Most location-data vendors are a black box. So we wrote the standard.
Location data can connect an ad impression to a real store visit, but only if it is sourced, refined and measured properly. Too much of the market ships unreliable data behind a finished dashboard. This is our attempt to be clear and honest about how it actually works, and where its limits are. Built on Azira's infrastructure, drawn from our campaign practice across Türkiye, the Balkans and MENA.
The data, at scale
Raw signal becomes consumer-behaviour intelligence only on top of a broad, consent-based foundation:
- 1.6 billion people, all with consent
- 100,000+ apps across 44+ countries
- 10 TB+ processed every day
- Millions of live geo-boundaries
Scale matters only with quality. Raw signal is refined, much like crude oil into usable fuel, before it becomes reliable.
Not all data is created equal
Few providers have the experience, business rules and data science to turn raw signal into value you can trust. When they don't, the cost is real: bad data is estimated to cost US business around $3 trillion a year, and roughly 40% of business goals are said to fail on wrong data. The question is never who has the most data. It is whose data is reliable, transparent and ready for a decision.
How the data is processed
Five disciplined stages turn raw signal into decisions:
- Collection. Signal from 100K+ apps via a DSP and SDK partners, always consent-based.
- Cleaning & validation. Duplicates, spoofs and anomalies are filtered out; filters updated continuously.
- Processing. AWS compute with proprietary compression and indexing; millions of polygons, Spark.
- Identity resolution. A persistent AziraID unifies a person's identities into one (US Patent 10,979,848).
- Integrity & monitoring. A custom platform flags quality shifts automatically, backed by audits.
The three pillars
- Global, quality data. Better data, not simply more of it.
- Privacy & transparency. Privacy-by-design; no PII stored; GDPR, KVKK and UAE PDPL.
- Data science. Algorithms and machine learning that filter noise and surface the patterns that matter.
Privacy, in practice
Location data is sensitive, so privacy is part of the architecture rather than an afterthought. PII is never stored; every record is consent-based, with the right to be forgotten. MAID hashing, GPS jitter and home/work obfuscation make re-identification impractical, and FIPS-grade encryption protects data at rest and in transit.
Delivery, formats & enrichment
The data should work in your environment, not lock you into someone else's dashboard: an intuitive interface, a direct API, data feeds and bulk export. Location data as GeoJSON, KML and Esri shapefiles; event data in CSV/TSV. Enriched with location context, demographics and audience affinity, turned into segments you can act on.
What to ask a data partner
- How is data quality assured?
- Is it global and compliant, at the scale you need?
- Identity resolution and enrichment?
- Privacy beyond the legal minimum?
- Is the process transparent, can you see and analyse the raw data?
What location data can't do
No dataset is perfect, and it is more useful to say so. After iOS ATT, usable signal volume dropped; panels are a sample, not full coverage; and resolving the exact floor of a multi-storey mall is still hard. Our approach is control groups and a transparent methodology: measuring not whether a visit happened, but how many happened because of the ad.
Let's talk
If you'd like to see how footfall, audiences and attribution could work for your brand across MENA, we're happy to talk it through. No pressure. sales@redc.media
Download the full PDF: [PDF link. Upload the PDF in Webflow and connect it here.]
