We recently announced our investment into Red Sift. I took the opportunity to write a longer piece explaining our rationale for the problem space and why we decided to back Red Sift's approach.
Put simply, we produce huge amounts of data — not just in aggregate on networks synonymous with big data like Google or Facebook — but as individual users as well. Mostly by sending and receiving tons of emails but also on WhatsApp and other platforms where we exchange and collect work or personal information.
The Red Sift platform brings the power of machine learning tools already making sense of the world’s data to bear on our own personal data sets.
Read on to understand how.
They conjured up a picture of a platform that would bring the power of machine learning tools already making sense of the world’s data to bear on our own personal data sets. At first this would be email, everyone’s biggest data dump, but the platform would quickly expand to Slack, Facebook Messenger, other communication services and IoT streams. The platform would be home to data apps called “Sifts”, open-source micro services, that can be built or forked by any developer to run secure computations on your data and deliver them back in-situ or on a dashboard.