Group items tagged

KIN has long stressed the importance of learning from failure. In this short, funny and revealing post, David D'Souza publicly shares his experience of what not to do in a TV interview. His post uses humour, it's punchy (note the bullet points) and is in the first-person. I doubt I'll ever be on TV, but everyone could immediately relate to and learn from this. Now that's real learning from failure - the antithesis of a dry 'lessons learned' report.

" Tim O'Reilly mashes it up... In the first segment of a two-part interview, technology guru Tim O'Reilly outlines his views on open source in an interview with the BBC World Service programme Go Digital. "

Interview with the Godfather of Organisational Change Dr John Kotter

"LexisNexis has surveyed 500 people working in information services in a range of roles across Europe.  In depth interviews were held with professionals in France, Germany and the Netherlands and a broader survey was sent out to information professionals across Europe.  Finally, the researchers interviewed senior academics to review the findings."

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.

" A freely accessible online KM Toolkit with tools to cover the following techniques: After Action Reviews, Peer Assists, Knowledge Exit Interviews, Conducting a Knowledge Audit, Storytelling amd much more."

Also includes a link to his recent Newsnight interview on 'fingerprinting' wher he explains some of his reserach about the affect of 'context' on decision making (in relation to finger-print experts in this example")

This tool automatically produces abstracts and keywords from text. Useful for knowledge elicitation transcripts etc.

" Useful for producing abstracts and keywords from interview transcripts"

Hidden biases may be written inadvertently into the algorithms used to decide who gets a job interview or who qualifies for a loan or for parole. If a data set considers the word "programmer" closer to the word "man" than "woman," or if you build a system that learns from Wikipedia, where only 17 per cent of profiles of notable people are women, these biases will be perpetuated in the machine.

Teaching computers to understand casual, contextual conversation in every language and accent is key to this quest to normalise our interactions with computers and to place Google even more squarely at the centre of our lives.

This site gives you access to the work and research of Dr Itiel Dror, one of the key note speakers at the December workshop.