Inefficient management of customer data is costing businesses millions, according to new research by SBDS. So although organisations are collecting valuable data and marketers understand the value of a single-customer view, data often remains siloed and is seen as expensive and time-consuming to integrate. Leaving this data untapped is clearly missing a trick, but it must only be used in an ethical way that prioritises customer privacy.
We’re helping organisations build trust and transparency by connecting the data dots in a mathematically privacy-safe way. Organizations can connect siloed data, such as data held in disparate systems or CRMs, and collaborate with colleagues, without losing control or putting customers’ privacy at risk. To make this possible, we’ve solved the industry issues around data standardization, data privacy and commercial trust.
Our platform makes it possible for brands, agencies and publishers to run privacy-safe analytics across digitally connected datasets without sharing the raw data. Rather than moving the data into a honeypot, only the query moves, meaning customer data is never at risk and always remains in the control of the data owner.
It seamlessly integrates with a range of databases, adtech platforms and BI tools to produce the same analytical output as stitching the datasets together in one location, without the data privacy, trust and implementation barriers. Only with InfoSum can organi`ations learn from multiple datasets without relying on sharing hashed data.
With InfoSum, marketers and analysts can safely access and activate more data-driven customer knowledge than ever before. For the first time, brands are safely use their first-party data for audience selection and targeting. Media buyers are remotely accessing client data and create targeted segments, while publishers are facilitating the comparison of ad exposure with subsequent engagement or purchase, all without sharing data.
A core component of our platform is our unique solution for identity resolution. Our platform automatically maps identities across isolated datasets via one or more identifiers, such as email address and social media handle, and determines the highest quality match. During set up, the identifying data is irreversibly anonymized and the original data deleted, then the record can be connected to others by a non-reversible mathematical model.
Our platform is built with customer privacy in mind, and uses differential privacy concepts and data anonymization techniques to enable users to gain insights from a multitude of datasets while making it impossible to extract information about a single individual. By not sharing data, data owners are assured that they can collaborate with partners without risking anonymized customer data being manipulated and stitched with other sources to identify individuals.