Statice is a Berlin based data privacy company, working at the cutting edge of data anonymization. Statice allows companies to unlock the value inherent in their data for internal use cases, to share with external collaborators, or for monetization purposes, while simultaneously protecting the individual consumer’s privacy.
My thesis for joining Statice was relatively simple: privacy is not going to become less of an issue for consumers, and access to data is not going to become less important to innovation at companies, no matter at which stage of their digital journey. In our growing customer-centric economy, innovation begins with understanding people. Companies partly do this by measuring, tracking, and storing personal data on individuals in order to quantify personal preferences and use this knowledge to tailor experiences and products towards each customer individually. Personal data is the core source of modern services and products and serves as the most important resource for the majority of modern technological advances and discoveries. This does not only hold for scientific settings but also for corporate R&D.
Not all anonymization is the same though, as Netflix found out a while back. It’s important to not just remove personal information, as this can be circumvented by matching the “anonymised” data with additional data sets (as in the Netflix case above. Statice solves thi by using generative machine learning algorithms built around differential privacy concepts to create synthetic data sets, which retain the statistical value of the origin data, but none of the personal value. This is relatively new technology, but it’s mathematically proven, and is accepted as being private under Europe’s new (ish) digital privacy law, the General Data Protection Regulation. In short, you can think of it as teaching a machine about a data set, having the machine learn that data set really well, and then having it generate a new data set that effectively contains very similar information. Imagine a set of census data, and running it through this process. You won’t find a single person from the training set in the synthetic data set, but you’ll find a very similar number of people living in street A, with and age between X and Y.
What this means in practice, is that companies can take sensitive data and effectively capture the value in that data to share, either internally (for example, allowing an internal product team access to sythetic data based on conversation histories to build better personalized experiences), or externally (for example, enabling medical research by enabling collaboration between hospitals and medical research institution).
Being able to contribute to Statice’s vision of becoming the central privacy-preserving data hub for data-driven collaboration across companies is tremendously exciting, and I can’t wait to get started! If you’re interested in data privacy, collaboration around private data, or want to grab a coffee to chat about Statice, just drop me a line at hi (at) bnolan0.com!