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It's not humans who are the recipients and decision makers of data and analysis, it's machines. Machines are making all or most of the decisions in areas like programmatic advertising, search engine optimization, credit approval, insurance underwriting, Internet of Things applications, and many more.

The use of machine learning, expert systems and analytics in combination with big data, is the natural evolution of what has been two different disciplines. They are converging.

"Value Networks and the true nature of collaboration meets this challenge head on with a systemic, human-network approach to managing business operations and ecosystems. Value network modeling and analytics provide better support for collaborative, emergent work and complex activities."

Humans are biased, and the biases we encode into machines are then scaled and automated. This is not inherently bad (or good), but it raises the question: how do we operate in a world increasingly consumed with "personal analytics" that can predict race, religion, gender, age, sexual orientation, health status and much more.

Finding useful knowledge nuggets amongst the torrents of data is a skill in itself. Creating insightful stories that bring the data to life is an emergent skill practiced by data journalists. An excellent article with lots of useful references for anyone who aspires to blend data analytics with storytelling.

McKinsey have developed an approach to retention: to detect previously unobserved behavioural patterns, they combine various data sources with machine-learning algorithms. Workshops and interviews are used to generate ideas and a set of hypotheses. Over time they collected hundreds of data points to test. Then ran different algorithms to get insights at a broad organisational level, to identify specific employee clusters, and to make individual predictions. Finally they held a series of workshops and focus groups to validate the insights from our models and to develop a series of concrete interventions. The insights were surprising and at times counterintuitive. They expected factors such as an individual's performance rating or compensation to be the top predictors of unwanted attrition. But analysis revealed that a lack of mentoring and coaching and of "affiliation" with people who have similar interests were actually top of list. More specifically, "flight risk" across the firm fell by 20 to 40 percent when coaching and mentoring were deemed satisfying.

McKinsey have developed an approach to retention: to detect previously unobserved behavioural patterns, they combine various data sources with machine-learning algorithms. Workshops and interviews are used to generate ideas and a set of hypotheses. Over time they collected hundreds of data points to test. Then ran different algorithms to get insights at a broad organisational level, to identify specific employee clusters, and to make individual predictions. Finally they held a series of workshops and focus groups to validate the insights from our models and to develop a series of concrete interventions. The insights were surprising and at times counterintuitive. They expected factors such as an individual's performance rating or compensation to be the top predictors of unwanted attrition. But analysis revealed that a lack of mentoring and coaching and of "affiliation" with people who have similar interests were actually top of list. More specifically, "flight risk" across the firm fell by 20 to 40 percent when coaching and mentoring were deemed satisfying.

"Hans Rosling is debunking the River of Myths about the developing world. By measuring the progress in the once labeled "developing countries", preventable child mortality can be history by the year 2030."

Amazon Web Services announced that it was selling to the public the same kind of software it uses to figure out what products Amazon puts in front of a shopper, when to stage a sale or who to target with an email offer. The techniques, called machine learning, are applicable for technology development, finance, bioscience or pretty much anything else that is getting counted and stored online these days. In other words, almost everything.

Worth downloading and reading this paper. One abstract: "The ultimate goal is to enable data scientists,business analysts and other users "to extract the most information they can out of data as quickly as possible....For the business, we need answers now. The market is fixing the pace, so we have to give the best answer we can at the right time."

Tableau helps people see and understand data

Staying ahead in the accelerating artificial-intelligence race requires executives to make nimble, informed decisions about where and how to employ AI in their business. One way to prepare to act quickly: know the AI essentials presented in this guide.

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies.

SNA analysis of Facebook's @Workplace by Laurence Lock Lee