Bring together insight from first and second-party data sources to power rich customer insights - with zero security and privacy risks.
Match identities across data sources without relying on a third-party ID. Deterministic matching on PII that always protects consumer privacy.
Access better customer insights and more accurate identity resolution to deliver advertising campaigns that drive conversions and ROI.
Unlike first-generation data platforms that require data to be moved into a third-party environment, InfoSum utilises a federated architecture that enables first-party data sources to remain decentralised across multiple locations while allowing them to be analysed as though combined.
Anonymous mathematical representations of each data set are generated and move between Bunkers to create aggregate statistical results. Audience segmentation can then be conducted using any of the individual attributes available.
By removing the need to share data, each party retains full control of their data and never risks the commercial value of the data being utilised by a third-party.
Privacy has been built into every element of InfoSum’s platform, delivering a truly privacy-by-design solution. By utilising a federated architecture, data can remain decentralised and in control of the data owner, while also enabling identities to be matched and analysis conducted - all without sharing any of the underlying data.
All results are at an aggregated statistical level and are designed to drive data insights, planning and measurement.
Finally, differential privacy techniques are applied to all results. These cutting edge techniques enable rich insights to be unlocked about a particular group of individuals while ensuring that no single individual can ever be re-identified through the platform.
With third-party identifiers under increased scrutiny from regulators and now actively blocked by all the major web browsers, Personal Data (PII), such as an email address, is becoming an increasingly important identifier. However, using Personal Data to match identities between data sets, requires an increased focus on privacy and security.
InfoSum is uniquely capable of matching both deterministically on personal data, and through more probabilistic methods such as IP address. Additionally, InfoSum’s identity resolution technology can take non-unique identifiers, such as name and postcode, and create a unique combination key with high-level precision.
InfoSum provides the only privacy-safe way to utilise your first-party data within the advertising ecosystem and delivers a ‘best of both’ solution that delivers both accuracy and scale, without compromising on privacy.
Traditional data platforms require considerable data manipulation prior to upload. This extract, transform and load (ETL) process creates a significant delay in unlocking valuable insight from the data.
InfoSum flips ETL to ELT (extract, load, transform) by requiring no change to the original data before uploading. Instead, our normalisation process standardises and maps the data to our global schema automatically.
Human intervention is only required occasionally when a data field does not map to an existing data category or where attribute data is too granular.
This unique approach removes the need for expensive data migration and manipulation processes and allows data to be uploaded and available for analysis in minutes, rather than days.
Unlike traditional data platforms that require data to be either centralised or shared with a third-party to achieve analysis across multiple datasets, InfoSum’s identity infrastructure provides a secure and privacy-safe way to connect first and second-party data sources, without moving data.
Data is uploaded to a secure and isolated instance of AWS known as a Bunker. Once the raw data is uploaded, it never leaves.
All attributes are mapped to categories within our extensive global schema.
A query can be created by an InfoSum user to analyse datasets available to them.
Each ID that matches the query criteria goes through various hashing algorithms and a mathematical representation is generated that cannot be read outside of the Platform.
Privacy controls are applied to protect the data within Bunker A.
The mathematical representation then moves from Bunker A to Bunker B.
The Platform runs a series of tests to determine if any of Bunker B’s hashed IDs are in Bunker A’s mathematical representation.
Further privacy controls are applied to protect the results data.
The Platform matches IDs one-to-one, to determine the intersection. However, the only knowledge either party gains is the numerical level of intersection. They do not learn who those individuals are, or any additional information about either party’s data.
For Activation, the mathematical representation moves to an Activation Bunker, where it is tested against the IDs in the Activation Bunker.
Where a match is found, the corresponding activation IDs, for example MobileID, are flagged and output to a file.
This file is then sent to the chosen activation Platform, for example a DSP or identity platform.
Deliver personalised messaging in the cookie-free world using first-party data.Data Onboarding
Maximise match rates to unlock omnichannel marketing in a privacy-first era.Identity
Grow privacy-safe relationships between brands and media owners.Second Party Audiences
Create flexible first-data and identity alliances to achieve a network effect of collaboration.Data Collaboration