At the Bio Data World conference in Basel in December 2019, Kees van Bochove, Founder of The Hyve gave a talk on re-use of pharma R&D data, and what strategies could be used to realize operationalization of FAIR data at scale.

1. Kees van Bochove, Founder, The Hyve
Reuse of R&D data and the
promise of FAIR data lakes
@keesvanbochove
BioDataWorld
Basel, 5 Dec 2019

2. Outline
1. FAIR Data is about people & change
2. The data lake is a passing phase
3. Forget about AI. Data & UX matter.

3. The Hyve
We advance biology and medical research…
… by building and serving thriving open source communities.
Services
Professional support for
open source software in
biomedical informatics
➢Software development
➢Data engineering
➢Consultancy
➢Hosting / SLAs
Core values
Share
Reuse
Specialize
Office Locations
Utrecht, The Netherlands
Cambridge, MA, United States
Customer Segments
Pharma
Life Sciences
Healthcare
Fast-growing
Started in 2012
40+ people by now

4. FAIR Data is
about people &
embracing change
Statement #1
@keesvanbochove @TheHyveNL

5. The roots of FAIR
►Public-private partnership to advance:
►Open Science
► Sustainability & reuse of data
►Workshop in Leiden in 2014
►Towards a Modular Blueprint ‘Floor-plan’ of a safe
and fair Data Stewardship, Trading and Routing
environment, provisionally called the Data
FAIRPORT
https://www.lorentzcenter.nl/lc/web/2014/602/info.php3?wsid=602

6. FAIR Workshop at The Hyve in Utrecht, 2018
http://blog.thehyve.nl/blog/highlights-from-pistoia-alliances-fair-workshop
https://www.sciencedirect.com/science/article/pii/S1359644618303039

7. 15 FAIR principles for (meta)data
http://www.nature.com/articles/sdata201618
Accessible:
A1. standardized protocol
A1.1 open, free and universally implementable
A1.2. authentication and authorization
A2. metadata stay accessible
Reusable:
R1. attributes
R1.1. license
R1.2. provenance
R1.3. community standards
Interoperable:
I1. language for knowledge representation
I2. vocabularies that follow FAIR principles
I3. qualified references to other (meta)data
Findable:
F1. persistent identifier
F2. metadata
F3. metadata - data link
F4. registered or indexed

8. The fundamental change behind FAIR
Data
management
Data
stewardship
scope: project scope: organization

9. GO-FAIR Initiative Pillars

10. Why resilience to change matters
● Domain changes and focus shifts: new data types,
new applications, new scientific paradigms etc.
● Organizational changes: M&A, re-orgs, people
moving roles etc.
● Technology changes: new software and hardware
platforms, analysis methods, automation, ML/AI etc.

11. FAIR Data is
about people &
embracing change
Statement #1
● From data management
to data stewardship
● Implies cultural, process
and technical change
● Data strategy should be
resilient to change
@keesvanbochove @TheHyveNL

12. The data lake is a
passing phase
Statement #2
@keesvanbochove @TheHyveNL

13. 15
Network architectures

14. Genomics
England
Research Environment
NHS Trusts
Airlock
Research Community

15. 17
MedMij Personal Health Apps

16. The classical monolith
Enterprise
Data Warehouse
ETL
ETL
ETL
Business Intelligence
/ Analytics

17. The modern (?) monolith
Ingest
Self-service
Pipelines
AnalyticsEnterprise Data Lake
Ingestion Team Data Engineering Team Unification TeamSearch TeamPlatform API Team Analytics Team
Architectural division
Axis of
change

18. Decentralized data management
● IRI / identifier schemes
● Metadata standards
● Provenance standards
CDO
Data Federation
{
{
Oncology
Neuro-
science Development
ClinOps
HCS
Omics platforms
Data science
Preclinical
ADME/Tox
Biomarker dev.
RWD
Epidemiology
● Catalog function
● Data standards
● Entities / data sets
Publish

19. Advantages of a decentralized FAIR approach
● More resilient to change: no dependency on large central functions
● Allows for an iterative data strategy operationalization (no ‘big bang’
data lake delivery needed, FAIRification can start today and locally)
● No need to shuffle people around to start a big data lake project:
embed informatics and data experts directly in the research and
development teams
● Centralize only standardization functions, decentralize the rest 
empower teams to do their own data science and informatics
● Embrace usage of external data and collaborations, no need to
‘ingest first’ via a central function, but use & link directly

20. The data lake is a
passing phase
Statement #2
● Centralization is a
potential bottleneck and
a barrier for change
● The solution is in
decentralization of
storage, applications etc.
● Standards management
and data federation as
central functions
@keesvanbochove @TheHyveNL

21. Forget about AI.
Data &UX matter.
Statement #3
@keesvanbochove @TheHyveNL

23. Teams at The Hyve: open source communities
Research Data Management
● FAIR Data Governance consultancy
● Fairspace (meta)data management
Genomics
● Cancer data portal: cBioPortal
● Knowledge base: Open Targets
Health Data Networks
● Data warehouses: tranSMART, i2b2
● Cohort selection: Glowing Bear
● Request Portals: Podium
Real World Data
● Real world evidence: OMOP/OHDSI
● Wearables platform: RADAR-BASE

24. FAIR Services at The Hyve
● Semantic modelling: creating (meta)data models that allow traversal of
linked data
● Data conformance: choose the right data standard for specific problems,
align with community standards to maximize benefits from the open
science communities and precompetitive collaborations
● Data landscape: create an understanding of existing applications and
data sources in the company and readiness for FAIR
● FAIRification: get started with FAIRifying datasets, defining metadata,
appropriate standards, provenance etc.
● Data catalog: build collaborative environment around data catalog (e.g.
using Fairspace)

25. Example: OMOP CDM v5 for RWE/RWD
● Observational
healthcare
data
● Fields defined
per domain
● Standardized
Vocabularies

26. cBioPortal: hard to resist value proposition
● 4000+ citations
in literature
● ~20k+ unique
users per
month
● Local instances
deployed in
many pharma
companies
and cancer
centers

27. Open
Targets
● Integration
of 20+ key
public data
sources for
target
discovery

28. Forget about AI.
Data &UX matter.
Statement #3
● Decision making by HIPPO
instead of by algorithms
● Make AI developers happy
with relevant FAIR data
● Strong semantics are key to
and standards can help
(e.g. OMOP, CDISC)
● Investments in UX are costly
& you should capitalize on
them (e.g. OpenTargets)
● It’s a great time to build
knowledge graphs!
@keesvanbochove @TheHyveNL

30. We advance biology and medical
sciences by building and serving
thriving open source communities