Pyte | Why Companies Need Secure Data Collaboration

The challenges
with existing solutions

Insecure: hashing is okay, but "secure"? Maybe.
Few encrypt at all, and most solutions don’t secure customer data in any way.
Less control: when "more with less" is the motto, the last thing you need is a custody battle over data. Solutions today require handing over your data.
Lack of performance: any solution that claims encryption is so slow, making it virtually impossible to use in a practical setting.
Inaccurate: some solutions protect customer data by introducing noise in the dataset, making it much less trustworthy.
Lack of functionality: some solutions limit what process you are able to run on your dataset (e.g. limited to machine learning), leaving it with limited functionality.
Less flexible: some solutions require you to have very specific server hardware, where your data can be processed, limiting who you can collaborate with.

Accessing multiple proprietary data sources has never been easy, but the ever changing regulatory landscape has made it more challenging than ever.

3rd party consumer identifiers are going away
Technology platforms are walling off data to prevent hacks
Consumers are opting out of tracking
Privacy regulations are making it more difficult to obtain and leverage customer data

2nd party data: collaborate with external data partners to enrich your customer data
Modeling: build statistical models with data partners to expand size of audience
Security & control: treat customer data with privacy and respect to reduce regulatory risk

2nd party data: collaborate with external data partners to enrich your data
Modeling: build statistical models with data partners to expand dataset size and efficacy
Security & control: employ data with privacy and security to reduce regulatory risk