Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Irving Wladawsky-Berger

There are many tasks for which machine learning methods aren’t applicable given the current state-of-the-art, such as the analysis of data derived from human behavior. who is likely to try this newly-launched product?;

The Competitive Value of Data: From Analytics to Machine Learning

Irving Wladawsky-Berger

Business analytics predates the big data era. Since the early days of IT, companies have been using their transactional data to improve logistics, inventory management, sales analysis and fraud detection.

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Reflecting on Learning Analytics and SoLAR

George Siemens

The Learning Analytics and Knowledge conference (LAK16) is happening this week in Edinburgh. I have great hope for the learning analytics field as one that will provide significant research for learning and help us move past naive quantitative and qualitative assessments of research and knowledge. Would you be interested in participating in a discussion on educational analytics (process, methods, technologies)? Not everyone was a fan of the idea of learning analytics.

Impact of Data Analytics in Today’s Digital Workplace

elsua: The Knowledge Management Blog

So, how about if I share with you all how I got involved into the space of data analytics for online collaborative environments in the first place? Anyway, back to data analytics. Observable work through data analytics, anyone?

Productivity tools for the networked workplace

Harold Jarche

These tools are ubiquitous in business and government, so I have agreed to write a few articles on how they can be used to improve work productivity. IT has to make us more productive, smarter.”. Office 365 productivity tools from Harold Jarche.

Become a More Productive, Empathetic, Creative Person With the Help of AI-Based Tools

Irving Wladawsky-Berger

Despite dramatic advances in technology, most of the world’s economies have been stuck in a long period of slow growth and slow productivity. We’ve long been leveraging technology to help increase productivity.

Reflections on Innovation, Productivity and Job Creation

Irving Wladawsky-Berger

I have also been thinking a lot about the impact of these innovations on the productivity of the service sector of the economy in general, and in particular, on the kinds of jobs that we can expect to be created over the next decades to replace those jobs that might decline or disappear as a result of such productivity improvements. Although there are numerous technological innovations that have improved the productivity of services over the past century (e.g.,

Digital Analytics Basics: Free Online Academy from Google

Beth Kanter

For many nonprofits, Google Analytics is the tool of choice for measuring traffic, reach, engagement, and action primarily because it is free. While it is somewhat easy to use, some of the more valuable features may require a little tutoring to understand and use.

Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

Irving Wladawsky-Berger

The paper is particularly focused on machine learning and related technologies, and is based on a detail analysis of more that 400 use cases across 19 industries and 9 business functions. The performance of traditional analytics tends to plateau as the data set size increases.

A Concise and Brilliant Peer-Reviewed Article on Writer's Block.

Bob Sutton

James Adams: Good Products, Bad Products: Essential Elements to Achieving Superior Quality. Davenport: Competing on Analytics: The New Science of Winning. It was published by Dennis Upper in the Journal of Applied Behavioral Analysis in 1974, and is funny, true, and inspired -- and a great demonstration that "brevity is the soul of wit." Bob Sutton. About Subscribe to this blogs feed Email Me Follow Me @ work_matters. Book Me For A Speech. Brightsight Group.

Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

Irving Wladawsky-Berger

The paper is particularly focused on machine learning and related technologies, and is based on a detail analysis of more that 400 use cases across 19 industries and 9 business functions. Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding. And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. .

Becoming a Data-Driven Business: Challenges and Opportunities

Irving Wladawsky-Berger

A few weeks ago, McKinsey published The age of analytics: Competing in a data-driven world , a comprehensive report on the state of big data, and in particular, on the challenges and opportunities a company faces as it strives to become a data-driven business.

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What is stopping companies from measuring learning: Skillsets, datasets, toolsets or mindsets?

Xyleme

In one of our recent posts, " Why you need to take a Google Analytics approach to measuring learning ," we lay the foundation of our perspective regarding L&D''s constant battle for greater relevance in business strategy and planning.

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

Irving Wladawsky-Berger

A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture.”. AI is now seemingly everywhere.

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Can AI Help Translate Technological Advances into Strategic Advantage?

Irving Wladawsky-Berger

The IT industry has long been associated from what’s been called the Solow productivity paradox , in reference to Robert Solow's 1987 quip: “You can see the computer age everywhere but in the productivity statistics.

How to Support the Widespread Adoption of AI

Irving Wladawsky-Berger

The advent of PCs in the 1980s then made it possible to apply IT to front-office processes and applications, such as word processing in office systems, spreadsheets in data analysis, and customer support. But during this period of rapid IT growth, US labor productivity grew at only 1.5%

Imagination, Creativity and Related Subjects

Irving Wladawsky-Berger

The book explored the essence of innovation in new product development by examining a few truly novel products in different market areas. They concluded that innovation involves two fundamental processes: analysis and interpretation. .

As Big Data and AI Take Hold, What Will It Take to Be an Effective Executive?

Irving Wladawsky-Berger

Big data , powerful analytics and AI are everywhere. Many of these jobs, - which include blue-collar production activities as well as information-based white-collar ones, - are based on well understood procedures that can be described by a set of rules that machines can then follow. .

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Learning to Apply Data Science to Business Problems

Irving Wladawsky-Berger

This past semester I was involved in an interesting course at MIT’s Sloan School of Management , - Analytics Labs (A-Lab). A-Lab’s objective is to teach students how to use data sets and analytics to address real-world business problems.

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The Rise of the T-Shaped Organization

Irving Wladawsky-Berger

A growing number of articles have been extolling the value of T-shaped professionals , that is, individuals who combine deep cognitive, analytical, and/or technical skills in a specific discipline, with broad multidisciplinary, social skills.

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Based on a study of over 150 AI-based projects, the authors found that AI can play a major role in three important business needs: advanced process automation, cognitive insight through data analysis, and cognitive engagement with customers and employees.

Data 207

Skills and Jobs in the Digital Economy

Irving Wladawsky-Berger

The digital revolution has yet to fulfil its promise of higher productivity and better jobs,” said The Economist in a special report on Technology and the World Economy in its October 4th issue. “The The analysis was repeated for the 2014 occupations, yielding 5 distinct skill factors: .

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Pirates Play Databall, Purge Postseason Drought

Andy McAfee

It was built around data, analysis, and organizational change. The way to do this, they concluded, was to start really listening to the geeks the team had hired: Before… Huntington…, the organization did not have an in-house analytics department. Analysis is Better.

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Anticipating the Future of the IT Industry

Irving Wladawsky-Berger

Let me share some of my personal impressions of this journey through the lens of three key areas, each of which has played a major role throughout IT’s history, and will continue to do so well into its future: data centers, transaction processing, and data analysis.

Do you really need all this personal information, @RollingStone?

Doc Searls

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My 25 Years of Ed Tech

Stephen Downes: Half an Hour

2000 – Learning Objects Also: Portals, CoI I wrote my paper 'Learning Objects' in 2000 - it was the product of a presentation I did on IMS-LOM a few months earlier, building on a lot of the idea people like Dan Rehak had already developed.

Data Science - the Emergence of a New Discipline

Irving Wladawsky-Berger

At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. Data science goes beyond the use of data mining, business analytics and statistical analysis to look for patterns in large data sets.

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The Evolution of the Internet of Very Smart Things

Irving Wladawsky-Berger

It’s impractical to move all that data to a central cloud for analysis and actions. Connectivity and intelligence are a means to a better product and experience, not an end.” . Most IoT business models also hinge on the use of analytics to sell user data or targeted advertising.

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Adios Ed Tech. Hola something else.

George Siemens

First, Sebastian Thrun, in an Economist article, states: “BECAUSE of the increased efficiency of machines, it is getting harder and harder for a human to make a productive contribution to society” If that is true, why is his startup trying to teach humans? Why not drop the human teaching thing altogether and just develop algorithms for making the stated productive contribution to society? And then to learning analytics.

Mindset and Heartset

John Hagel

In these cultures, the belief is that if you have the right data and perform the right analytics you can deliver whatever is needed. The best way to do this is to simplify things and subject them to rigorous analysis. Many factors are fostering this fear, but one key factor is the mounting performance pressure that is a product of the Big Shift transforming our global economy and society. Is mindset all there is?

Data-Driven Decision Making: Promises and Limits

Irving Wladawsky-Berger

In a recently published article, Data Science and its Relationship to Big Data and Data-Driven Decision Making , Foster Provost and Tom Fawcett succinctly define data-driven decision making as “the practice of basing decisions on the analysis of data rather than purely on intuition.”

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Embracing Disruptive Change - Why Is it So Difficult?

Irving Wladawsky-Berger

Mature companies that once led their industries are too slow to respond to the waves of startups now attacking them with innovative products and services. . In its early stages, it’s not clear how the market for a new product or service will develop and there is little data to analyze.

Change 269

Human-AI Decision Systems

Irving Wladawsky-Berger

Few organizations have applied social network analysis to help them scale the size and expertise of the decision-making group. Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. .

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The 6 capabilities that drive future business value from Staggeringly Enormous Data

Trends in the Living Networks

There does need to be a simple analysis of the potential value versus cost of gathering new categories of data, however as storage costs slide more data domains become viable. Data analytics. Communication of data analysis.

Data 158

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Based on a study of over 150 AI-based projects, the authors found that AI can play a major role in three important business needs: advanced process automation, cognitive insight through data analysis, and cognitive engagement with customers and employees.

Data 163

Increasing “Jointness” and Reducing Duplication in DoD Intelligence

Martijn Linssen

While modest gains have been made in breaking down “stovepipes,” the initial energy of these “collaborative” efforts has waned and increased spending has largely cemented bad habits: siloed analytic reporting that fuels massive duplication of effort.

intangible value

Harold Jarche

It was her work on value network analysis [PDF] that particularly influenced my thinking. Value network modeling and analytics reflect the true nature of collaboration with a systemic human-network approach to managing business operations. Image: Value Network Analysis by Patti Anklam.

Reflections on Big Data, Data Science and Related Subjects

Irving Wladawsky-Berger

CUSP’s research and educational programs are centered on urban informatics , - “the acquisition, integration, and analysis of data to understand and improve urban systems and quality of life.” Big Cities + Big Data and Bringing Urban Data to Life are prominently displayed in its website.

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Rejected: On being disappointed, sorta

George Siemens

However, the focus of this research chair on learning innovation and analytics captured my interest and I decided to apply (obviously informing my colleagues at Athabasca University of my intent. Whether as a startup, in my current role, or something else entirely, the intersection of social media, analytics, new pedagogical models, and networked learning are a lucrative and provocative area of exploration. Corporate partnerships with organizations involved in learning analytics.

Non-Research Citations in the Siemens Research Study

Stephen Downes: Half an Hour

A meta-analysis on the effects of computer-presented feedback on learning from computer-based instruction. Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief (No. Effectiveness of fully online courses for college students: Response to a Department of Education meta-analysis. LinkedIn endorsements turn you into the product.

Relationships and Dynamics - Seeing Through New Lenses

John Hagel

While it would be difficult to summarize Nisbett’s rich analysis, I want to focus on a key distinction that he develops in his analysis of two cultural ways of perceiving our world.    Even in the more contemporary world of social network analysis, this analysis often remains highly static – elegant maps show the rich structures of these social networks as they exist today, but they rarely reveal the dynamics that evolve these networks over time.

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

Irving Wladawsky-Berger

A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture.”. A common theme throughout the report is that the same players who were leaders in the earlier waves of digitization and analytics are now leading in the AI wave. AI is now seemingly everywhere.

Why Do We Need Data Science when We’ve Had Statistics for Centuries?

Irving Wladawsky-Berger

One of the best papers on the subject is Data Science and Prediction by Vasant Dhar , - professor in NYU’s Stern School of Business and Director of NYU’s Center for Business Analytics , - which was published in the Communications of the ACM in December, 2013.