In the past year or two, many companies have shared their data discovery platforms (the latest being Facebook's Nemo). Based on this list, we now know of more than 10 implementations. I haven't been paying much attention to these developments in data discovery and wanted to catch up. By the end of this, we'll learn about the key features that solve 80% of data discoverability problems. We'll also see how the platforms compare on these features, and take a closer look at open source solutions available.

If you own a business that relies heavily on machinery, and you're not doing any predictive maintenance yet, the chances are that you're suffering from downtime losses even while reading this. Carington Phahlamohlaka, Data Scientist at Altron Bytes Managed Solutions, says: "It's a real challenge to predict when you need to service your equipment and it's difficult to weigh the risks of lost productive time against those of a potential breakdown." This challenge is traditionally addressed in one of two ways: either reactively, by fixing the already existing failures, or proactively, where past experience is used to anticipate potential breakdowns. Unfortunately, neither of these approaches is effective enough. If you don't predict precisely when a machine or a piece of equipment is going to break down, the resulting downtime may be longer than anticipated, as you don't only need to replace a failed part, but you may also need to order it and ship it, and sometimes from overseas.