Jakub JirkaIn today's data-driven world, organizations face significant challenges when it comes to implementing effective governance strategies, particularly in the realm of data management. One of the most daunting tasks for institutions is taking the first step toward comprehensive governance. A crucial initial move in this direction is establishing unified access to various governance disciplines, with Data Management Planning (DMP) being a key component.
Data Management Planning, like other governance disciplines, benefits greatly from a united and systematic approach. This methodology not only aids the institution in maintaining consistency and control over its data assets but also provides scientists and researchers with a structured framework for managing their data throughout its lifecycle.
DMPs enhance research by ensuring data is organized, accessible, and reusable. They foster collaboration, support compliance with funding requirements, and promote FAIR principles, increasing the impact and discoverability of research outputs. Additionally, DMPs provide a better overview of data assets and help reduce costs by minimizing redundancies and optimizing resource use.
By prioritizing unified data management planning, organizations can transform their data from a potential liability into a strategic asset, driving growth, efficiency, and competitive advantage in an increasingly data-centric business environment.
To address these challenges, a structured approach to Data Management Planning can be divided into three key parts: Collect, Govern, and Improve. The first step, Collect, focuses on gathering the essential information needed to establish a solid foundation for effective and unified data management across the institution.
Before you begin collecting data, start by identifying the key criteria that are truly relevant to your processes and build your DMP around them. Focus on gathering information that will be actionable and useful, avoiding unnecessary burdens on researchers and data stewards by asking for details you won’t use. While it’s tempting to collect as much information as possible, starting small and targeted can often lead to a greater impact, creating a more efficient and effective foundation for data management.
Once you’ve identified the initial set of information you want to collect in your DMPs, the next step is efficiently gathering it from key stakeholders. Instead of relying on outdated Word documents, consider using modern tools designed for this purpose. With FAIR Wizard, you can use the Knowledge Model, as the foundation for your information-gathering process. There are two main approaches: start with an existing Knowledge Model and customize it to create an institutional version or build your own from scratch. For most organizations, starting with an existing model is recommended—it’s quicker, easier, and ensures alignment with best practices while still allowing for customization based on specific needs.
With your information collection set up, it’s time to move to the Govern phase. This stage involves defining the phases of your projects and representing them in your Knowledge Models and project workflows. It’s essential to identify what information is critical or desirable at each stage of the project and determine how to monitor these elements effectively. For example, if a project requires specialized laboratory equipment, this should be outlined during the planning phase so the institution can ensure everything is ready when needed. Governance also includes regularly reviewing and checking the content of your DMPs, as this is key to refining and improving your processes over time.
In FAIR Wizard, the Analytics App is designed to help you govern your projects efficiently. You can create customized dashboard overviews to monitor all project phases and track KPIs based on data from your DMPs. Dashboards allow you to see which phase a project is in, the percentage of completed questions, and any metrics defined in your Project’s Knowledge Model. For deeper insights, you can use the Knowledge Model Insights feature, which provides a detailed overview of how users answer questions across projects based on a specific Knowledge Model. This tool helps identify common responses, highlight unanswered questions, and pinpoint areas for improvement—insights that are invaluable not only during governance but also in the final phase of improvement.
Last but not least is Improve. There are countless ways to enhance your DMPs, and this phase is all about refining both the information you collect and the processes around it. Start by focusing on the data you truly need and ensuring it’s effectively integrated into your internal workflows. Next, look for ways to make data collection more efficient—optimize the structure of your Knowledge Models, introduce integrations to help users select from available options in your systems, prefill information from other institutional tools, and generate tailored outputs from your DMPs that integrate seamlessly with your existing solutions. The possibilities for improvement are endless, and FAIR Wizard is designed to fully support these efforts.
However, one crucial step in improving your DMPs is gathering feedback. Your users—researchers, data stewards, and other stakeholders—have firsthand experience with the process and can provide valuable insights into what works well and what needs adjustment. Engage with them to understand their challenges, guide them toward shared goals, and educate them on why certain requirements are necessary. When users see the value their additional efforts bring to both their work and institutional goals, they are more likely to embrace the process. At FAIR Wizard, our mission is to make this journey easier for everyone involved.
