion questions

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MC-DS – Data Science Model Curriculum• Switch to website or document view

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MC-DS: Discussion Questions• DQ1: Background knowledge/prerequisite– How to impose necessary mathematics and computer knowledge andskills in all Data Science programmes?
– How to teach Scientific and Research Methods in the most effectiveway?

• DQ2: MC-DS and curriculum design for dynamicallychanging technology landscape– Creating mentality of self-reskilling workforce• DQ 3: Top down vs bottom up approach in developing Data
Science curricula– Universities practices• DQ6. Benefits and challenges in adopting ACM Classification
Computer Science (2012) and ACM/IEEE CS curriculum
guidelines– How to extend current ACM classification to reflect requiredcompetences for Data Science? Including Domain related?
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Certification and Accreditation• Certification scheme to be delivered by Sept 2016
• To be based on CF-DS and DS-BoK• Using experience of the FitSM by EGI• Talk to Małgorzata Krakowian (EGI)Champions Conf 2016

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Data Science Professional Portal andSustainability• Switch to separate presentation by Andrea Manieri(Engineering)Champions Conf 2016

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Discussion• Questions• Observations• Suggestions•

Survey Data Science Competences [1]: Invitation to participate
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession•

Community discussion documents: Request for comments––––

Data Science Competence Framework
http://edison-project.eu/data-science-competence-framework-cf-ds
Data Science Body of Knowledge
http://edison-project.eu/data-science-body-knowledge-ds-bok
Data Science Model Curriculum
http://edison-project.eu/data-science-model-curriculum-mc-ds
Data Science Professional Profiles
http://edison-project.eu/data-science-professional-profiles

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Suggested e-CF Competences for Data Science:
Presented at eCF Workshop meeting on 14 April 2016
A. PLAN and Design (9 basic competences)•
A.10* Organisational workflow/processes model definition/formalisation•
A.11* Data models and data structures
B. BUILD: Develop and Deploy/Implement (6 basic competences)•
B.7* Apply data analytics methods (to organizational processes/data)•
B.8* Data analytics application development•
B.9* Data management applications and tools
15 Data Science Competences proposed•
B.10* Data Science infrastructure deployment
C. RUN: Operate (4 basic competences)•
C.5* User/Usage data/statistics analysis•
C.6* Service delivery/quality data monitoringcovering different organizational roles andworkflow stages• Data Scientist roles are crossing multiple
org roles and workflow stages

D. ENABLE: Use/Utilise (12 basic competences)•
D10. Information and Knowledge Management (powered by DS)•
D.13* Data presentation/visualisation, actionable data extraction•
D.14* Support business processes/roles with data and insight (support to D.5, D.6, D.7, D.12)•
D.15* Data management/preservation/curation with data and insight
E. MANAGE (9 basic competences)•
E.10* Support Management and Business Improvement with data and insight (support to E.5, E.6)•
E.11* Data analytics for (business) Risk Analysis/Management (support to E.3)•
E.12* ICT and Information security monitoring and analysis (support to E.8)

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Data Scientist and Subject Domain Specialist•

Subject domain components
–
–
–
–
–

•

Model (and data types)
Methods
Processes
Domain specific data and presentation/visualization methods
Organisational roles and relations

Data Scientist is an assistant to Subject Domain Specialists– Translate subject domain Model, Methods, Processes into abstract data driven form
– Implement computational models in software, build required infrastructure and tools– Do (computational) analytic work and present it in a form understandable to subject
domain– Discover new relations originated from data analysis and advice subject domain
specialist– Present/visualise information in domain related actionable way– Interact and cooperate with different organizational roles to obtain data and deliver
results and/or actionable data•

Overall goal: Maintain the Data Value Chain:–

Data Integration => Organisation/Process/Business Optimisation => Innovation

Champions Conf 2016

EDISON Data Science Framework

38Data Science and Subject Domains
Data Science domain components
Data structures &databases/storage
Visualisation• Abstract data driven
math&compute models• Data Analytics
methods• Data and Applications
Lifecycle Management

Crossorganisational
assistive role

Data Scientist functions is to translate between two domains

Domain specific components
Domain specificdata & presentation(visualization)• Models (and data types)• Methods• Processes

Organisationalroles

Data Scientist role is to maintain the Data Value Chain (domain specific):•
Champions Conf 2016

Data Integration => Organisation/Process/Business Optimisation => Innovation
EDISON Data Science Framework39