Tag: analytics

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Latest read: Superforecasting

Superforecasting: The Art and Science of Prediction by Wharton’s Philip Tetlock and Dan Gardner is another great read addressing analytics, human behavior and analytic technologies for establishing and sharpening forecasting abilities for any organization.

superforecasting the art and science of predictionThe book title is a wonderful attention grabber. We certainly live in the era of big data. Today business is driving the new age of immediacy.

Superforecasting acknowledges data lessons supplied by Nate Silver’s The Signal and the Noise as helping to define standards to data and how it misled the national media.

Yet just into the second chapter Tetlock and Gardner point to the research by Daniel Kahneman excellent work Thinking Fast and Slow to help determine behaviors shaped over time that have fundamentally changed how we predict success.

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Introducing the AWS IoT cloud

The emerging IoT developer community received a much anticipated jolt of news when Amazon finally announced new enterprise services dedicated to the AWS IoT cloud launch at their 2015 re:Invent conference.
AWS IoT Cloud PlatformThis new AWS IoT cloud service will permit web based interfaces to manage IoT events from various devices: sensors, wearables, drones, and of course mobile tools and apps around an established AWS ecosystem.

The AWS IoT cloud emerges as Amazon’s long term platform following the SalesForce Thunder platform announced last month. Both vendors look to establish key IoT cloud solutions in the corporate enterprise space. They join Cisco’s IoT, Microsoft’s Azure IoT, Oracle’s Movintracks along side GE’s energy launch of Current IoT. The race is now on to process millions of data events from light bulbs to dishwashers and cars over the MQTT protocol and process those messages in their respective clouds.

Amazon is leveraging 11 services around their IoT Cloud strategy to include existing AWS services: Kinesis, Redshift, S3, SNS, SQS, ML, DynamoDB and Lambda. A key investment to this strategy was the recent acquisition of 2lemetry, a IoT enterprise company tuned for transforming raw data from IoT devices onto their ThingFabric platform.

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LMSaaS

Over breakfast this weekend at a popular farmhouse two high school teachers sat next to me to discuss how their respective LMS solutions made teaching difficult. Both were from wealthy suburbs outside Milwaukee. What really peaked my interest was hearing how one spent over 45 minutes trying to add polling for in-class feedback.
LMSaaSI helped lead the adoption of a Moodle LMS at a private Wisconsin college in 2007 that is still in use today and also had the pleasure of attending a conference at UW-Madison with Martin Dougiamas the founder of Moodle.

Yet over that breakfast I was intrigued by their difficulty with all things LMS for the upcoming school year. Frustration ranged from how one teacher received no LMS training (poll example above) while the second teacher spoke about her district migrating to a new LMS vendor over the summer.

Of course no technology discussion can avoid a teacher mentioning K12 servers going offline for hours during the school day making their teaching even more difficult. Seems like teachers have a lot to confront on a daily basis in delivering education to a classroom of twenty plus students. A local LMS run from an empty closet is no longer acceptable.

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Latest read: Learning with Big Data – The Future of Education

Big Data has changed education forever. Learning with Big Data reveals If your school has not fully embraced big data you should consider moving your child’s education elsewhere. In higher education its fully integrated across the institution from the admissions office all the way through the office of alumni relations.
Learning with Big Data – The Future of EducationThis short e-read builds upon the success of Big Data: A Revolution That Will Transform How We Live, Work, and Think released in 2013. This book is not about MOOCs, but does dedicate pages to the background and success of Khan Academy.

Authors Viktor Mayer-Schonberger, Professor of Internet Governance and Regulation from Oxford and Kenneth Cukier from The Economist introduce Learning with Big Data by way of the role of machine learning at Stanford. The course is taught by Andrew Ng, cofounder of Coursera.

Ng has brought to the globe the ability to teach a world class curriculum in machine learning from California to students in Tibet. In many ways this very idea is threatening to close minded administrators sitting in their siloed office.

The focus in this special book is how big data, which reveals to educators what works and what does not is reforming education. The ability today to interactively track the performance of each individual student in real time throughout the semester can make a big difference because the data drives how focused, dedicated administrators can more effectively budget extremely tight dollars in guiding a campus forward.

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Latest read: Too Big to Ignore

Phil Simon’s book Too BIG to IGNORE: The Business Case for Big Data is another well written primer for Big Data in business. The focus is NOT the programmer’s dilemma of Hadoop vs. NoSQL vs NewSQL. Simon richly documents how Big Data has forever changed business and society.
Too Big to Ignore The Business Case for Big DataThe core message: for the first time we have a very inexpensive ability to data mine a wealth of information. Organizations that tap into this new capability can execute their business more accurately than ever before. However Simon also acknowledges the obvious fact that companies must understand the format of their data while the leadership understands the benefits of pushing big data solutions throughout the organization.

Too Big to Ignore begins by telling about the success of MoneyBall by noted author Michael Lewis. Too Big to Ignore reveals how sports franchises exploit data to win on the field and for the worse how Las Vegas casinos have been using A/B testing to pinch every penny out of their best customers. Probably the best but sad example. Similar to the approach by other authors addressing Big Data the focus is on the explosion of data from Google, Facebook, Apple and Netflix as the smartphone and wireless technologies began to change society in ways that drive unstructured data well beyond traditional structured environments.

Simon walks through the history of big data from Web2.0 to Predictive Analytics and touches The Internet of Things. Simon address the tool Hadoop lightly enough as to not scare off any non-database programmer to understand how this free tool is used by Yahoo, Google, Facebook and Apple just to name a few successful highly profiled companies. The growth of Hadoop and the emerging role of Cloudera is addressed in greater detail for the non-technical audience.