C4D: Manage data

What is it?

Good data management means that systems are in place for consistent and ethical collection, recording, storage, security, backing up, cleaning, and modifying, and ownership of data. This is part of data quality assurance (DQA). Data quality assurance (DQA) should be built into each step in the data cycle − data collection, aggregation and reporting, analysis, use, dissemination and feedback and longer-term ownership and retention. An important part of this is 'data cleaning', which refers to checking for inaccurate or missing data. 

General information

The 'Manage data' page of the Rainbow Framework provides generalist information, methods and resources about data management. This page is recommended background reading before considering methods to apply to C4D. 

Applying the C4D principles

Holistic

It is important to consider that taking a holistic approach to data collection means that the data is often not pre-standardised (e.g. following a standardised interview protocol), but is, rather, more responsive and open to being shaped by the context.

Complex

Where there are multiple project partners, it is important to pay attention to data quality across organisations, data security when sharing data, and compatibility of IT systems. To support adaptive implementation of C4D it is useful to have data management systems that can quickly produce different types of reports in response to changing information needs. 

Participatory

In a participatory approach, it is important to think about who owns the data and therefore has responsibility for data management. 

Learning-based

Related to the participatory approach, it is important to consider whether stakeholders may need capacity-building support to be able to effectively manage data. 

Realistic

Good data management practices are important for keeping analysis processes manageable and feasible.

Accountable

C4D emphasises good data management and ownership processes that are respectful, ethical, and responsible. It is important to agree to policies and processes that prevent or minimise harm (especially for vulnerable groups). These discussions should take place before, during and after the data collection.

Resources

  • The following resources provide useful guidance on managing data:

  • Ethnographic Action Research Toolbox

    The Ethnographic Action Research Toolbox has a whole section devoted to dealing with data, including documenting data, organising and labelling data, and developing themes and managing codes.   

    Equal access participatory monitoring and evaluation toolkit

    Page 63 of the Community Researcher Manual for Equal Access (a C4D organisation) covers good data management processes

    The resources above are particularly useful in the context of the C4D Evaluation Framework for the following reasons:

    • Holistic: open ended, unstructured data tends to be messier and more difficult to manage than structured (i.e. survey) data.
    • Realistic:  both tools were developed in the context of C4D NGOs, and so are inherently aimed towards being as pragmatic as possible. 
  • Oxfam Responsible Program Data Policy

    This document outlines a rights-based policy for data management, based on the following rights: the right to be counted and heard; the right to dignity and respect; the right to make an informed decision; the right to privacy; and the right to not be put at risk. This policy is consistent with the C4D Evaluation Framework in the following ways:

    Critical: The policy recognises that data and ownership of data entails a position of power and responsibility, and the importance of considering marginalised voices in this process.   Accountable: The policy emphasises the ethical dimensions of data management processes and responsibilities.

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