Level 2 "Getting FAIR"
- Giovanni Nisato
Level | Nickname | Marketplace metaphor | Features | Picture |
2 | "Getting FAIR" | "Street Market" | Pilots for FAIR implementation are in place |  |
Table level 2 summary.
FAIR Maturity Matrix: maturity levels (columns) | Level 2: "Getting FAIR" summary
Level 2 - Capabilities
The organization ensures findability of its own data through unique identifiers, standardized metadata, and data registries. Unique identifiers facilitate specific dataset searches, while standardized metadata schemas provide comprehensive descriptions. Data registries organize datasets with searchable interfaces based on criteria like keywords, enhancing discoverability. Data and metadata are retrieved by their identities using standardized resolution protocols.
Level 2 - Business value
Speed, cost-effectiveness, and machine automation increase. Use-cases for machine-findable and interpretable data start becoming possible. Data access quantification and qualification (license, costs) reduce barriers to access. Being decoupled from applications, data increases its applicability, further reducing the need for new data creation or acquisition.
Level 2 - Questions to ask
How is data being cataloged within the organization, and what role does metadata play in this process?
Do metadata documents contain their own identifiers and other ones for the data?
Are the protocols for resolution of data and metadata identifiers universally implementable? Are they open? Are they free?
Are there license documents for both the data and its associated metadata which are retrievable by humans?
What role do champions play in the early stages of FAIR data implementation, and how are teams forming under their leadership?
Which efforts are being made to align FAIR implementation with existing data strategies and models within the organization?
What efforts are being made to designate and recognize FAIR-related roles within the company?
How is the organization conducting needs assessments to structure formal training frameworks for FAIR data practices?
In what ways are efforts being made to standardize FAIR data processes and coordinate proof-of-concept projects?
How are designated roles shaping the understanding and formalization of FAIR knowledge within the company?
What internal and external resources are being utilized to develop FAIR training tailored to the organization's needs?
What tools have been introduced to capture and publish metadata within the organization?
How is the organization seeking or creating reference tools, such as controlled vocabularies, ontologies, and data standards, to support FAIR data principles implementation?
Level 2 - FAIR data
Data, conformed to a local model, is cataloged (i.e. a metadata record of existence is created), and data resides in a data lake.
At the organization level, data is conformed system-by-system and may not map to a domain data model.
Controls on access at the system level.
Requires average level technical and subject matter knowledge to use. ( cf. the role of data scientist).
(Back to the FAIR Matrix).
Level 2 - FAIR leadership
Initial "C-suite" workshops are starting on the need and value of FAIR data.
Initial projects to make data FAIR are starting.
The company has some champions, and first teams start to gather.
Internal workshops are starting to create more awareness in the company.
More leadership members in the company have started to understand FAIR data and its value.
More "buy-in" is happening: leadership-funded initiatives, commitment to enabling key steps, and Memberships to enabling organizations (e.g. for PIDs ).
(Back to the FAIR Matrix).
Level 2 - FAIR strategy
The first version of the vision, strategy and plan has approval.
This strategy may be at the highest level of the company but also more localized levels.
Some champions in the company start to appear.
More people in the company are starting to understand FAIR data and its strategic value.
People follow FAIR awareness or tactical implementation plans, which can be local to various parts of the company.
They learn about what works and what doesn't.
They also share this in communities while picking up new knowledge as input for their strategy.
Efforts to align FAIR implementation to Data Strategy and "data models", if they exist (cf. data models in "tools" dimension). If those are not yet available, there is an awareness they are needed.
FAIR emerges as an element of a broader Data strategy.
The realization that technical choices, architectures and partnerships have strategic implications, e.g. on the policy and concrete implementation of PIDs, URI, and GUPRIs.
(Back to the FAIR Matrix).
Level 2 - FAIR roles
Key Role: Data Scientist.
Emerging roles: Data standard expert, data curator, semantic web expert, data strategist, Data stewards (cf. governance as a "Process"), Data product "owners" (to do: define a "data product" in LEXICON tab ). Community of practice leads / Community managers.
Designated roles start to get shaped in the company.
The same individual often has multiple roles.
They are beginning to have designated FAIR-related roles: advertising for new positions and recognising the FAIRification work that some people have done in their previous roles.
The first champions and teams of various roles mentioned above develop Minimal Viable Products or services showcasing what prototypes can do, prove value and get internal recognition.
(Back to the FAIR Matrix).
Level 2 - FAIR processes
The organization conducts needs assessments to structure formal training frameworks while continuing ad hoc training initiatives. At least one pilot project showcases successful FAIR implementation, emphasizing impact and value.
Efforts toward aligning individual FAIR data processes are underway but still need to be standardized, with ongoing coordination and integrated proof-of-concept projects in development.
Specific training on FAIR principles, such as persistent URIs, involves cross-board participation, including procurement.
Regular retrospectives on pilot projects inform ongoing learning and improvements.
The organization possesses comprehensive metadata and awareness of data specifics. Although not all data details are within a FAIR catalog, there's an insight into licenses, refresh cycles, lineage, rationale, intended usage, and business value. Process design ensures knowledge of these aspects, particularly in procurement, contributing to a more comprehensive understanding of data assets.
Culture change. The first projects to make data FAIR are starting. A plan to make all data FAIR starts to shape. Competence grows, and buy-in starts to happen on the business side.
Processes include data curation, metadata guidelines definition, and retrospective data FAIRification. FAIR data procurement has defined requirements.
Governance: recognize the need for governance processes related to FAIR principles and the processes for FAIR (e.g. master and reference data, consideration of dimensions for FAIR, RDMs ).
Governance processes may have other names. They focus on conflict resolution, reconciliation, and community building.
(Back to the FAIR Matrix).
Level 2 - FAIR knowledge
Designated roles start to take shape in the company.
FAIR knowledge is largely in people yet needs more formalization.
The first champions and teams in prototypes can do it, explain it, prove value and start to get internal recognition.
Community of practice also forms internally and facilitates knowledge exchanges ( for example, exchanges around emerging/good practices for FAIR and data Stewardship ).
External resources help develop FAIR training based on the organization's specificities.
Knowledge on processes, e.g. "FAIRification", what can be considered "FAIR" and what is not "FAIR" begins to be shared.
"Vendors" (e.g. publishers) and "service providers" (e.g. CROs) curate, distribute, and generate data and play a role in the FAIR knowledge ecosystem.
(Back to the FAIR Matrix).
Level 2 - FAIR tools and infrastructures
At this stage, the following are likely to be implemented (at least in some departments, do not need to be company-wide): Tools introduced to capture and publish metadata; Tools to work with controlled vocabularies; Tools to work with / manage persistent identifiers (PIDs).
"FAIR data points" capturing and tooling may depend on the specific pharma function.
Configure existing systems to implement FAIR, e.g., assigning PIDs to key documents, persons in organizational structures dealing with Human Resource, grant management, and publishing (authoring).
The deployment of FAIR data maturity framework assessments (see "Lexicon")
Initial infrastructure and tooling are in place for the first working POCs. Now that POCs work and prove value, RFPs or company-wide plans for tooling and infrastructure are getting scoped, including additional capacity for putting that infrastructure and tooling in place (people and budget).
"Vendors", e.g. for ELN, play a key role in providing tools toward FAIR data principle implementation.
The organization may look for reference "tools", e.g. controlled vocabularies, ontologies, data standards.
(Back to the FAIR Matrix).
Findability:
Tool(s) or infrastructure component which contributes, enhances or enriches Findability:
Accessibility:
Tool(s) or infrastructure component(s) which contributes, enhances or enriches Accessibility.
Interoperability:
Tool(s) or infrastructure component(s) which contributes, enhances or enriches Interoperability.
(less in focus at this stage)
Reusability:
Tool(s) or infrastructure component(s) which contributes, enhances or enriches Reusability.
(less in focus at this stage)