Meet Renaisi’s Data Manager

Renaisi’s Data Manager, Mahdy Alraie, explains what his role entails and shares examples of  how he helps clients’ understand their quantitative data and improve their data processes.

I have spent more than two years staring at hundreds of spreadsheets with the consultancy team in Renaisi, and yet I struggle to find the best words to precisely describe what I do. Maybe that’s because ‘consultancy’ is a broad concept, or because of the unique structure of Renaisi: offering consultancy services for non-profit organisations, and economic inclusion programmes for individuals.

It could also be that my role involves looking at the data of so many different things. Each project is different, but I generally conduct descriptive statistics.  In learning projects, I might review all the data collected by a given client to assess the quality, identify gaps, and make recommendations so the data could eventually answer their questions. Or in evaluation projects, it could be looking at existing data through a longer analysis cycle including:   

• Exploring and understanding: to ensure the data is actually what I think it is by sense checking and asking questions. 
• Consolidating different pieces of the data in one place and linking them together: it is not uncommon that data in non-profit organisations, and many other places, does not live in a perfectly consistent structure or system.
• Data cleaning and transformation: this is an essential stage to get the data in good shape before the actual analysis  
• Running the analysis!
• Discussing findings with the team and exploring conclusions alongside qualitative analysis.

 My favourite part is developing visualisations throughout the analysis stage. I think visualisation is the best way to start capturing patterns while looking at numbers simultaneously. My personal interest, when working with my clients is not just to provide them with final insights but to involve them in generating insights themselves by engaging them in the analysis process and trying to make data a bit more attractive to them.

The exciting part of my work is when I can develop interactive dashboards that are useful and aesthetically appealing. I am satisfied the most when my clients could use those dashboards in their day-to-day or strategic decision making.   

There are three crucial elements to my job!

1. Make sure quantitative analysis speaks to the qualitative through ongoing conversations with my colleagues and collaborative report writing.
2. Ensure that quantitative findings are not being overinterpreted or used to build conclusive results where they should be dealt with cautiously.
3. Problem-solving is an everyday skill I have to practice. I often start a project unsure if I will reach the desired outcome.  

Using data for evaluation and learning projects

Good Work Camden is an employability programme  we are commissioned to evaluate by the London Borough of Camden. I have been reviewing participants data, including demographics, activities, outcomes, and wellbeing. I triangulated data from different sources to understand if the measures used address all outcomes of the theory of change. Then I worked with my colleague Mylene Pacot to create a list of recommendations that could improve the data quality. 

The Good Work Camden project is an excellent example of utilising the knowledge I gained while evaluating our own employment programmes and from working in employment services. Before I joined the consultancy team I worked as an outreach officer on the RISE employment support programme for refugees. It is fascinating to see how much employment programmes have in common, including similar challenges in capturing the data we care about. Transforming learning across employment programmes is key for developing support for jobseekers, and I am lucky to be in Renaisi to work on that from both sides.

Community Connectors is a partnership between British Red Cross and Sport England to tackle loneliness through physical activity. I have been working on a Social Return on Investment (SROI) analysis to explore the programme’s efficiency, which includes looking at outcome data collected using UCLA loneliness measure. Next, I will run a counterfactual analysis to assess if the programme’s loneliness outcomes are statistically significant. 

I am working with our partner MyCake to look at the financial data of over 500 community businesses that received grants from Power to Change (Community Business Fund, Trade Up and Bright Ideas funds). This work is undoubtedly exciting because MyCake developed an interesting method of evaluating the growth of all grantee businesses over time regardless of when they received their grant. We will publish a paper about the method, as we think it can be helpful for other funders.   

Since June, I have also been delivering training sessions to my colleagues to enhance their quantitative skills. I enjoy those sessions where we listen to music in the practical exercise parts, and we share Excel memes in our Teams channel.   

Outside of work

 I recently watched Stateless, which is a TV mini-series about a refugee camp (or a detention centre) in Australia, where an Australian citizen ends up detained there somehow. I loved it for many reasons, but mainly that it shows what refugees go through while they are immigrating, what sacrifices they have to make, and what they escape! On the other hand, it shows the suffering of this Australian woman and leaves the audience determine there is no reasoning for any sort of comparison between miserable experiences of refugees and local citizens, and no matter what kind of suffering people go through, it is all entirely valid and must be addressed. 

I also finished reading Free Will – Sam Harris which is about a controversial way of dismissing the whole concept of free will as we intuitively think we have. I like the combination of philosophy and neuroscience it’s based on, and certainly the morality related implications on justice and compassion. It is a rather discomfort read, but I think the more challenging any reading is, the more meaningful it is. 

Finally, I have been convincing myself that I could train for a triathlon, and I have now done two sessions. I am not sure for how long I can keep the momentum, but I am just considering it!