Data_Analyst

Data Analyst is responsible for designing, developing, and maintaining dashboards and recurring reports that support business needs.

This persona collects, cleans, and integrates high-quality data into analytical models and data pipelines. The Data Analyst ensures data is well-documented, traceable, and accessible for a variety of stakeholders, enabling robust analysis and clear, actionable insights for strategic decision-making. Additionally, they collaborate with data engineers and business partners to continuously improve data processes, enhance data usability, and maximize the value of analytics within the organization. This persona locates appropriate data from various sources; ensures proper insertion of data into relevant workflows, pipelines, and models; ensures that workflow outputs are appropriate and made available to downstream consumers.

Upside

In a FAIR environment, Data Analysts rapidly discover and access trusted, well-documented data, reuse interoperable pipelines, reduce validation and rework, ensure traceability and audit readiness, and deliver analytics that scale across teams, support AI, and provide higher-value, reusable insights for decision-making.

Downside

In a non-FAIR environment, Data Analysts struggle to find and access data, lack trust in quality and provenance, face high integration effort across silos, work with poor documentation and traceability, and produce analytics that are hard to reuse, audit, or scale across the organisation.

A FAIR data environment transforms the work and added value of a Data Analyst persona, moving from reactive data wrangling to proactive insight generation. Data becomes easier to find, access, and trust through rich metadata, clear provenance, and shared semantic definitions. Interoperable data structures enable reusable pipelines and faster cross-domain analysis, while built-in traceability supports reproducibility and audit readiness. Analytical outputs themselves become reusable assets, or data products, supporting downstream teams and AI use cases. As a result, Data Analysts spend less time finding, formatting, validating and integrating data and more time delivering scalable, high-quality data products ultimately generating insights that directly support scientific, operational, and strategic decision-making.