Featured Latest News

Default Image

Scrap the Annual Performance Review for This....

In 2015, Deloitte revamped its performance management system after revealing they were spending…

Winners Circle

Learning! 100

Enterprise Learning! Conference Call for Papers Open

The 7th Annual Enterprise Learning! Conference is now accepting submissions for the September… Read more...

Learning! 100 Award Deadline Extended to Feb. 28th Due to…

Learning! 100 Award Honors Top Global Learning Organizations Learning! 100 Award call for… Read more...

7th Annual Learning! 100 Award Call for Applications Opens

Award Program Recognizes Top 100 Global Learning Organizations Elearning! Media Group, publishers… Read more...

Best of Elearning!

99 Solutions Named Best Of Elearning!…

The 2016 “Best of Elearning!” awards honor best-in-class solutions across the learning and technology marketplace. Celebrating their 12th year, these honors are bestowed across 27 different… Read more...

The Best of Elearning! 2016 Finalists…

Executives Nominate 99 Solutions Across 29 Categories for Excellence Elearning! and GovernmentElearning! magazines, the industry voices of the enterprise learning and workforce technology market,… Read more...
Default Image

Call for Learning! Champion Nominations

Apply by Dec. 1 for consideration Elearning! Media Group hosts the second annual Learning! Champion Awards recognizing individuals for exceptional contributions to the industry. Nominees can be… Read more...

Amazon is using Alexa to compete against all of the other retailers on the planet and Google Home. Tesla’s A.I. downloads updated geo-intelligence to compete against all the other car brands that don’t update via the Cloud. IBM’s Watson is automating decision analysis that competes with clinics and hospitals not enabled by its cognitive computer. “This is just the beginning of the A.I. Wars,” says James Canton, futurist.

Companies that are using A.I. to compete will shape the future of A.I. There are companies using A.I. for diagnosing disease, deciphering law, designing fashion, writing films, drafting music, reading taxes or figuring out if you’re a terrorist, fraudster or threat. A.I. is everywhere.

You are exposed to A.I. in real-time if you are within sight of video camera, cell phone, driving a car, traveling by transit, in the city, online or offline claims Canton.

“Here’s a forecast—every job a human can do will be augmented by (increased intelligence assets) and possibly replaced by A.I. Companies will use A.I. to outcompete other companies. A.I. augmented humans will outcompete the Naturals—humans not augmented by A.I.,” predicts Canton.

—Source: Institute of Global Futures www.globalfuturist.com

Published in Trends

Learning Tree International has been awarded a five-year contract by the NATO Communications and Information (NCI) Agency as its strategic learning partner for the provision of training solutions for up to 33,000 NATO staff across 28 European and North American countries. Learning Tree will provide training in project, program and portfolio management; cyber security; service management; and technical I.T. to support NATO in remaining resilient through continuous, rapid innovation.

Under the contract, Learning Tree will provide commercial training services to the NCI Agency, plus the wider community of NATO agencies. The training will be delivered through a combination of on-site courses provided at over 40 different NATO locations, including permanent classrooms on NATO sites, fully equipped by Learning Tree; at publicly scheduled training events in Learning Tree Education Centers; and through AnyWare — Learning Tree’s virtual learning environment.

Published in Deals

Artificial intelligence (A.I.) is playing a bigger role in our every- day lives, but how are enterprises adopting this technology? En- ter cognitive computing and predictive analytics, and so much more. According to the National Business Research Institute, A.I.’s most important benefit is the ability to predict future tasks (38%) and automation of tasks (27%).

—Test your AI knowledge at: https://www.emarketer.com/quiz/artificial-intelligence?ecid=1014#/q/1

 

Published in Latest News

The top consumer technology transformations were revealed by Dr. Shawn Dubravac, Research Director, Consumer Technology Association. These trends will impact enterprise technology over the coming months and years.

1. The Next Computer Interface is Voice

The word error rate is now at human parity, meaning the graphic user interface will disappear ushering in an era of faceless computing. Voice will be the command function for digital devices including robotics, A.I., etc.

2. Increasing Intelligent Systems  will Connect Diverse Objects

Software is now found in hardware out of the box, and hard- ware is eating software. Alexa is found in refrigerators and automobiles. While google’s newest smartphone is embedded with AR/VR software out of the box.  “This creates a ‘physical manifestation of data’ in our lives,” says Dubravac.

3. Transportation Transformation

The self-driving car was the catalyst for intelligent systems. With connected systems reporting to other cars, the vehicles can respond and react without human intervention. This is a model of many “robotic” type activities that may complement or replace human interactions.

4. A.I.’s Infusion into Real Life

Blending data from diverse devices is improving signal, functionality, and recommendations for users to follow. Hub de- vices will be used for vocal computing. A.I. will boost informa- tion processing geometrically. For example, Google cars have already driven more miles than a human can in 75 years. The speed, experiences, data collection and sharing has increased geometrically.

5. Digitizing the Consumer Experience

From wearables to smart home, online and mobile characterizes consumers tastes. Drone purchases reached 1.1 million units in 2016; VR 700,000 units; smart watches 5.5 million units and fitness trackers 12.6 million.

—Source: CTA.tech  bit.ly/CES2017TRENDS

Published in Insights

BY PRADEEP KHANNA

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play?

Yes! We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

Just like virtual reality (VR), AI has been around for quite some time. In fact, I remember taking AI as a subject when doing my second master’s degree in computer science at University of Technology in Sydney 17 years ago. So, why so much fuss about AI now? AI will reshape how we live and work, but will AI also reshape the way we learn?

ABOUT BOTS, CHATBOTS, TEACHERBOTS AND A.I.

A bot is software that is designed to automate repetitive tasks. Bots have been around for quite some time. An example is use of bots for searching and cataloguing Web pages for search engines. Another example is shopping bots which pull out prices of an item from different vendors from the Internet. Some recent examples are bots making dinner reservations, adding an appointment to the calendar, or fetching and displaying information.

Chatbots are bots that conduct a conversation mirroring potentially a real-life conversation. Chatbots can either be simple rule-based or more sophisticated AI-based. AI-based chatbots get smarter as more interactions take place. The popularity of messaging apps has been lifting the demand for chatbots. Another way to look at the rise of chatbots is the user migration from Web to apps and now from apps to chatbots.

What are teacherbots? Just like a bot or a chatbot, a teacherbot can be a simple rule-based or smart AI-based. Simple rule-based teacherbots can automate simple teaching tasks whereas a smart AI-based teacherbot can become a teaching assistant (TA). A yet smarter AI-based teacherbot can also be personal tutor.

TEACHERBOTS AT WORK

There are two well known instances of teacherbot pilot projects at the University of Edinburgh in the U.K. and Georgia Tech in the U.S.A. The University of Edinburgh teacherbot project was led by its School of Education in collaboration with the School of Informatics and the Edinburgh College of Art. It was launched in April 2015 by Siân Bayne, professor of Digital Education. “Botty,” as this teacherbot was affectionately called, was created to engage on Twitter with students of Edinburgh’s e-learning and Digital Cultures MOOC on Coursera. This MOOC has had 70,000 enrollments across three course runs. The teacherbot’s primary role was to act like a TA. It could answer simple questions on deadlines, course content, etc. It was also able to answer some complex questions as well, based on AI that had been developed on stored tweets with Twitter hashtag #edcmooc. In “Botty’s” case, the students were aware that a teacherbot and not a human being was answering their questions.

Georgia Tech’s teacherbot was developed by Ashok Goel, a professor of Artificial Intelligence at Georgia Tech. Typically, the 300 students at Georgia Tech’s online AI course posted around 10,000 messages in online forums during a semester. Many of these questions were repetitive in nature. This was enough of a driver for Ashok to initiate work on the teacherbot. Leveraging IBM Watson’s technology platform and a databank of 40,000 questions and answers from past semesters, Ashok developed the smart AI overlay for the teacherbot, calling it “Jill Watson. The students were not told that TA “Jill Watson” was a teacherbot.

“Jill Watson” was launched in Jan 2016. As expected, its responses were not very accurate in the beginning, so responses were moderated by the human TAs before posting in the online forums. But by April, it had become sufficiently “intelligent” to answer the questions without human intervention.

The table on the following page compares the two pilot teacherbot projects.

ELM March A.I. Already Reshaping

Many factors determine the accuracy level of any AI project, including the AI technology layer at the infrastructure level, the size of the database, and the contextualizing smart AI layer. Looking at the above comparison between the two projects, the Georgia Tech project does have an advantage of using IBM Watson as a technology platform and having a database of 40,000 questions and answers from previous courses. No wonder, it performed better.

AI NEXT IN LEARNING?

The potential of AI to disrupt education and skills training sectors is immense. As Microsoft’s Bill Gates remarked sometime back, we already have online tutoring services where humans provide the services while the platform is online. Smart AI-based teacherbots can replace the humans to provide personalized learning. This has special relevance in lifelong learning scenarios where we will be dipping in and out the learning continuum all through our life.

Automated assessments are a natural application of AI in education and skills training. This application gets further amplified when  large number of assessments are being done in an online environment. Use of technologies like Facebook’s facial recognition technology and proctoring are classic examples.

Are we already there in the brave new world where AI is reshaping the way learn? IN these early days, where we are seeing AI-based projects being rolled out in different parts of the world. In the first instance, the focus appears to be on automating routine teaching tasks. This is akin to the Robotic and Process Automation (RPA) implementation onslaught we are seeing in other industries.

“Jill Watson” is estimated to have taken 1500 hours to develop. When many “Jill Watsons” are produced in 15 hours is when we will see real disruption in education and skills training.

Developments in AI in education and skills training will to an extent follow the developments in AI in general. With all major technology innovators investing heavily in AI, it appears certain that our working and learning will get reshaped by AI in future.

—Pradeep Khanna is founder & CEO of Global Mindset (www.globalmindset.com.au) and Technology-enabled Innovations in Learning & Teaching (TILT). He works on enhancing collaborative learning across boundaries and by leveraging technology. Khanna has also been Global Delivery Leader for IBM GBS Australia/New Zealand. He lives in Sydney, Australia, and can be contacted via email at This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Published in Insights

6 STRATEGIES FOR MAKING YOUR TRAINING STICK - BY DEAN PICHEE, CEO, BIZLIBRARY, INC.

THE SCIENCE OF LEARNING

Many professions use science to improve the outcomes of their work. For example, architects use the principles of physics and math to design buildings that will function safely and last decades or even centuries. Architecture is often equated with art, but it's the science behind the art that truly makes it work.

In much the same way, we as learning and HR professionals need to understand and use our knowledge of the science of learning to improve the outcomes of our efforts in training employees. What does science tell us we should do to improve the way employees learn?

Here are six things you can start doing today:

1. Make learning bite-sized. Use short, relevant, video-based training (microlearning) focused on individual concepts.

2. Space training out over time. Employees should use the information they learn during training within the first 24 hours after the training event and in the days and weeks to come. Time is on our side here!

3. Add post-training reinforcement. Use quizzes, polls, videos and other resources to reinforce key concepts.

4. Mix it up. Combine training of multiple related skills rather than focusing on one skill at a time. Scientists call this learning concept interleaving.

5. Make it difficult. Resist the temptation to make training easy for learners. Challenging them actually increases the learning impact. One of the ways to make it more difficult is to increase the amount of time between testing and retrieval opportunities.

6. Write to remember. Your brain will recognize more of what's important when you write what you learn.

WHAT WORKS?

We call microlearning and post-training reinforcement "Burst and Boost Training." Using a combination of "bursts and boosts" is a proven way to get more ROI from your employee training program. Bursts of microlearning have been proven by cognitive psychology to be the most effective way learners receive information. Cognitive load theory states that we have mental "bandwidth" restrictions. The brain can only process a certain amount of information before reaching overload. To improve training content, chunk it down into bite-sized bursts to lower the cognitive load. Microlearning is very popular today and is a key component of BizLibrary's online training solutions.

Boosts, or post-training reinforcement, has been shown to increase long-term memory. Testing can actually INCREASE learning more than any other study method. Scientists call this idea "The Testing Effect," and numerous studies have shown that long-term memory is increased when some of the learning time is devoted to retrieving the to-be-remembered information. Incorporating tests and quizzes into employee training programs is more than just measuring the amount of learning that has taken place ... it's a critical part of the learning itself. Resist the temptation to skip testing and boost learning!

THE GREAT TRAINING ROBBERY

Stop the great training robbery that occurs when we deliver programs that are too long, too boring and easily forgotten. Microlearning is the first step. It's also crucial to add on-going reinforcement. Think of post- training reinforcement as the deadbolt on the door of your house, keeping the valuable information you're delivering to your employees from being forgotten and ultimately, maximizing the ROI of your program.

Published in Insights

Online learning continued to grow ex- ponentially, partially fueled by com- panies like Udemy, Lynda.com and Coursera. With employers more willing to accept that this type of courseware is necessary, we expect other related trends to emerge. The top five learning predictions for this year are:

1. EDUCATION HACKING

The churn in technology advancement - both software and hardware - leaves a lot of traditional educational facilities in a tough spot. Most times, universities and colleges find that their course- ware is being rapidly obsolesced by new advancements that occur in 9-12 month increments.

An example of this rapid obsolescence can be seen with some of the new cloud computing companies. Amazon Web Services boasted that it has over 700 significant changes to their cloud computing infra- structure each year. That means that if you’re going to participate in that arena, you can’t expect to find that content in traditional degree courses.

2. TECHNOLOGY BOOT CAMPS

These are coding boot camps that compress the learning process into weeks instead of semesters. Their popularity has spread quickly with venues like General Assembly, which has opened up campuses throughout the country to meet demand. 

But don’t count the universities out just yet. Many entities are expected to announce their own versions of these technology boot camps, which offer professional courses versus credential courses to their students. The University of Phoenix has launched one such venture called Red Flint, in Las Vegas, Nevada. You can expect to see them increase that capability as they re-tool to be more responsive to current trends.

3. MICRO-CREDENTIALS

These are non-degree courses that offer expertise in niche areas like technology, but also other areas where there is a shortage of talent. These courses cost a fraction of typical education venues and can be stacked to create a customized educational experience, i.e., the “hacked” education venue.

With more employers warming to online certificates, and people changing jobs more often, expect this particular trend to grow exponentially. In an age where there is continuous change, the need for continuous learning is a foregone conclusion.

4. APPRENTICESHIPS

This is expected to be another area where we’ll see greater growth, as evidenced by the agreement between Amazon and the U.S. Department of Labor announced earlier this year. This particular program announced an apprenticeship program to train veterans for tech jobs at Amazon. One of the unique benefits of this type of program is that the veterans can earn a salary while learning the skills needed for the job. We expect other major software and technology companies to follow this trend.

5. BRICKS AND CLICKS

We see this as a more accepted venue as educators in the corporate space focus on the unique job roles that have to be brought up to speed across their enterprises and ecosystems. While it was pretty easy to dump everything into the classroom venue in the past, the huge economies of blending online training with classroom venues will continue to push this trend further.

We expect that the ultimate solution in the next few years will be the enactment of the 20/80 model. That model suggests that 20% of the training will occur in the classroom, while 80% of the training is being provided by a combination of online and embedded learning - the latter of which is training within an application, or like in the Amazon apprenticeship program, right on the warehouse floor where employees can access the training at the point of need.

Published in Ideas

Economists have predicted that a rapid period of innovation follows an economic downturn. We are in that innovation cycle. We once could count on an obsolescence cycle of 24 months (thanks to Moore’s law), which was condensed to six months (the life of a smartphone). Now, we are learning and evolving instantly thanks to A.I. and machine learning.

In 2017’s Annual E-learning User Study conducted by Elearning! magazine, 65.6% of respondents are using machine learning today, and 46.9% are planning to purchase. Artificial intelligence is deployed by 31.8% of respondents with 72.7% planning to deploy over the next 12 months, a 228% compounded annual growth rate (CAGR). Augmented reality and virtual reality are close behind with 68.6% and 67.6% planning to deploy. (See article E-learning User Study.)

These advancements are transforming our practices, ecosystems and knowledge base. In the article titled, “Three Disruptive Macro Trends Shaping Enterprise Technology,” we tapped Gartner and leading learning technologists to share their insights and implications (see article Disruptions in Enterprise Technology). Dr. Shawn Dubravac from Consumer Technology Association also identified five transformational technology trends (see article 5 Transformational Technology Trends). Pradeep Khanna also shares his views on A.I. in learning (see article The AI Effect: Are You Ready), and Joe DiDonato makes five learning predictions for 2017 (see Top 5 Learning Predictions for 2017). All conclude that technology’s rapid evolution is spurring transformation at home and at work.

Nothing is gained without the steadfast commitment by our peers, partners and technologists. Elearning! magazine recognizes 28 Learning! Champions who have made extraordinary contributions to the learning industry. Three professionals earned our Lifetime Achievement Award: Elliot Masie, Kevin Oakes and Joe DiDonato. We are honored to feature all 28 thought leaders, trail blazers, innovators, mentors, and high performers inside (see article 2017 Learning! Champion Awards). You will hear from these champions across the year via articles, conferences, Web seminars and blogs. The 2016 Learning! Champion, Dr. Christopher L. Washington, shares his article titled “The Evolution of E-learning and Learning Analytics” on article The Evolution of E-learning and Learning Analytics  .

Thank you to all the learning professionals, technologists and colleagues who continue to advance learning everywhere.

Let’s keep learning!

—Catherine Upton, Group Publisher

Published in Ideas

Every day the enterprise learning ecosystem becomes more complex making a few questions even more important for learning and development leaders. What is the current state of the training function in your organization large or small? How do you evaluate the effectiveness of your training?

Only 8% of CEO’s in LinkedIn’s 2017 Workplace Learning Report say they can see a measurable impact from their company’s Learning and Development. These CEO’s are getting quantifiable activity data from other business functions, so why not L&D?

Chances are, your learning has now spilled out of the confines of an LMS, and touches a TMS, HRIS etc. You may have many of these systems in your organization along with new 3rd party providers, self-directed learning, or apps and portals available to your learners. You are probably spending L&D budget on micro-learning, self- paced learning, gamification, mobile, and more. Surveying aside, how effective are those new initiatives and training techniques? Are you able to track anything more than completions? Are you even able to track completions?

The first step to providing measurable impact is to baseline the effectiveness of your current training by getting better interaction data wherever learning occurs. You can baseline ALL of your current training across multiple learning technologies and you can start today.

It is relatively easy to get all of your training initiatives reporting better learning activity data in the form of Experience API (xAPI) activity streams to a Learning Record Store (LRS). Think of xAPI as a digital mesh that will get all of your proprietary learning technologies talking in the same analytics language. You can mine xAPI activity streams for patterns and react to them. You can keep your LRS data totally anonymous if you would like. xAPI is also technology agnostic so when you add new technologies or remove technologies within your ecosystem it is non- disruptive to your learning activity reporting. But most importantly, an LRS will provide you the learner activity data for formative and summative evaluation.

BENEFITS OF LRS:

1. Baseline your current training with better evaluation data.

2. Begin to build learner competency and performance profiles.

3.  The proper implementation of xAPI/LRS is the first step toward:

  1. Intelligent/Automated Tutoring
  2. Adaptive Learning
  3. Predictive Analysis
  4. Sustainment and Improvement of Training Systems

 

The path to modern training technology and the future of learning starts with xAPI and the implementation of a Learning Record Store. At Riptide, we have been working and engineering learning technology using xAPI since just after it’s inception. Before it was even called xAPI we were generating activity streams to early versions of our LRS, which is now our Storepoints LRS product. We are on the workgroup that created xAPI 1.0 and we are working with it daily.

Interested in learning more on how a Learning Record Store would work within your unique learning ecosystem? Visit RiptideLearning.com and request a free consultation today!

—Nick Washburn is Director of Learning at Riptide Software. Contact him at nick.washburn@ riptidesoftwarecom

Published in Ideas

Artificial Intelligence, Machine Learning, Intelligence Systems. These applications are transforming business, and the enterprise technology an platforms to support them. By Catherine Upton

The digital evolution is changing how business is done. This is the era of impassioned CEOs and technology leaders with creative ideas who can inspire their organizations and lead them in transforming into digital businesses.

"The learning ecosystem is going through a technical disruption to automation and autonomous learning programs in the corporate space. Reminiscent of the shift from contact management software to sales force automation software or email marketing to marketing automation, the learning stack is the laggard to be re-invented and adopted, says Rory Cameron, General Manager, Litmos by Callidus Cloud.

In a Gartner report titled, “Top 10 Strategic Technology Trends” authored by David W. Cearley, Brian Burke and Mike J. Walker, there are three macro trends leaders must embrace to enable a shift to the digital enterprise.

MACRO TREND 1:ALGORITHMIC BUSINESS  DRIVES TRANSFORMATION

Algorithmic business is an accelerator and extension of digital business, according to Gartner. It focuses on how increasingly intelligent algorithms enable smart machines and systems to become autonomous actors in the digital business as agents for human beings. Algorithms drive the connectedness among people, things, businesses and information that drive business value. Algorithms provide the “intelligence” to get the most out of the connections and interplay between people, things, processes and information. Algorithms also are critical to delivering a differentiated customer experience. Although big data remains a major concern for CEOs, big data generated as part of the digital business process is of no value in itself. It is only when the organization shifts from a focus on big data to “big answers” that value begins to emerge.

"Forward-thinking learning profes- sionals and learning technology providers have long recognized that we are amassing a significant amount of data on learners, reports Chip Ramsey, CEO, Intellum. “From the corporate perspective, the enterprise should already be drilling down to the individual employee to determine which learning asset positively altered which specific outcome. On the learning technology side, we should be leveraging the tremendous amount of anonymous user data within our reach to identify learning trends that impact performance. But these are still ‘fixed’ approaches by which learning technology providers, and our clients, are making decisions."

Analyzing big data to identify patterns and insights that drive business actions is the start of this shift, according to Gartner. Algorithmic business transformation occurs when organizations encapsulate these insights into algorithms tied tightly to real-time business processes and decision-makers, and when they use machine learning to allow increasingly autonomous algorithmic action. Algorithms are more essential to the business than data alone. Algorithms define action.

Algorithmic business extends beyond data and analytics to influence the evolution of applications, business models and future digital business solutions. This is ushering in a post-app era in which system and application vendors such as Microsoft, Google and Apple are likely to deliver platforms and applications with ever-more powerful agent- based interfaces.

Intellum’s Ramsey continues: “As business sectors across the board, including learning, continue to apply machine learning techniques, these traditionally fixed algorithmic approaches are themselves learning. At Intellum, we are already testing a solution that presents the exact information the user needs to consume at the moment in which that presentation has the highest likelihood of improving that employee’s performance. The algorithms that control this approach are not static equations but processes that learn from large numbers of prior successful outcomes to better determine who needs what, when.”

Algorithmic business builds on digital business, shifting the emphasis to the intelligence encoded in software, according to Gartner. Enterprise architects must add algorithmic business and related enabling technologies to their planning and future enterprise, data, security and application architectures.

IBM’s acquisition of The Weather Company is an example of algorithmic business. The Weather Company has a massive Internet of Things (IoT) implementation, with hundreds of thousands of weather sensors sending 28 billion transactions to its Cloud every day. Before the acquisition, IBM had an agreement to feed data to IBM Watson for weather prediction. With the acquisition, IBM brings together The Weather Company’s digital environment and associated data with IBM’s analytical and cognitive computing capabilities. This has created an algorithmic business that provides analytical services and results to a business ecosystem with more than 5,000 customers. These customers — in, for example, airlines, insurance companies and retailers — can use the algorithmic input to drive their own business operations.

Organizations must examine the potential impact of these macro trends, factor them into their strategic planning for 2017 and 2018, and adjust business models and operations appropriately. If they fail to do so, they will risk losing competitive advantage to organizations that do. {See Figure 1}

ELM March Disruptions 1

Ramsey concludes: “The algorithm that learns how to present the right information to the right person at the right time is beyond valuable. It will fundamentally transform the company that learns to harness it. Imagine the competitive advantage gained when the learning solution recognizes in real time an opportunity to intercede and present the user with information (a new sales technique) that turns an otherwise negative outcome (lost sale) into a positive one (closed sale). This is not an imagined future state. Companies like Intellum will be providing this competitive advantage to clients within the year.”

MACRO TREND 2:THE EMERGENCE OF THE DIGITAL MESH

Gartner defines the “economics of connections” as the creation of value through increased density of interactions among business, people and things. As an organization increases the density of its connections (among people, business and things), it increases the potential value it can realize from those connections.

Connections are at the core of digital and algorithmic business models. The digital mesh builds on the economics of connections, focusing on devices, services, applications and information. The digital mesh is a people-centered theme that refers to the collection of devices (including things), information, apps, services, businesses and other people that exist around the individual. As the mesh evolves, all devices, computers, information resources, businesses and individuals will be interconnected. The interconnections are dynamic and flexible, changing over time. Building business solutions and user experiences (UXs) for the digital mesh — while addressing the challenges they create — must be a priority for enterprise architects.

“This concept of a digital mesh that is made up of all the devices and digital applications that are tracking every aspect of our lives is very applicable to enterprise learning," claims Ramsey. “In a corporate environment, we use applications to manage projects and relationships, receive customer feedback, and control versions of critical documents and code. We interact with these applications across a number of devices from a number of locations. The things we rely on to get our jobs done are actually gathering data about how well we do our jobs.”

The digital mesh has emerged as a re- sult of the collision of the physical and virtual worlds, as computing capability becomes embedded in virtually everything around us. Additional advances allow the virtual world to enter the real world through advanced UI and virtual reality models, as well as physical items created with 3-D printers. This blending of both worlds delivers new insights into the physical world, allowing us to understand it in greater detail, and interact with it in new and intelligent ways. This will change how people experience the world in their daily lives. Opportunities for new business and operating models will abound.

Ramsey adds: “At Intellum, we can already mine this data from a range of devices (think Fitbit) and applications (think Salesforce) to determine employee performance levels. We can now experiment with how well specific inputs, like a mid-day walk or a two-minute video on how to become more persuasive, can alter an outcome or improve an employee’s performance. Once these feedback loops are in place, particularly at scale, we can apply the algorithms that will determine the exact learning asset an employee should encounter in a specific scenario. This will, of course, require even more data from even more sources, and the digital mesh will continue to grow.”

MACRO TREND 3:SMART MACHINES SET THE STAGE FOR ALGORITHMIC BUSINESS AND THE ALGORITHMIC ECONOMY

The smart machines theme describes how information of everything is developing to extract greater meaning from a rapidly expanding set of sources, reports Gartner. Advanced data analysis technologies and approaches are evolving to create physical and software-based machines that are programmed to learn and adapt, rather than programmed only for a finite set of prescribed actions.

The amount of big data collected by the many devices currently in place is staggering. However, the accelerating merger of the physical and virtual worlds will make the present volumes seem paltry. New kinds of data will continuously stream from new types of devices at record rates. This oversupply will overwhelm those who are ill-prepared. But for those who are prepared, the potential to gain new kinds of critical intelligence will be unprecedented. Leading senior executives will build a strong competency in turning this data into critical intelligence that will drive their organizations’ future direction. Additionally, leading organizations will significantly advance operational agility with near-real-time information, feeding business processes that can absorb it and react accordingly. Data coming from almost all directions provides the possibility for intelligence everywhere when combined with advanced artificial intelligence algorithms and other machine-learning techniques.

Three distinct trends are intimately linked in the smart machines theme. They represent an evolution in how systems deal with data, and the machines and people that create and consume this data, culminating in intelligence everywhere. {See Figure 2}

ELM March Disruptions 2

“These three macro trends are substantiated by what we have seen in the financial trading arena," says Apratim Purakayastha, CTO, Skillsoft. “For some years, sophisticated algorithms have taken over trading decisions. Those algorithms are connected in a mesh, taking decisions and automatically trading across firms — and those ‘smart machines’ — have set the stage for a mostly automated algorithmic business. There are other areas, such as supply chain management, where this trend is currently growing. In the area of digi-tal advertisement, we can also see this trend dominating. Overall, it is already a broad, cross-industry phenomenon.

Even everyday objects such as a stethoscope and enterprise software such as CRM systems or security tools increasingly have a smart and autonomous aspect. In “Top 10 Strategic Technology Trends: Autonomous Agents and Things,” Gartner looked at how information of everything and advanced machine-learning algorithms, supported by advanced system architectures, are leading to more intelligent software and hardware-based solutions. These are creating new market segments and enhancing existing ones.

“The pervasive nature of these trends demands that everyone understand what comprises a 100 percent digital workforce — a workforce that is fully trained and conversant with fundamental digital skills, along with its benefits and risks,” adds Purakayastha.

The key digital skills sets required include but are not limited to:

>> Broad digital skills such as productivity and collaborative tools.

>> Modern technological trends such as Big Data, Blockchain, etc.

>> A thorough understanding of fundamental cybersecurity issues such as phishing, ransomware and other risks

>> Best practices and laws relative to digital compliance and data privacy

>> Digital “presence, leadership and image in a virtually interconnected workforce.

—This article contains excerpts from the Gartner Research Report titled “Top 10 Strategic Technology Trends” by David W. Cearley, Brian Burke, Mike J. Walker. To access the complimentary Gartner report, download it at: http://gartnerevents.com/ Top_10_Strategic_EMEA?ls=ppcggle&gclid =CJiMlrSN184CFVAo0wodWdQNkQ

Published in Top Stories
Page 2 of 109

 


You are now being logged in using your Facebook credentials