Data frames faciliate the systematic collection and analysis of data to assess the progress and effectiveness of programs, projects, or interventions. They provide a structured frame to store diverse types of information that comes form various sources.

Furthermore, data frames seamlessly integrate with your log frame and indicators, enabling the efficient tracking of goal achievements. This integration ensures a comprehensive and interconnected approach to M&E processes, allowing for a more holistic assessment of programmatic success.

Each row (1) in the data frame represents a unique case or observation, while columns (2) can represent different variables or attributes.
In the data frame you can perform operations like searching, filtering or sorting (3) to always work with the data you really need.
Via the action (4) you can manage your data and edit each entry directly in Reframe Monitor.

Data frames are the basis to create graphs and charts for better understanding and presentation of your results. Data frames facilitate the comparison of data across different time periods, geographical locations, or subgroups. This is crucial in M&E to identify patterns, trends, and variations in program outcomes.

Data model

To utilise the full potential of data frames, they can be connected to create a data model.

Here you see a graphical representation of the data model employed in an education project. The illustration depicts the interconnection of the "Schools" data frame, responsible for collectiong information about schools (such as name, location, type, education stage, etc.), with various other data frames. When adding data into the "School enrolments" data frame, for instance, a reference is made to the "School" data frame, facilitating the selection of a specific school.

Using a data model has the following benefits:

Structured Organization: A data model establishes a structured framework for organizing diverse information, defining how entities are related and their attributes they possess. This enhances data organization for effective monitoring and evaluation.
Data Governance and Standardization: Data models contribute to effective data governance by establishing standards and guidelines for data management. They help enforce naming conventions, data quality standards, and access controls.
Data Integrity: Data models include constraints and rules that help maintain data integrity. By specifying relationships between data frames and defining their properties, data models prevent inconsistencies, errors, and redundancies in the stored information.
Efficient Reporting: Data models also optimize the efficiency of the reporting processes as they enable users to retrieve and analyze data more effectively.