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.
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.
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.
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.
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.
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.
Collaborate and agree on the compliance needs for "anonymity". Define the data contract, and make the handshake.Learn more
Define the transformations you need to meet compliance needs, from straightforward de-identification to K-Anonymity.Learn more
When it's anonymous, data is safe. But before it's anonymous is the step that matters. With STRM, everything just stays in your VPC or even premise.Learn more