I’m preparing to publish a bunch of transit-related data as APIs, for us across a number of applications from visualizations to conversation interfaces like bots and voice-enablement. As I’m learning about the data, publishing it as unsophisticated CRUD APIs, I’m thinking deeply about how I would enable others to ask questions of this data using web APIs. I’m thinking about the hard work of deriving visual meaning from specific questions, all the way to how would you respond to an Alexa query regarding transit data in less than a second, going well beyond what CRUD gives us when we publish our APIs and taking things to the next level.
Knowing the technology sector, the first response I’ll get is machine learning! You take all your data, and you train up some machine learning models, put some natural language process to work, and voila, you have your answer to how you provide answers. I think this is a sensible approach to many data sets, and for organizations who have the machine learning skills and resources at their disposal. There are also a growing number of SaaS solutions for helping put machine learning work to answer complex questions that might be asked of large databases. Machine learning is definitely part of the equation for me, but I’m not convinced it is the answer in all situations, and it might not always yield the correct answers we are always looking for. https://goo.gl/U9XLSo #DataIntegration #ML