by inlineint on 1/6/19, 2:51 PM with 2 comments
by andyidsinga on 1/6/19, 7:21 PM
I've been working with a buddy on some analytics service products for biotech/life sciences (one is called Yukon Data Solutions). What we've found looking at other products in the space is they often require the customer to focus a lot on processes, tooling, infrastructure - and so some degree algo research - in order to use the product.
What is key, we believe, is a focus on the analytics recipes and available algo ingredients in order to support decisions: how are the analytics recipes and resulting reports going to help decide on the next steps for the customer's business?
Whats seems to be background / secondary? explaining the tools, libraries, languages, cloud infrastructure, automation, data lakes etc that a service may or may not use behind the scenes to achieve the goal. Are these needed - yes at different levels and different times depending on the customer - but focus on the recipe, ingredients and decision support seems key.
edit: I found Eugene Dubossarsky's thoughts on Decision Support interesting in this podcast : https://anchor.fm/datafuturology/episodes/1-Dr-Eugene-Duboss...
by mooreds on 1/6/19, 3:09 PM