Metroline Step: Design solution plan

status:status: in development

Short description

Building on the pre-FAIR assessment, this step defines a structured approach to addressing identified gaps in FAIR compliance. It transforms assessment insights into an actionable solution plan, ensuring that technical, organisational, and procedural aspects are considered. It helps your team allocate resources efficiently and prepare for upcoming challenges. The outcome is a clear roadmap that enhances data management and prepares for improved FAIRification.

Why is this step important 

The pre-FAIR assessment identifies key gaps and challenges in FAIR compliance, such as incomplete metadata, lack of a data storage strategy, or missing documentation practices. If left unaddressed, these issues can hinder data quality, interoperability, and reuse. This step ensures that assessment insights are systematically translated into a structured plan, preventing fragmented and ineffective FAIRification efforts. A well-defined solution plan helps to:

• provide a structured approach to tackling complex data issues and aligning stakeholders on a shared strategy;

• optimise the use of resources and define success criteria to track progress;

• enhance data quality, ensuring better insights, improved compliance with FAIR principles, and greater efficiency in research processes.

How to 

Step 1 - Identify and prioritise key requirements

Using the pre-FAIR assessment findings, identify and rank key issues based on urgency, impact, and feasibility. Prioritisation ensures that critical gaps, such as incomplete metadata, lack of persistent identifiers, or missing documentation, are addressed first.

Example: If metadata incompleteness is blocking data reuse, improving metadata standards should take precedence over less critical issues.

Step 2 - Refine your FAIRification objectives

For each prioritised issue, define SMART objectives (Specific, Measurable, Achievable, Relevant, Time-bound). These objectives will guide implementation and measure progress. Ensure that these SMART objectives align with your FAIRification objectives (as defined in Metroline Step: Define FAIRification objectives).

Tip: The FAIRification Process framework emphasises defining clear goals before implementation. Taking the time to explicitly set FAIRification goals at this stage helps ensure a structured and effective solution plan.

Example of refined objective: By [date], update metadata for 90% of datasets to comply with [standard], ensuring all required fields are completed.

Example of alignment with FAIRification objective: The SMART goal from the example above is aligned with the following FAIRification objective: “Metadata must be mapped to the DCAT-AP core schema and provided via a FAIR Data Point”.

Step 3 - Assess feasibility and identify constraints

Evaluate the current infrastructure, policies, and governance environment. Identify constraints such as legal barriers, technical limitations, or resource availability. Develop mitigation strategies to address them. You might have to revisit Step 2 (Refine your FAIRification objectives) depending on the constraints found in this step.

Example: If data governance policies prevent external repository use, consider alternative institutional solutions.

Step 4 - Select appropriate tools and methodologies

Choose tools, frameworks, and methodologies that best address identified gaps. Resources such as FAIRsharing, FAIR Cookbook and FAIR Implementation Profiles (FIPs) ensure alignment with best practices. Discussing your plan with FAIR experts who work in similar projects can also help in selecting the most suitable resources.

Tip: In many cases, existing tools and methodologies can be reused or adapted. The FAIRification process framework (FAIR Cookbook) provides structured guidance on selecting and applying tools at different stages of FAIRification. It offers practical recipes that can help ensure your chosen methodologies align with FAIR best practices. For a structured approach to implementing FAIR principles, explore the How to GO FAIR guide, which provides methodologies and best practices for FAIRification.

Example: Persistent identifiers ensure your work remains findable, accessible, and citable over time, even if locations change. The FAIR Cookbook provides guidance on how to implement persistent identifiers and also lists providers of such services.

Step 5 - Engage stakeholders and align objectives

Involve key stakeholders from the pre-FAIR assessment phase, including data stewards, domain experts, and IT teams. Ensure alignment between technical and organisational priorities to create a feasible solution plan. For additional guidance on coordinating data management efforts across teams, see the Data Management Coordination page from RDMKit.

Tip: Regular check-ins help maintain engagement and prevent misalignment.

Step 6 - Develop a structured FAIRification roadmap

Translate the objectives from Step 2 into an implementation roadmap outlining key actions, responsibilities, timelines, and resources. Define key performance indicators (KPIs) to track progress and enable adjustments.

An iterative, agile approach helps structure the roadmap, allowing flexibility and continuous refinement. The FAIRification process framework (FAIR Cookbook) emphasises breaking implementation into manageable phases to ensure steady progress while adapting to evolving requirements.

For example, when making data FAIR, datasets can be split into smaller sets, improving step by step. Much like an agile kitchen (see Types of Project Management Methodologies), where chefs adapt dishes based on available ingredients, refine flavours as they cook, and collaborate to enhance each other’s work, FAIRification benefits from incremental improvements, teamwork, and adaptability. By iterating on smaller datasets and building on previous improvements, each cycle moves closer to full FAIR implementation.

Step 7 - Conduct risk assessment and refine the plan

Identify potential risks (e.g., delays, lack of resources) and define mitigation strategies. Review the solution plan with stakeholders and refine it based on feedback.

Example: Risk: Limited personnel availability. Mitigation: Cross-train team members to ensure continuity.

Expertise requirements for this step 

Designing a solution plan is typically a collaborative effort by a range of experts, as described in Metroline Step: Build the Team.

• FAIR experts. Provide guidance on best practices, standards, and methodologies.

• Domain experts. Define data semantics, quality standards, and use cases.

• Data stewards. Ensure data and metadata comply with FAIR principles.

• IT specialists. Support infrastructure needs, interoperability, and tool integration.

• Project managers. Oversee planning, stakeholder coordination, and implementation tracking.

Practical examples from the community 

This section should show the step applied in a real project. Links to demonstrator projects. 

Training

• FAIRification Tools and Templates – ELIXIR-UK Fellowship

• FAIRification Workflow – FAIR Toolkit

• FAIR Implementation Profile

• Tutorial: Insights in FAIRification planning

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