Mohamed Kotaish on e-waste, data quality in emerging markets, and why the roadmap matters as much as the numbers.
This is the second in a series we’re calling 10 Years. 10 Lessons Nobody Warned Us About — where BFS team members share what they actually learned from their hardest projects.
- E-waste is one of the fastest-growing waste streams globally, but reliable data on it in developing countries is scarce, outdated, and rarely updated.
- BFS Technical Consultant Mohamed Kotaish has worked on e-waste projects in Egypt and built data systems for his master’s thesis — this is what he learned.
- On the MENA 130 project, the most recent e-waste data available for Egypt was from 2021. The answer was not to find better databases — it was to go into the field and interview the right public and private sector stakeholders.
- There is no one-size-fits-all solution for e-waste management. Every sub-stream has its own best practices, and the sector is still defining them.
- The single most important takeaway: have a roadmap for your data in mind before you start collecting it.
| $80.78Bn
Global e-waste management market size in 2025 — growing to $149.96Bn by 2030 |
5 Years
Age of the most recent e-waste data available for Egypt at time of MENA 130 project |
13.1% CAGR of e-waste management market 2025–2030 — fastest-growing major waste stream globally |
Mohamed, you work at the intersection of data and technical consultancy. When you think about data in your work, what actually comes to mind first?
Several things, honestly. The first is reliability — where is the data coming from? That is your starting point. If the data is weak at the source, everything downstream is compromised.
The second is organisation. Through a project you can end up with a large data pool quite quickly. If you don’t have a structure in mind from the start, you will lose things. Critical things. And the problem is that data is not one thing — it can be quantitative, qualitative, regulatory, process-related, specification-based. Unless you know how you’re going to handle it, you won’t make sense of it.
Third, validation. It’s an iterative process. When you’re drawing from multiple sources, you have to keep checking: do these pieces connect? Are there gaps? Do the numbers hold together? Because discontinuities in data — especially across sources — can completely change your analysis.
Finally, presentation. This is where your work communicates. You’ve done the research, you’ve organised it — now you need to bridge that to the audience. The way you visualise data is not a design question. It is a communication question.
You mentioned structure and organisation early. Can you walk us through a specific project where that was tested?
The clearest example for me was working on the MENA 130 project, focused on e-waste in Egypt. The challenge from the outset was that we simply did not have much recent data to work with. The most current figures we could find were from 2021. That’s five years old in a sector that is still forming.
So you have a choice. You either present that data with full transparency — “this is from 2021, here is what the projection literature says, here is the uncertainty” — or you go and find better data yourself. The honest answer is that both are needed.
For a market like Egypt, secondary data has real limits. The better solution is fieldwork. That means identifying the right people to interview, doing the stakeholder mapping first, and then extracting current information directly. What I found is that interviews do two things at once: they give you the current picture, and they guide you to other sources you hadn’t thought to look at. In markets where data infrastructure is still developing, that guidance from people in the field is sometimes the only way to understand where the numbers actually live.
You also talked about an AI-assisted visualisation tool from your master’s thesis. What happened there?
In my thesis I was handling a very large data set and we used a tool — a base-44 platform — to help visualise it. It was useful. But there is something important that I think gets skipped in conversations about these tools: you have to understand their limitations, ideally before you’re deep into the analysis.
Because if you realise at the end that the tool has distorted something or introduced a bias in how the data is displayed, you have a problem. The credibility and authenticity of your analysis is at risk. The better practice is to test the tool alongside the analysis, while you still have time to adjust. You can still visualise well without compromising the data — but you have to stay in control of that process.
Where is the data problem worst right now? Which markets or waste streams are most affected?
E-waste in developing countries, without question. And I don’t think that’s a niche observation — it’s a fairly broad consensus in the sector.
For other waste streams — municipal waste, plastics, packaging — there has been decades of work. Technologies are established. Processes are well documented. But e-waste is a much more recently developing field. The management technologies are still being researched. And within e-waste, there are multiple distinct sub-streams, each of which has different treatment requirements, different material compositions, different best practices. There is no single solution that fits all of them.
That makes data even more critical. If you don’t have granular, current data on what you’re actually dealing with — which components, which volumes, which hazardous substances — you cannot design the right intervention. And in developing countries, that starting-point data is often simply not there.
One last question. If someone reads this and takes away a single thing — what should it be?
Have a roadmap for your data.
Before you collect anything, think through: what do I actually need? How am I going to organise it? How will I process it? And — just as important — how will I present it, because that is where the work finally becomes useful to someone else.
A lot of research effort gets lost not because the data was bad, but because there was no plan for what to do with it. The roadmap is what makes the data work.
“Data is the starting point and the cornerstone for environmentally sound waste management. But only if you know where it is going.”
— Mohamed Kotaish, BFS Technical Consultant
About the series:
BlackForest Solutions turned 10 in March 2026. Instead of a celebration, we asked 10 team members to share the hardest lesson from their hardest project. This is what a decade of doing the work actually looks like.
About Mohamed Kotaish:
Mohamed Kotaish is a Technical Consultant at BlackForest Solutions GmbH, specialising in technical consultancy with a focus on e-waste and data-driven waste management. He has contributed to projects in the MENA region, including the Egypt-focused MENA 130 project, and completed a master’s thesis built around large-scale environmental data systems. His work sits at the intersection of regulatory context, stakeholder analysis, and practical data methodology.
Project at a Glance
| MENA 130 — Egypt E-Waste Assessment | Project Snapshot |
| Region | Egypt / MENA |
| Waste Stream | E-Waste |
| Key Challenge | Most recent available data was from 2021 (5 years old at time of project) |
| Approach | Secondary data review + stakeholder interviews to establish current baseline |
| Co-lead | BFS Technical Consultant (with Angelica) |
| Scope | E-waste landscape mapping, data sourcing, gap analysis |
| Insight | Field interviews surface both recent data and guidance on where to find more |
E-waste — Electronic waste; discarded electrical or electronic devices. Includes a wide range of sub-streams with distinct material compositions and treatment requirements.
ESM — Environmentally Sound Management; the Basel Convention standard for handling hazardous and other wastes in a way that protects human health and the environment.
PRO — Producer Responsibility Organisation; a collective body set up by producers to manage end-of-life waste obligations.
Frequently Asked Questions: E-Waste Data Quality and Management in Emerging Markets
Q1. Why is e-waste data so unreliable in developing and emerging market countries?
E-waste is still a forming sector — management technologies are being researched, informal recycling operates outside any reporting system, and what official data exists is typically years out of date by the time analysts can use it.
Q2. What are the different sub-streams of e-waste and why does this matter for data collection?
E-waste spans consumer electronics, IT equipment, appliances, medical devices, and industrial machinery — each with different materials, hazardous content, and treatment requirements. A single total volume figure is never enough; you need sub-stream level data to design the right intervention.
Q3. What is Environmentally Sound Management (ESM) of e-waste?
ESM is the Basel Convention standard for managing hazardous waste — including e-waste — in a way that protects both human health and the environment. In practice, it means ensuring collection, dismantling, and recovery processes keep toxic substances like lead and mercury out of workers and communities.
Q4. How do you collect reliable e-waste data when secondary sources are outdated?
The answer is structured fieldwork — map your stakeholders first, then conduct targeted interviews to extract current ground-level information. Interviews do double duty: they give you the current picture and point you toward sources no database would have led you to.
Q5. What are the four properties of good data in a waste management project?
Reliability (source quality), organisation (structure before you collect), presentation (a communication decision, not a design one), and validation (cross-source consistency checks throughout). Skipping any one of the four creates risk that compounds at every stage downstream.
Q6. What is a data roadmap and why is it important before starting a waste management project?
A data roadmap defines what you need, how you will organise it, how you will process it, and how you will present it — before collection begins, not after. Most research effort is lost not because the data was bad, but because nobody planned what to do with it.
Q7. What did BFS’s MENA 130 project in Egypt reveal about e-waste data gaps in the region?
The best available e-waste data for Egypt at the time of the project was from 2021 — five years old in a sector still forming its frameworks. The solution was combining transparently caveated secondary data with primary fieldwork, confirming that in MENA markets, fieldwork is not a supplement to the methodology — it is the methodology.