Level 1 "Started the FAIR journey"
- Giovanni Nisato
Level | Nickname | Marketplace metaphor | Key Features | Picture |
1 | "Started the FAIR journey." | "Flea market" | Awareness started, and the first pilots for implementation. |  |
Table level 1 summary.
FAIR Maturity Matrix: maturity levels (columns) | Level 1: "Started the FAIR journey" summary
Level 1 - Capabilities
The “F” principle is in focus, especially with the establishment and implementation of unique identifiers for key data sets, and the implementation of metadata catalogs.
Level 1 - Business value
Speed and cost are the main benefits. Humans can more efficiently locate important data assets; for example, researchers take much less time to find data. There is also a reduction in redundant data collection. Data is recognised as a Strategic Asset.
Level 1 - Questions to ask
Are there Globally Universal Identifiers (GUID) for key data and their metadata? Are they unique?
How does heterogeneity manifest in the organization's data landscape, and what efforts are being made to address this?
What steps are being taken to deploy identifiers and metadata within the organization?
How are top-down and bottom-up leadership approaches contributing to FAIR data initiatives within the organization?
What initial steps are being taken to craft a vision and plan for FAIR data within the organization?
What emerging roles are being recognized within the organization's FAIR data initiatives?
How is the organization determining which data should be made FAIR retrospectively, and what processes are being established for this purpose?
How are individuals connecting with external communities of practice to further their understanding of FAIR data?
How are existing IT practices being enhanced to accommodate data and metadata creation and curation?
Level 1 - FAIR data
Some data is cataloged and hosted in a data lake, and there is governance for accessibility; however, data is generally "unconformed" (meaning that no domain model is used to constrain information).
Data requires average technical and expert subject matter knowledge to use.
Heterogeneity is a feature of this stage: some data may be unstructured, while pockets of structured data may be in the organization. There is an emerging awareness and application of metadata. Scoping starts with making data FAIR. The deployment of Identifiers and metadata is beginning. Multiple types of identifiers (not necessarily GUPRI) may exist.
(Back to the FAIR Matrix).
Level 1 - FAIR leadership
Leadership Awareness of FAIR data starts. NB "Leadership" typically implies persons with responsibility and budget for financial and human resources. (Cf. FAIR roles dimension and lexicon).
Awareness of FAIR data starts, and there is some understanding of the (potential) value of FAIR data. The people with this awareness can become internal "thought leaders".
That can be a combination of "top-down" or "bottom-up" leadership.
In "Bottom-up leadership", intermediate-level executives are looking for "top-down" leaders and champions closer to the "C-suite”.
There can be situations where FAIR implementation is driven “top-down”. However, the strategy and means to implement may be vague as “C-suite” leaders may lack the specific knowledge to develop those. Leadership may have some buy-in to train the first people to make data FAIR.
(Back to the FAIR Matrix).
Level 1 - FAIR strategy
Awareness of FAIR data started. A vision and plan for FAIR data begins to emerge. Different strategies are possible and are embryonic.
Some leaders are starting to craft a plan and vision. Some organization leaders recognize the imperative for a strategic approach to FAIR data management. Although there is a visionary proposal for FAIR, the plans need more execution.
This realization has spurred a proactive stance towards implementing FAIR data principles and understanding their significance as a strategic asset. The organization has initiated a review of past and current efforts in this domain while also examining the FAIR practices employed by other entities, internal or external. To this point, some organizations see the value of strategic collaborative efforts with pre-competitive organizations, e.g. Pistoia Alliance, EU co-financed projects, GO FAIR implementation networks, Elixir or similar US-based initiatives.
This effort indicates a shift towards a more proactive and informed approach to FAIR data management to optimize data accessibility, interoperability, and reusability in the organization's operations.
Strategic considerations and approaches emerge towards more "closed" (internal creation of FAIR tools and process) or more "open-source" FAIR resources (e.g. Controlled Vocabularies, ontologies,...).
(Back to the FAIR Matrix).
Level 1 - FAIR roles
Those who work with data start to use FAIR Processes.
Key emerging roles: curator (e.g. corrects and complements data in systems, checks consistency, completeness, and accuracy); semantic expert (e.g. creates and corrects data dictionaries)
Support staff: e.g. data coordinator (ensures the following of embryonic processes ).
Data strategist (e.g., create "leadership" awareness, design architectures, process flows ).
Leadership hires external experts (consulting firms) to explain FAIR, train people internally or start making existing datasets "FAIR".
Organization members are sent to external workshops or training (e.g. GO FAIR Foundation) to lead the first internal projects.
(Back to the FAIR Matrix).
Level 1 - FAIR processes
In recognizing FAIR data's potential (business) value, there are processes to consider and determine which data we should make FAIR retrospectively. Efforts are underway to establish processes that provide guidelines for creating FAIR data, including the policy development for globally unique identifiers to enable the "F" principle.
We see a strategic integration approach with the pursuit of Initiatives to implement FAIR principles within specific functions or groups. As part of these efforts, processes may surface for identifying internal and external good practices related to FAIR data. This approach may imply a move toward more systematic and structured approaches to FAIR implementation, aligning with the organization's evolving understanding of the importance of standardized and accessible data.
The start of alignment discussions on metadata centralization, content management, and what constitutes reference data take place.
As the organization navigates this phase, the emphasis on training, guidelines, and targeted initiatives reflects a growing commitment to embedding FAIR principles in its data management practices.
There is an emergence of culture change processes alongside the ongoing ad hoc training process for FAIR roles.
Data domains will have a data discovery, profiling, and optimization phase or project.
(Back to the FAIR Matrix).
Level 1 - FAIR knowledge
Awareness of FAIR data starts with first workshops and training by external experts. People are sent to external workshops or training to understand FAIR better. Higher-level leadership are sent the First whitepapers, proposals or plans for buy-in.
There are pockets of FAIR knowledge (e.g. individuals) but no shared organizational knowledge. The organization becomes aware of what it has yet to know.
Community of practice: not yet established "internally", but some individuals connect with external communities of practice (e.g. academia, Public-Private Partnerships, Pistoia Alliance, ELIXIR).
(Back to the FAIR Matrix).
Level 1 - FAIR tools and infrastructures
At this stage, there is an understanding of the data challenges and a recognition of the importance of FAIR data. Still, the organization needs to learn how to make it happen. Initial plans for tooling and infrastructure for POCs start to take shape, along with organizational buy-in.
We see pragmatic progress in data and metadata creation and curation tools used to enhance existing IT practices (e.g. Excel templates, a drop-down list with limited selected items). There still needs to be complete FAIR tooling, but there is an inventory of the existing tooling and some determination of gaps along with the requirements for FAIR tooling.
There is some centralization of metadata, e.g. with content management systems, ELN systems, e.g. DKAN. There may be a deployment of other systems such as Github, Drupal, Sharepoints.
The organization starts thinking about and implementing "Findability" with Uniform Resource Identifier (URI) / Globally Unique, Persistent, Resolvable Identifier (GUPRI) services, published metadata catalogs, controlled vocabularies, Master Data Management (MDM) system (What needs to be "found"? Who needs to find what?). At least one policy on unique resource locators.
(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.
Reusability:
Tool(s) or infrastructure component(s) which contributes, enhances or enriches Reusability.