Founded in 2018 and headquartered in Berlin, Datarade is a B2B technology company focused on data commerce.
The company was established by Thani Shamsi, Richard Hoffmann, and Florian Rösler.
The company explicitly focuses on enabling:
• AI model training and fine-tuning
• Contextual data enrichment
• Real-time decision intelligence
At the heart of the company’s offering is its flagship product, the Datarade Marketplace.
It serves a global user base, with tens of thousands of monthly users and customers across more than 200 countries.
This platform allows users to:
• Search and compare datasets from thousands of providers
• Preview sample data before purchase
• Access pricing and benchmarking information
• Explore data across hundreds of categories (e.g., financial, geospatial, consumer, AI training data)
The marketplace aggregates data from over 1,000–2,500 providers and offers access to thousands of data products, making it one of the largest curated ecosystems for external data sourcing.
As well as DaaS (Data as a Service) Datarade also provides a SaaS solution called Data Commerce Cloud (DCC), designed for data providers and it currently has over 500+ data providers on its platform.
This platform enables organizations to:
• Monetize proprietary datasets
• Distribute data across multiple channels
• Manage pricing, contracts, and customer relationships
Here are real examples of mobility & geospatial data providers listed on Datarade’s marketplace, specifically from their mobility data category and location intelligence ecosystem.
These are the types of companies Datarade aggregates, vets, and makes searchable for enterprise buyers.
Key Mobility & Location Intelligence Providers on Datarade
Quadrant
Quadrant is one of the largest mobility data providers featured on Datarade.
• Provides global mobile location data panels
• Claims coverage of 1B+ mobile devices
• Produces 500B+ location events per month
Focus: location analytics
Veraset
Veraset specializes in large-scale population movement datasets.
• Provides raw and processed mobility data
• Derived from opted-in mobile device signals
Intuizi
Intuizi is a real-time mobility intelligence platform.
Converts smartphone signals into behavioral insights
Covers hundreds of millions of devices globally
Irys
Irys focuses on consumer movement intelligence and foot traffic analytics.
• Processes billions of daily events
• Tracks consumer behaviour across physical locations
• Includes historical + real-time mobility datasets
Factori
Factori provides multi-layered mobility and consumer data intelligence.
• Combines mobility data with identity and behavioral signals
• Tracks brands and consumer movement patterns
• Offers “360-degree customer views”
Unacast
Unacast is one of the best-known location intelligence providers on Datarade.
• Offers global mobile location datasets
• Provides historical + real-time movement data
• Strong presence in US and European markets
Echo Analytics
Echo Analytics specializes in large-scale mobility and POI datasets.
• Combines location signals with points-of-interest intelligence
• Focuses on retail and commercial movement behaviour
•Strong in geospatial enrichment
GapMaps
GapMaps provides foot traffic and consumer visitation insights.
• Focus on consumer mobility and retail analytics
• Helps businesses understand physical customer behaviour
• Strong marketing and operations use cases
Michelin Mobility Intelligence
A surprising but important player, this arm of Michelin provides logistics-grade mobility data.
• Road traffic and mobility analytics
• Fleet and logistics optimization data
DRAKO
DRAKO provides digital out-of-home (DOOH) and mobility insights.
• Tracks movement of large populations
• Helps optimize advertising placement in physical environments
• Focus on media + mobility intersection
What These Providers Have in Common
Across all of them, Datarade is effectively aggregating a few major data archetypes:
1. Mobile device signals
GPS / SDK / app-based tracking
2. Foot traffic intelligence
Store visits, dwell time, return frequency
3. POI (Point of Interest) mapping
Linking movement to real-world places
4. Aggregated population flows
Commuting patterns, travel corridors, migration trends
Why This Matters
This ecosystem exists because no single company can easily build global mobility intelligence alone.
Datarade acts as a distribution and discovery layer, while providers like Quadrant or Veraset act as the data infrastructure layer.
Datarade’s Mobility & Logistics — geospatial and location intelligence segment, sits at the intersection of real-world movement data and AI-driven decision-making.
It is one of the most commercially valuable parts of its marketplace because it translates raw location signals into insights about how people, vehicles, and goods move through physical space.
Here’s a deeper breakdown of what that actually means in practice.
In Datarade’s ecosystem, mobility and logistics data typically refers to:
• Mobile location signals (GPS or device-level movement data)
• Aggregated foot traffic patterns (visitation and dwell time)
• Transportation and routing datasets (roads, travel flows, congestion)
• Points-of-interest (POI) movement interactions (stores, airports, warehouses)
• Supply chain and fleet movement data (trucking, shipping, logistics networks)
These datasets are used to build location intelligence systems—analytics layers that explain not just where things are, but how and why they move.
As a category, this is often grouped under “location intelligence,” which combines POI data, mobility traces, and contextual layers like demographics or activity patterns.
Core Idea: From “Maps” to “Behaviour”
Traditional mapping tells you where a place is.
Mobility intelligence tells you:
• Who visits it
• When they visit
• How often they return
• Where they came from
• What other places they visit before/after
This shift—from static geography to behavioural geography—is what makes mobility data powerful for AI and logistics optimization.
Key Data Types in This Segment
1. Mobile Device Movement Data
This is one of the most important inputs.
It tracks device pings to reconstruct:
• Travel paths
• Commuting flows
• Regional migration patterns
• Urban congestion trends
2. Footfall and Visit Data
Used heavily in retail and commercial logistics.
It answers questions like:
• How many people visited a store or warehouse?
• How long did they stay (dwell time)?
• Was it a customer or passing traffic?
3. POI + Mobility Enrichment
This combines:
• Location databases (shops, logistics hubs, stations)
• Movement patterns around those locations
4. Transport & Logistics Flow Data
This includes:
• Trucking routes
• Shipping lane analysis
• Fleet telematics
• Delivery time optimization signals
How Datarade Uses This Data in Practice
Lets be clear, Datarade itself is not the data collector—it is a marketplace and infrastructure layer connecting buyers and sellers of personal data.
This means companies can search for datasets on its platform, and even request samples before purchase.
The Bigger Picture
Within Datarade’s ecosystem, Mobility & Logistics is not just a dataset category—it is part of a broader shift toward geospatial intelligence infrastructure.
Like most data platforms, its value depends heavily on how responsibly the data is sourced, sold, and used. The same features that make it powerful for legitimate analytics can be misused if safeguards fail or bad actors get access.
Here are the ways such a platform could be corrupted:
Re-identification of anonymized data
Even if datasets are “anonymized,” combining multiple datasets purchased through a marketplace can allow someone to re-identify individuals.
This is a known issue in data science—linking datasets can reveal identities or sensitive attributes.
Example: Combining location data with purchase histories to infer who a specific person is
Risk: Privacy violations, surveillance without consent
Targeted manipulation or exploitation
Detailed datasets about behaviour, preferences, or demographics can be used to manipulate individuals or groups.
Microtargeting political messaging or misinformation
Identifying vulnerable populations (e.g., financially distressed individuals) for predatory schemes
Behavioural profiling for coercion or discrimination
Corporate espionage / competitive intelligence abuse
Companies might use purchased datasets to gain unfair or unethical advantages.
Inferring a competitor’s supply chain or pricing strategy
Tracking employee movement patterns
Extracting insights that violate confidentiality expectations
Surveillance and tracking
Location and mobility datasets are particularly sensitive.
Tracking individuals or groups over time
Monitoring activists, journalists, or political opponents
Mapping routines and physical behaviours without awareness
Fraud and cybercrime enablement
Aggregated data can lower the barrier to fraud.
Building richer profiles for phishing or identity theft
Enhancing social engineering attacks with real personal data
Identifying high-value targets
Bias amplification and discrimination
Datasets can encode biases. When resold and reused:
AI models trained on biased data can discriminate (e.g., hiring, lending)
Segmentation data can be used to exclude or disadvantage certain groups
Regulatory arbitrage
Some actors may exploit differences in global data laws.
Buying data sourced in regions with weak privacy protections
Using data in ways that would be illegal in stricter jurisdictions
A platform like Datarade doesn’t inherently do these things—it acts as an intermediary.
The real risks depend on:
• Data suppliers (how the data was collected and 'anonymized')
• Buyers (their intent and compliance practices)
• Platform governance (vetting, auditing, usage restrictions)
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