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Stephen Dale

What is Predictive Analytics ? - Predictive Analytics Today - 0 views

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    Predictive analytics uses many techniques from data mining, statistics, machine learning and AI.
Stephen Dale

Analytics, Data Mining, and Data Science - 0 views

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    Curated website of all things about Big Data, Analytics etc.
Stephen Dale

Deep Learning And The Future Of Search Engine Optimization | Myinforms - 0 views

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    Many companies are seeing that the future of deep learning is here, and that it doesn't require a lot of money or resources to take advantage of this new industrial science. IBM's Watson Analytics offers a freemium service that allows you to upload up to 500MB, and enables you to explore your own real-life applications for deep learning. Inputting Google Adwords or other sales metrics into this tool can help even startup companies find relational and predictive information in their data.
Stephen Dale

Amazon to Sell Predictions in Cloud Race Against Google and Microsoft - NYTimes.com - 0 views

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    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.
Stephen Dale

Setting the stage to effectively visualize data - 0 views

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    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."
Stephen Dale

How Michigan State University Calculates Likelihood of Philanthropic Engagement - 0 views

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    Michigan State University has over 450,000 alumni around the world. The school's University Advancement department sought to create a representation of alumni and donor sentiment and likelihood of philanthropic engagement based on data gathered from social media. However, these analyses often took weeks to process, limiting the school's ability to gather valuable insights in a timely manner. This case study describes how MSU leveraged business intelligence and predictive analytics to gain deep insight into an individual alum's potential to give, resulting in the following positive results: -An annual ROI of 55% -An average annual benefit of $34,434 -And more The case study purports to show how organisations can identify new opportunities for revenue generation by embracing a BI and predictive analytics strategy.
Stephen Dale

Power to the new people analytics | McKinsey & Company - 1 views

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    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.
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    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.
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