Analytics for the Internet of Things (IoT)

Developing a progression process

Data science is all about experimentation. IoT analytics has great potential. However, no one is certain of how it will develop. Put them both together and an enormous amount of experimentation should be expected. To keep this from becoming an impossible-to-manage mess, there needs to be a way to progress ad hoc datasets from early development into repeatable and stable data products.

Setting up a progression process will help manage this. Data science is highly iterative, which makes it difficult to find a clear point that signals a change in state, as with normal database development projects.

A way to handle this is by setting up regular review periods where a team determines which datasets are ready to ...