Bridging Signals and Statistics: Reflections from the OECD MNO-MINDS Conference in Paris
When statisticians, telecom operators, and data scientists meet in Paris, you can be sure of one thing: every signal matters.
At the OECD Conference Center this September, the MNO-MINDS project brought together Europe’s leading statistical offices, Eurostat, the OECD, and private-sector innovators to explore one of the most complex challenges in modern data science — how to integrate mobile network operator (MNO) data into official statistics.
For Telcofy, this wasn’t just another event. It was a sign that our approach — a placement-first, code-driven, cost-efficient mobility data pipeline — is not just relevant but ahead of the curve.
The Core Question: Can MNO Data Become Official?​
The conference revolved around a simple yet profound idea:
How can anonymized, aggregated mobile signals — originally collected to deliver phone service — be turned into trusted official statistics?
Speakers such as Fabio Ricciato (Eurostat) and Li-Chun Zhang (Statistics Norway & Southampton University) highlighted both the promise and the pitfalls. While MNO data provides near-real-time insights into population movement, its path from raw signal to “official statistic” remains long and complicated.
As Ricciato summarized:
“Moving from experimental statistics to official production requires transparent methods, strong partnerships with MNOs, and sustainable pipelines that respect both privacy and quality standards.”
In other words — the technology is ready, but the methodology and governance are still catching up.
Signals Are Not Surveys​
Official statistics traditionally rely on survey data — structured, intentional, person-based, and traceable back to individuals (within strict privacy controls).
MNO data, by contrast, is reactive: generated as a by-product of network operations, not by human intent.
Each step from signal to statistic introduces complexity:
- Base station placement affects coverage and spatial accuracy.
- Aggregation and anonymization happen differently across MNOs.
- Algorithms and machine learning models interpret those signals — often with proprietary variations.
As discussed in the MNO vs. Official Statistics sessions, this makes full “deconstruction” of mobility data nearly impossible — but not useless. The challenge is to design reference pipelines that are transparent, harmonized, and cost-efficient.
This is exactly where Telcofy’s innovation lies.
Telcofy’s Take: Placement Matters​
At Telcofy, we believe accuracy starts long before the algorithm — it begins with how the signal is placed.
Our placement-optimized model improves spatial precision by incorporating land use, environment, and infrastructure data into the early stages of processing.
Combined with our code-first architecture, it compresses computation, harmonizes inputs across operators, and reduces processing costs by more than 60 % compared to traditional multi-vendor solutions.
This isn’t just technical efficiency — it’s an economic enabler.
As long as the cost of converting signals into insight remains high, the adoption of MNO data will remain slow. Lowering that barrier is the key to scaling the mobility data economy across both official statistics (B2G) and commercial use cases (B2B).
The Paradox of Paying for Data​
One of the most debated topics in Paris was the so-called “no willingness to pay for data” paradox.
National Statistical Institutes (NSIs) often claim they cannot pay for data — legally or philosophically — yet they spend millions each year on survey collection.
Some countries, like Spain’s INE, have found middle ground: compensating operators for data processing, not for the raw data itself. Eurostat even referred to this as a “reasonable model” for collaboration .
Telcofy’s position?
A sustainable data ecosystem needs fair compensation for processing, transparent governance, and open interfaces — not free data and closed models.
Public–private collaboration is the future, but only if both sides understand each other’s value drivers.
Why the Private Sector Must Stay in the Loop​
Several conference speakers — from OECD’s Geospatial Lab to Motion Analytica and Positium — emphasized that mobility analytics is technically demanding and rapidly evolving.
Trying to internalize the full processing chain within government agencies could slow innovation and inflate costs.
Private companies like Telcofy, operating under GDPR-compliant frameworks and open-source standards, can deliver secure, efficient, and scalable pipelines that public agencies can trust.
Keeping the processing private while ensuring results are public might be the best balance between innovation and accountability.
A New Data Economy Emerging​
Across discussions on tourism, migration, and transport planning, one theme kept returning: integration beats isolation.
The future of mobility analytics lies not in a single dataset, but in harmonized, multi-operator, multi-source ecosystems.
Telcofy’s approach — building a neutral, multi-MNO data pipeline aligned with Eurostat’s 2024/3018 regulation — positions us to deliver exactly that.
By treating telecom data as infrastructure, not inventory, we enable shared value creation for National Statistics Institutes, city planners, and commercial analytics platforms alike.
Closing Reflections: From Paris to Practice​
Walking out of the OECD conference hall in Paris, one message resonated clearly:
The signal is strong — but the connection between technology, methodology, and governance still needs work.
That’s precisely the intersection where Telcofy operates: bridging telecom data, statistical rigor, and real-world usability through efficient, transparent pipelines.
As Europe prepares for its next wave of Trusted Smart Statistics, we're proud to be among the few shaping not just the discussion — but the infrastructure to make it real.
Keywords: Mobile Network Operator Data · Official Statistics · Eurostat · Mobility Analytics · Trusted Smart Statistics · Cost-Efficient Data Pipeline · Human Mobility Science · Telcofy