7.1 The Responsible Data Maturity Model from Care

TABLE OF CONTENTS

• What is this about?
• Why will this resource be useful to you?
• Why is this resource particularly interesting?

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What is this about?

The text presented here was originally published under the title “A Responsible Data Maturity Model for Non-profits”.

In 2019, Linda Raftree worked with CARE to develop a Responsible Data Maturity Model (RDMM: Responsible Data Maturity Model) to help organisations evolve toward more responsible data management.

The CARE model identifies five levels of responsible data maturity:

• Unaware: when the organization has not thought about Responsible Data much at all.
• Ad-Hoc: when some staff or teams are raising the issue or doing something on their own, but there is no institutionalization of Responsible Data.
• Developing: when there is some awareness, but the organization is only beginning to put policy, guidelines, procedures and governance in place.
• Mastering: when the organization has its own house in order and is supporting its partners to do the same.
• Leading: when the organization is looked to as a Responsible Data leader amongst its peers, setting an example of good practice, and influencing the wider field. Ideally an organization would be close to ‘mastering’ before placing itself in the ‘leading’ stage.

Why will this resource be useful to you?

Below are a few examples of RDMM:

• As a diagnostic or reference and planning tool that allows organisations to see where they stand now, where they would like to be in 3 to 5 years and where they need to allocate more support/resources.
• As an assessment tool, if you are looking for a base/final line for organisational approaches to responsible data.
• In workshops as a participatory self-assessment tool to 1) help people see that the shift to a more responsible approach to data is gradual and 2) to identify what a possible ideal state might look like. The tool can be adapted to what an organisation considers its ideal future state.
• With an adapted context, a “persona” (i.e., putting yourself in the position of a particular group of people within the organisation) or a workflow approach that helps identify what the maturity of responsible data might look like for a particular project or program or for a particular role within a team or organisation. For example, for HQ versus a CO, for the board of directors versus front-line performers. It could also help organisations identify those parts of the responsible data for which the different positions or teams should be concerned and responsible.
• As an investment roadmap for HQ, Management or donors, to get an idea of the investment needed to reach responsible data maturity.
• Just about any other way you might think of The RDMM is released with a Creative Commons license that allows you to modify it and adapt it to your needs.

Why is this resource particularly interesting?

• It makes it possible to divide responsible data into disparate elements that can be assigned to different parts of an organisation or different team members.
• It provides indicators or “markers” related to the Responsible Data that can be integrated across an organisation.
• It enables teams and Management to see responsible data as a marathon, not a sprint , and will require multiple workflows to be processed over time with the involvement of different skill sets and different parts of the organisation Strategy, Operations and IT, Legal, Programs, M&E, Innovations, HR, Fundraising and Partnerships, etc.)
• It helps teams with limited resources to examine how to make progress step by step without feeling compelled to make responsible data their only goal.