Level 0 “Life is unFAIR”
- Giovanni Nisato
Level | Nickname | Marketplace metaphor | Key Features | Picture |
0 | “Life is unFAIR” | “Junkyard” | Lack of FAIR awareness, possibly acquiring awareness. |  |
Table - Level 0 summary
FAIR Maturity Matrix: maturity levels (columns) | Level 0: “life is unFAIR” summary
Level 0 - Capabilities
No FAIR capabilities are present. The organization is application-centric. FAIR principles may begin to get in focus.
Level 0 - Business value
Application Centric Business Value, classically, we see desktop integration of disparate data systems. Data is a “tactical asset”.
Level 0 - Questions to ask
How is data currently managed within the organization, and what challenges exist in accessing and integrating data across departments? Are there data “silos”?
Can users other than those who created a data set find it and interpret it?
Are there organizational guidelines on data formats or on how to document metadata?
Is there a register of data licenses?
Is there an Information Security Policy?
What level of understanding and engagement with FAIR data principles exists among organizational leadership?
Does the organization have a formal strategy for adopting FAIR data principles, and if not, what are the perceived barriers to its development?
What level of awareness and understanding of FAIR data principles exists among employees within the organization?
What steps are being taken to ensure that data adheres to FAIR principles, particularly in terms of findability and access policies?
Level 0 - FAIR data
Data is often in silos, making it inaccessible or challenging to integrate across departments. There's a mishmash of data types, formats, and file systems akin to a "Wild West" scenario, leading to inconsistency and inefficiency. Data practices' adherence needs to be improved, making it hard to establish standardized processes. Additionally, data agreements tend to be ad-hoc, with individual or small teams making decisions about data usage without a broader organizational strategy. Data cataloguing appears in situ. Raw data follows un-conformed formats and is external to a data lake. It requires expert technical and subject matter knowledge to access and use.
(Back to the FAIR Matrix).
Level 0 - FAIR leadership
There needs to be champions or an understanding of FAIR data in the organizational leadership.
There needs to be more leadership engagement, and co-workers and processes need more support for adhering to FAIR data principles. Furthermore, the organization lacks a conceptual grasp of "FAIR" and displays an absence of ownership, interest, and overall awareness regarding data management. This disregard extends to the organization's strategic focus, as data and FAIR principles are not considered priorities. These factors may combine to tarnish the organization's reputation regarding adherence to FAIR practices in data management.
(Back to the FAIR Matrix).
Level 0 - FAIR strategy
No FAIR strategy exists. There is no awareness and understanding of FAIR data, so it needs to be in the company's strategy or any department. Data management may be simplistic but also, in other ways, quite advanced. The prevailing approach to FAIR data management is reactive, with FAIR principles, at best, haphazardly applied in response to external pressures and specific needs. Notably, there needs to be a formal strategy delineating adopting FAIR principles and their associated values within the organization. This lack of strategy reflects an overarching need for more ambition and recognition of the significance of the FAIR principles. As a result, there needs to be more intent or a structured pathway for implementing FAIR data principles across various organizational levels or divisions. This result underscores the organization's need to acknowledge and prioritise FAIR principles in data management.
(Back to the FAIR Matrix).
Level 0 - FAIR roles
There needs to be awareness and understanding of FAIR data. Competency is absent in the company.
There needs to be designated roles or recognition of the need for roles dedicated to FAIR data management. Resistance and headwinds are prevalent, as people are hesitant to allocate resources or engage in data sharing, often preferring to hoard data under the guise of security by secrecy. This resistance and potential hostility towards the FAIR approach hinder its adoption. Furthermore, FAIR data principles must be more balanced in favour of other data-related aspects, undermining their integration into the organization's data management practices. These challenges reflect the existence of cultural and organizational barriers to the adoption of FAIR principles.
(Back to the FAIR Matrix).
Level 0 - FAIR processes
No FAIR process exists company-wide, and no explicit data management processes exist. If (implicit) data management processes are in place, they may divert effort and resources from implementing FAIR data principles, albeit not intentionally.
(Back to the FAIR Matrix).
Level 0 - FAIR knowledge
There is no FAIR-related knowledge in the organization. There needs to be awareness and understanding of FAIR data; leadership must have champions and understanding.
(Back to the FAIR Matrix).
Level 0 - FAIR tools and infrastructures
The organization presents a significant deficit in tools and infrastructure for implementing FAIR data management principles. Notably, the organization needs specialised tools, well-defined requirements, or a structured roadmap to ensure data adheres to the FAIR principles of Findability. Free field forms, e.g. in Excel documents, are frequent. This deficiency results in an unstructured and fragmented approach to data capture, undermining the organization's capacity to manage its data effectively. A pronounced lack of awareness and understanding of FAIR-compliant tools leads to a disjointed, ad-hoc problem-solving methodology that often contradicts FAIR principles. In the worst-case scenario, specific tools actively work against FAIR principles, exacerbating the challenge, especially when mandated by organizational processes, potentially coupled with geographical and web domain restrictions, further hindering the establishment of a robust FAIR data management framework. The organization must include an Inventory of the licences and access policy related to data sets.
(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.