One of the first tasks in building a strong PMEL system involves thoroughly reviewing the data you gather – and that includes how it’s collected, stored, and analysed. This can help you and your team work with your programme teams to find the best way to meet your PMEL needs and keep things light for programmes.

Step One: A Data Audit

So the first thing I do with clients is a joint data audit, ideally with PMEL and programme teams, joined by grants officers if in a foundation, and the communications/fundraising team if in an implementing organisation.

We reflect on the data you being collected across the organisation, at all levels. This could be from:

• partners and local groups
• government partners when you are scanning your context
• donor/grantee information
• surveys
• case studies and stories of change
• project or grant reports
• emails and memos – both internally and externally
• etc

It’s quite likely that you already have all of this information, so you can see how there’s a wealth of data already being gathered, but possibly not used in the best way.

Step Two: Data Usage

From this list then, we filter out what’s being used and how – and for whom! For example, we may discover that to feed into donor reports, we are gathering a lot more data than we need, and aren’t doing enough to learn from our context. Filtering out in this way helps us find these gaps, and bottlenecks. We need to question each step of this process and understand its purpose and need.

I prefer doing this in a visual way so we can map out the process, and thereby evaluate whether it’s working well or not. We would interrogate whether the data we’re collecting helps us to best learn from our partners, about our impact, and therefore evaluate our roles in our context.

In an ideal scenario, the data you gather for PMEL is also used by your comms and fundraising teams, so that’s why they’re involved in the management process.

Step Three: Data Reflections

We all then reflect together about how the data helps us answer questions about our outcomes and goals, using our results frameworks. This helps us better understand the data gaps, and what we should therefore be collecting. Many of my clients discover early on that they’re collecting a lot of data, but aren’t using it well enough – or it’s sitting stationery in a shared drive somewhere.

I’ve found that there are so many incredible sets of qualitative data being gathered for case studies for example, but then only being applied to stories of change or in annual reports.

Case studies are an incredible tool to help us learn more about our work and relationship with our communities, whether we’re helping them to work towards change, and what specifically we’ve contributed to. But so often, a story is gathered solely to go into a text box in a donor report, and not used for better internal learning and reflection.

Identifying these gaps, and therefore areas of improvement, is my favourite step!

Step Four: Making the Most of Data

So the final step is to correct these gaps, or at the very least to deeply understand the gaps and how to fill them. This can help you make the data – and possibly also PMEL processes – more efficient. Imagine if the process could help your programme teams see PMEL as less of a burden and more as a fun activity!

Your data should help you clearly understand your work at a glance – an activity plan for example. What did we do this month, and how did it go? What resources did we engage for this activity, what were the lessons learned and best practices?

Ideally it would also help you better udnersand the community and your partners’ needs, to help you figure out if you’re best emeting them and adapt accordingly.

How a Data Management System can Help

During sessions with your programme team, you can look at the data collected and line it up with your results framework to clearly know about your role and contributions to change, the outcomes achieved, and use a pathway logic to understand how your day-to-day is helping make change.

Through conversations with partners and within your organisation, you can evaluate how community-led, representative, or participatory you really are being, especially if you trust that your feedback and response mechanisms are strong enough for you to receive honest response.

You may be able to better understand where each partner in your project can best make the change – you may work with local advocates for example – which can help you find the best strategies to work together to make the most of your resources.

In its best form, data is of course a source of evidence, which can be used to triangulate/validate for donor and other periodic reviews.

By working with your communications team, some data can even be ‘translated’ a little and turned into a great media campaign, or a fundraising tool if you can demonstrate how your strategies are helping meet needs, for example.

It can help answer questions about what’s working and what isn’t, which can help you feed into your programme or organisation’s strategy during annual reviews.

How does all of that sound? Exciting, right? I love supporting organisations with this portion of their framework, and thinking through how their data can better meet programme needs. Reach out with any questions you have, and I’d be happy to walk you through the process. ‍