No Thanks, We Have Data. Everywhere we are told that data makes winners and winners use data. No "animal spirits" for us, thank you very much! But abandon hard-won good judgment? There was a time when decision makers were criticized for "analysis paralysis", which is the failure to choose.

In response to automation and dislocation in the 19th Century, Mary Shelley gave us Frankenstein. Signals processing engineer, data scientist and now author S. B. Divya has imagined a whole world where boundaries between machine and human are blurred. Interestingly, according to reviews, the question of how work is performed by either human or machine drives a lot of the dramatic tension in Divya's imagined world. At Data Decisioning, we like that because work is the meaning of technology. When the CEO asks about ROI of a proposed AI investment, the justification is productivity, i.e. more work for less.

Your Home Is Your Castle; Don't Forget The Moat In this week's lead quote, a16z's Casado and Martin claim diminishing returns to scale for more data. This is a bold proposition because it undermines a key argument for many data programs: that more data creates a defensive moat to protect the enterprise. Certainly, all organizations in competitive markets want to create defensive moats, and data can help. But it has to be done the right way. The right way to build a viable data-based defensive moat is specific data, not more data.

Data is useless, exclaims Piyanka Jain, author of this week's lead Big Data Quote of the Week. No need for data champions to despair however; Jain goes on to clarify the real value of data, which is to drive decisions.. The same rule shows up in the NYT job ad for "Analyst, Revenue Analytics"; the role is all about providing "essential recommendations to crucial business decisions". So while data is nice, decisions are the ultimate payoff. Put them together and you get the "data-to-decisions" value chain.

If "it's not obvious yet, the amount of compute to generate data will always outpace the capacity to analyze it. In other words, the'cost of a question' will always go up as data grows." "Deep neural networks can ingest large amounts of data and exploit huge computing resources to solve very narrow problems...However, we often can't count on them if the environment differs, sometimes even in small ways, from the environment on which they are trained." "Successful implementation of predictive modelling could represent a major leap forward in the fight to rid the world of some of the most infectious diseases. Big data analytics can help de-centralise the process and enable the timely analysis of widespread data sets generated through Internet of Things (IoT) and mobile devices in real-time."

'They (Amazon) happen to sell products, but they are a data company,' says James Thomson, one of the former executives interviewed. 'Each opportunity to interact with a customer is another opportunity to collect data.'

Leaders don't understand what adoption of AI means. Many companies feel pressured to adopt AI by any means necessary -- without thinking through the why and how ... The metaphor that comes to mind is a fish lured to the next shiny bauble, only to realize too late that the hook will be its last meal.

We have a lot more to fear from natural stupidity than from AI.