Over the last 5 years I have often been in boardrooms where C-Suite champions have entranced fellow VPs with glittering visual analytics. Too often they have not not moved far beyond that and everyone knows that "all that glitters is not gold"

Purpose built analytics applications deliver true decision-making capability. But, as Martin Butler points out, if us poor humans can generally only deal with five things at the same time, machine learning and automated decision-making must come to the rescue of humans to allow them to focus on the important five things.

Algorithms, and those derived from machine learning will have to evolve fast to achieve this end. Who would have predicted the spread of the zika virus before the 2016 Olympics? What was then a "Brazilian Thing" has now made Singapore a place to avoid today. Before we get too reliant on TeleHealth and IoT Wearables data scientists have lots of work to do on automated decision making.

I am interested in location intelligence solution providers like Carto. Can they combine location and health data to anticipate hazards like the Zika virus? 

Combining these spatial and other data sources in analytics platforms and embedding these in healthcare decision-making engines is part of the Five stages of the Analytics of Everything

Most organisations are only on stages one and two- to judge where you are check here.

I have used healthcare as one example but the same goes for all industries and segments