Smartphones Will Offer Personalized Learning and Decision Support

Employees Can Access Information On The Spot Through Their Mobile Device

Mobile Strategy Comments (1)

Smartphones are not smart to possess a great deal of intelligence.

It only calls them smart in relation to “feature phones,” which is the name given to basic mobile phones of the previous generation.

I considered smartphones smarter than feature phones because they could connect with the Internet; do basic computations so that one could play games; stored larger amounts of data such as contact lists and simple documents; and could use such affordances as geolocation, accelerometers, and sensors to carry out functions not available in simpler phones.

But they’re not “smart” in how the next generations of smartphones will be.

Smartphones within the next five years will have much more powerful and faster CPUs, allowing them to process large amounts of data using very sophisticated algorithms.

And, they will learn as they interact with their owners in the world around them. They will have lots more memory and will be networked to supercomputers like IBM’s Watson, which is already being used for medical diagnoses delivered to doctors’ tablets and mobile phones.

In a 2013 article in Supply and Demand Chain Executive, Yan Krupnik suggested that in-store sales staff will soon be able to use “retail predictive analytics” to combat the growth of online stores using mobile devices.

This set of technologies includes:

  • Dynamic Data-Driven Decision Support,
  • Advanced Demand Forecasts,
  • Predictive Pricing Strategies, and
  • Inter-store Inventory Balancing.

These new technologies are only possible because we’ve entered the era of “big data” with a gigantic leap in the amount of data available to organizations they can use to analyze and predict behaviors of both staff and customers.

As Google CEO Eric Schmidt told the audience at the 2010 Techonomy Conference, in Lake Tahoe, “between the dawn of civilization through 2003, there were just 5 exabytes of information created. That much information is now created in two days, and the pace is increasing. People aren’t ready for the technology revolution will happen to them.”

Not only is the amount of data rapidly increasing, but new types of data are becoming available. These include data on location, orientation, movement, activity levels, spending patterns and data on how mobile devices are being used. All this data has given rise to new methods of data analysis both for retailing and for learning.

New mobile applications are in development that use a combination of big data and artificial intelligence to supply “smart tools” to retail sales staff.

These include such applications as the Emotient App for Google Glass that will detect a customer’s emotions and feed them live back to sales associates, product recommendation engines, and predictive software that will recommend products to be offered to customers, perhaps before they even know of what they need or want.

These developments are already happening.

In 2012, the online tech magazine  GigaOM reported that “WalmartLabs is building big data tools — and will then open source them.” Walmart is planning to merge 10 different data platforms into one, to have a single big data warehouse to analyze and predict customer information on a worldwide basis.

A variety of learning analytics methods have been developed over the past decade with the use of computers and learning management systems. These methods have included:

  • Training analytics – using Kirkpatrick’s levels of training evaluation as an analytical framework
  • Learning analytics – the results from tracking and reporting by learning management systems
  • Human capital analytics – the results from talent management and HR data
  • Performance analytics – analysis of how well people do their jobs or perform specific tasks

There are many changes coming in learning analytics. With big data, new methods of learning analytics are being developed and include (in order of complexity):

  • Sentiment analytics – analysis of feelings of a large population based on what they say or do
  • Visual analytics – turning large amounts of data into pictures to make them easier to understand
  • Entity analytics – a new data analysis that allows algorithms to identify people (entities) and their relationships
  • Predictive analytics – analysis that allows forecasting from large data sets
  • Prescriptive analytics – analysis that suggests solutions to problems and assists in making decisions

The result of all this analytical capability will be an increased ability to offer personalized learning information and decision support to employees on the spot as they need it, accessed through their favorite mobile device.

Float is a recognized expert in new technologies for training and business. Please contact us to discuss where your company is going.

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Gary Woodill is a senior analyst with Float, as well as CEO of i5 Research. Gary conducts research and market analyses, as well as assessments and forecasting for emerging technologies. Gary is the co-editor of "Mastering Mobile Learning," author of “The Mobile Learning Edge,” and the co-author of “Training and Collaboration with Virtual Worlds.” He also presents at conferences and is the author of numerous articles and research reports on emerging learning technologies. Gary holds a doctor of education degree from the University of Toronto.

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On October 9, 2014
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One Response to Smartphones Will Offer Personalized Learning and Decision Support

  1. Interesting article! With the emerging trend of BYOD, smartphones and tablets have made their way into the world of corporate training as well. It is safe to say that BYOD is changing the face of corporate learning to a large extent. Here is a recent article that we published on our blog that shares our thoughts on BYOD and the benefits that organizations enjoy by encouraging the trend – hope this sheds some light.

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