Realizing the Economic Promise of Predictive Analytics

Irving Wladawsky-Berger

Wikipedia defines predictive analytics as a set of statistical techniques, - such as data mining, business analytics, and machine learning, - “that analyze current and historical facts to make predictions about future or otherwise unknown events.”

Notes On "Global Guidelines: Ethics in Learning Analytics"

Stephen Downes: Half an Hour

In my post referencing the ICDE's Global Guidelines: Ethics in Learning Analytics yesterday I said: This document summarizes the considerations of an ICDE working group on learning analytics. The presumption throughout is that a global ethic in learning analytics is needed, can be known, and comprehends the issues in this document. Definition and purpose of the use of Learning Analytics Where is data drawn from? What data must be included in learning analytics?

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Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Irving Wladawsky-Berger

They’ve enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to complex problems like language translation, natural language processing, and playing championship-level Go. Its prediction algorithms integrate social physics technologies into its data analytic engine, enabling it to efficiently extract the underlying social attributes embedded in the raw data being analyzed.

Ethical Codes and Learning Analytics

Stephen Downes: Half an Hour

Abstract The growth and development of learning analytics has placed a range of new capacities into the hands of educational institutions. The ethics of learning analytics will therefore need to be developed on criteria specific to education.

SXSW: What Social Media Analytics and Data Can’t Tell You

Beth Kanter

I’m just back from the SXSW Interactive Festival where I was on a panel called “ What Social Media Analytics Can’t Tell You ” moderated by Alexandra Samuel of Vision Critical , Jeremiah Owyang , Crowd Companies, and Colby Flint, Discovery Channel. We discussed how social media analytics can provide some great information on your existing social media followers, but at the same time, there are gaps that need to be filled through other techniques.

Evidence that "Retail Therapy" is Effective - Bob Sutton

Bob Sutton

Davenport: Competing on Analytics: The New Science of Winning.   BPS Research reports a recent article containing a series of small studies that shows "retail therapy" does work -- at least in a sample of young American consumers.  Bob Sutton. About Subscribe to this blogs feed Email Me Follow Me @ work_matters. Book Me For A Speech. Brightsight Group. Search. 17 Things I Believe. Sometimes the best management is no management at all -- first do no harm!

Game Learning Analytcs for Evidence=Based Serious Games

Stephen Downes: Half an Hour

hindsight – insight – foresight - Needs all the dat - Now being used in MOOCs, because they have so much data Game Learning Analytics (GLA) - Learning analytics applied to serious games - Collect, analyze and visualize Uses of GLA - Game testing – eg., stealth’ student evaluation - Formal game evaluation RAGE – game analytics (using xAPI) Beaconing – game deployment GLA or Informagic?

Zoom needs to clean up its privacy act

Doc Searls

A few samples: Zoom is a work-from-home privacy disaster waiting to happen ( Mashable , March 13). think, for example, Google Ads and Google Analytics). As quarantined millions gather virtually on conferencing platforms, the best of those, Zoom , is doing very well. Hats off.

Data 285

Social Physics and Cybercrime Detection

Irving Wladawsky-Berger

These patterns can be used to detect emerging behavioral trends before they can be observed by other data analytics techniques. These advanced statistical methods have enabled the creation of AI algorithms that can be trained with lots and lots of sample inputs instead of being explicitly programmed. Endor’s analytics engine identified 80 Twitter accounts as potential EOIs because they were similar enough to the positive samples that the agency provided.

Big Data Takes Center Stage

Irving Wladawsky-Berger

The first is what they call n=all, that is, collect and use lots of data rather than settling for small samples, as statisticians have done until now. “The The way people handled the problem of capturing information in the past was through sampling. When collecting data was costly and processing it was difficult and time consuming, the sample was a savior. Sampling requires anticipating how the data will be used so you can design the proper sample.

Data 157

Recommendations for Good Practice Using AI in Learning

Stephen Downes: Half an Hour

Data (or at least, samples of the data) should be studied for potential errors in labeling, data recording, and other systemic errors. Some Resources Ethics, Analytics and the Duty of Care [link] FTC.

Data 277

Blockchain and Public Health Solutions

Irving Wladawsky-Berger

Population data. “All these data would represent the entire population, not some partial and potentially misleading sample of it. Using next generation data analytics and AI they could understand the possible trajectories of a virus and take steps to crush it.”.

Time for advertising to call off the dogs

Doc Searls

A sample of that stench is wafting through the interwebs from the Partnership for Responsible Addressable Media , an ad industry bullphemism for yet another way to excuse the urge to keep tracking people against their wishes (and simple good manners) all over the digital world.

Do you have 4 minutes to help me learn what people do all day at work?

Dan Pink

In an effort to add some statistical meat to the book’s analytic bones, I’ve enlisted the wonderful folks at Qualtrics and devised a brief survey on people’s work activities and attitudes. But because we’re working hard to make sure we have a fully representative sample, and because the huge response has given us a chance to assemble even representative subgroups, we’re looking for more participants.

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

It’s led to the development of AI algorithms that are first trained with lots and lots of sample inputs, and then subsequently applied to complex problems like language translation, natural language processing, real time fraud detection, personalized marketing and advertising, and so on. After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. AI is seemingly everywhere.

Data 159

The State of American Jobs

Irving Wladawsky-Berger

The survey was based on landline and mobile telephone interviews with a national sample of over 5000 adults. workplaces as the economy moves deeper into the knowledge-focused age,” said the report in its opening paragraph. “These changes are affecting the very nature of jobs by rewarding social, communications and analytical skills. Its analysis showed that between 1980 and 2015, jobs in occupations requiring high social and analytical skills had the highest growth.

Skills 101

Decision Making in Our Increasingly Complex Organizations

Irving Wladawsky-Berger

It’s the best and worst of times for decision makers,” said McKinsey in a recent article, Untangling your Organization’s Decision Making. “Swelling stockpiles of data, advanced analytics, and intelligent algorithms are providing organizations with powerful new inputs and methods for making all manner of decisions.” The sample skewed toward upper management, - one-third of respondents were C-level executives and 35% were senior managers.

Educational Research in Learning Technology

Stephen Downes: Half an Hour

Sample Size 4. Sample size and representation (see below) are the two cardinal principles of statistical methodology, and it we well known that a sample of a certain size is needed for a survey ti be accurate "plus or minus three percent, 19 times out of 20".

Narratives of culture: the score

Dave Snowden

Suffice it to say for the moment that this uses SenseMaker® and a non-hypothesis question that can be asked of the whole workforce or just a sample: What story would you tell your best friend if they were thinking of joining your workgroup? In this case we can see that the overall orientation of the organisation is towards analytical decision making, there is little in the intuitive space and some orientation towards principled decision making.

Solow Paradox 2.0: The Lagging Impact of Technology Advances on Productivity Growth

Irving Wladawsky-Berger

The report analyzed the productivity-growth declines across a sample of seven countries, - France, Germany, Italy, Spain, Sweden, the United Kingdom, and the United States, - which represent about 65% of the GDP of advanced economies. Fewer workers is one of the key reasons for our stagnant economic growth. Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline, especially in advanced and emerging economies.

What Machine Learning Can and Cannot Do

Irving Wladawsky-Berger

Machine learning , and related advances like deep learning , have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed. One of the key features of deep learning algorithms is that, unlike classic analytic methods, there’s no asymptotic data size limit beyond which they stop improving.

The Public Face of Science

Irving Wladawsky-Berger

The first survey is based on a representative sample of 2,002 general public adults and was conducted by landline and mobile phones. The second survey was conducted online, and is based on a representative sample of 3,748 US-based scientists who are members of AAAS. Last month, the American Academy of Arts and Sciences launched a 3 year initiative to address the complex relationship between scientists and the public.

Survey 107

Notes from ELI 2015 Riyadh - Day One

Stephen Downes: Half an Hour

Online Newspaper Software

Stephen Downes: Half an Hour

Focus on subscription, scheduling, advertising and analytics. Here's a sample story. Some samples here and here 's another. Drupal & Hosted Drupal Newspapers Running on Drupal This is a demo site showcasing newspapers running on Drupal, a popular open source content management system. These are sites set up using Drupal and then expanded with various modules.

The Linearity of Stephen Downes. Or a tale of two Stephens

George Siemens

The sample sizes are too small for quantificational results (and the studies are themselves are inconsistent so you can’t simply sum the results). The sample is biased in favour of people who have already had success in traditional lecture-based courses, and consists of only that one teaching method. Where we have large amounts of data, learning analytics can provide insight, but often require greater contextual and qualitative data.

Reflections on Experience

Clark Quinn

While machine learning and analytics is one opportunity, there’s another, which is having people look at the data. That is, the system tracked and intervened on whether you were varying one variable at a time, ensuring your data sampling was across a broad enough range of data points, etc. The API previous known as Tin Can provides a consistent way to report individual activity. With the simple syntax of <who> <did> <this> (e.g. <Clark

Sample 137

Design principles in DSS software

Dave Snowden

As such it accepts the problem of samples of one or less which is the reality of managing for uncertainty and creating resilience. That means any system must be designed to enable the rapid deployment not just of data analytics, but also of mass human sensors who see things from different perspectives and who in aggregate will scan more. Both are important, but there is too great a tendency to want to black box analytics.

AI - the Creation of a Human-Centric Engineering Discipline

Irving Wladawsky-Berger

It’s enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to difficult AI problems like language translation, natural language processing, and playing championship-level Go. Machine and deep learning are the latest examples of tools like the World Wide Web, search and analytics that are helping us cope with and take advantage of the huge amounts of information all around us.

Ritual and the Service Experience

Adaptive Path

Most organizations lean heavily on analytical methods to define rigid processes and procedures that are designed to reduce waste and increase predictability in service delivery. Yet, the service of wine is a highly ritualized experience involving presentation, sampling, and approval. The interplay between efficiency and quality in a service experience is often what separates a merely transactional interaction from a valuable and pleasurable one.

Sample 176

Nothing Has Changed. Everything Has Changed.

Charles Jennings

This study sampled 35,000 managers and employees across the globe. A Revolution or a Slow Demise? I’ve recently read Clark Quinn’s excellent new ‘Revolutionize Learning & Development’ book. Clark always provides a thoughtful and enlightening perspective. There are some observations and suggestions in here that get to the heart of the issues around the fact that our approaches to building capability through learning need a radical rethink.

Change 183

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

It’s led to the development of AI algorithms that are first trained with lots and lots of sample inputs, and then subsequently applied to complex problems like language translation, natural language processing, real time fraud detection, personalized marketing and advertising, and so on. After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. AI is seemingly everywhere.

The Jobs Outlook for 2022

Irving Wladawsky-Berger

The survey identified four specific technology advances that are set to dominate business growth over the next five years: ubiquitous, high speed mobile Internet; widespread adoption of big data analytics; artificial intelligence; and cloud computing. Applied to our sample, representing over 15 million workers in total, the above numbers would suggest a decline of 0.98 A few weeks ago, the World Economic Forum (WEF) released The Future of Jobs Report 2018.

Survey 112

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

It’s led to the development of AI algorithms that are first trained with lots and lots of sample inputs, and then subsequently applied to complex problems like language translation, natural language processing, real time fraud detection, personalized marketing and advertising, and so on. After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. AI is seemingly everywhere.

What’s in your social media measurement tool box and why?

Beth Kanter

Measurement tools or perhaps more accurately, social analytics tools collect that data. If you are measuring reach, engagement or action, you’ll need an analytics tool. Analytics Tools: Google Analytics, Facebook Insights, Twitter Tools (Socialbro, Hootsuite, Bit.ly, Twitalyzer). These tools combine what’s needed into one application. They also say that a single MMM tool won’t solve all your needs and that Google Analytics is another must have.

Human-AI Decision Systems

Irving Wladawsky-Berger

Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. . Sports analytics are now used by just about every professional sports team in the world. It’s enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to difficult AI problems, including natural language processing, language translation and computer vision.

System 101

Wrap Up of the Australasian Talent Conference

Kevin Wheeler

Emerging topics that will be featured next year include a more complete integration of workforce planning, recruiting, learning, and performance management into talent management as well as new approaches to workforce planning, better and more useful analytics, and the application of quality standards and measures to talent management. There we stopped by a dozen or so of the best and sampled generously. Australasian Talent Conference.

Human-AI Decision Systems

Irving Wladawsky-Berger

Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. . Sports analytics are now used by just about every professional sports team in the world. It’s enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to difficult AI problems, including natural language processing, language translation and computer vision.

System 100

Jobs, Skills and Education

Irving Wladawsky-Berger

Last year, for example, the Pew Research Center , - in association with the Markle Foundation , - published The State of American Jobs , a report based on interviews with a national sample of over 5,000 adults to learn what Americans are thinking about the skills and training needed to get ahead in our fast changing digital economy.

Skills 101

Jobs, Skills and Education

Irving Wladawsky-Berger

Last year, for example, the Pew Research Center , - in association with the Markle Foundation , - published The State of American Jobs , a report based on interviews with a national sample of over 5,000 adults to learn what Americans are thinking about the skills and training needed to get ahead in our fast changing digital economy.

Skills 100

What Makes a Company Good at IT?

Andy McAfee

Companies that were one standard deviation higher in being data-driven had 4% higher productivity and 6% higher profits than the average in our sample, all else being equal. Haven’t modern tools for analytics and business intelligence transformed how most companies make decisions and sense their environments? My MIT Center for Digital Business colleague Erik Brynjolfsson and I published an article in the Wall Street Journal on April 25.

The Datafication of Business and Society

Irving Wladawsky-Berger

Physicists, astronomers, biologists, geophysicists and other scientists and engineers were developing methodologies and architectures for dealing with very large volumes of unstructured data, as well as analytical techniques, like data mining , for discovering patterns and extracting insights from all that data. . I first encountered the term datafication in The Rise of Big Data: How It’s Changing the Way We Think About the World , by Kenneth Cukier and Viktor Mayer-Schönberger.

Data 157

What Machine Learning Can and Cannot Do

Irving Wladawsky-Berger

Machine learning , and related advances like deep learning , have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed. One of the key features of deep learning algorithms is that, unlike classic analytic methods, there’s no asymptotic data size limit beyond which they stop improving.

The future of higher education and other imponderables

George Siemens

The adoption of the internet and mobile technologies (read the whole Meeker Report if you want a good sampling of the scope of technological change) continues at a frenzied pace. The research topics are: social networks and media, data and analytics, and systemic change in higher education. We will be running an open online course from Oct 8-Nov 16 , 2012, addressing some of the concepts in this post. Registration is free (duh).