There's value hiding in your data. Unlock it with anonymization.

Many organizations need to anonymize data. But defining when data is anonymous is challenging, requires coordination across many departments, and is hard to implement. With STRM, you define, agree and deliver anonymization pipelines through a simple interface.

Analytics

Secure data for data analytics

Capturing insights from data requires deep inspection. But you aren't always allowed to just analyse everything for anything - either by law or compliance concerns. Anonymized data can lift this constraint, and help unlock the value in your data.

Data science

Safe, secure data for machine learning

Do you need anonymous data for advanced machine learning and data science? Make your models behave: define anonymized data sets with STRM and ensure your training and inference uses safe, secure and valuable data.

Don't just take it from us

Anonymous

When is data anonymous? Well, "it depends". With STRM, you can tailor the way you anonymize to every use case and pipeline. No complicated deployments, expensive maintenance or model training and data drift.

Collaborative

Definining "anonymity" involves many teams across your organization. Legal has an interpretation, compliance worries, engineering just wants to build. With STRM, you can collaborate with different teams to establish the confidence and trust you're doing it right.

Continuous

Defining anonymity for a data set is one thing, for many data sets quite the other. With STRM, you can set up continuous pipelines to anonymize data as it comes in. It doesn't get any fresher than that.