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Sentiment, Sentiment Analysis, Sentiment Tracking have become a hot topic with multiple 'AI' startups focussing on providing sentiment driven insights to enterprises. The number of such startups points to the potential that enterprises see in Sentiment analysis and the impact it has on how the enterprise plans, operates, executes and delivers value. Sentiment Analysis is process of extracting sentiment (emotion or feelings) captured in signals that are embedded in various types of media such as print, text, audio, video, images etc. For example, if a reporter submits a report on a particular enterprise, the sentiment embedded in the article can point to how excited, worried, upbeat or impassive they might are about the enterprise. This sentiment can be then used by the enterprise to understand the perception about the enterprise that the external market carries and whether that perception is improving, degrading or staying unchanged. This insight can be used by the enterprise to improve their go to market plans, change their PR strategy or even go deeper and change their product strategy. Sentiment Analysis Is Not New The tracking, measurement and use of Sentiment is not a new scenario. Enterprises have been leveraging the output of sentiment analysis for a long time. User surveys, focus groups, market research, customer interviews etc. are all examples of generating data to perform and track sentiment. Similarly, influencer marketing through association with influencers or events or organizations with a certain perception or sentiment associated with them is a common technique to improve the enterprise's own sentiment. Sentiment Analysis and strategizing based on the analysis is a common and required function for any enterprise. Sentiment Analysis Using Artificial Intelligence With the advent of Artificial Intelligence (AI), enterprises now have another technique in their kitty to understand how they are perceived in the market and how that perception i

Safety Myths We Could All Live Without Safety MythsI'm going to level with you today: There's a lot, and a mean a metric ton, of misinformation when it comes to safety in the workplace. The more experience you have, the more and more you discover which of the things you've been told actually seem to hold up to the action of a real workplace, and which advice becomes clearly idealistic and less useable. That said, why can't we just skip past the clutter? Why do myths about the nature of safety and accident prevention continue to circulate when experienced practitioners know them to be complete bologna? To be honest, I can't answer that, but hopefully what I can do is more useful; in this article, we're going to go over some common workplace safety myths and dissect them so that, with a whole lot of luck, they won't leave anyone's lips ever again. Accidents Happen Safety Myths AccidentsFirst of all, yes, accidents do happen… but the statement itself is dismissive, and suggests that some things are just out of our control. The problem with this sentiment is twofold. First of all, it's a slippery slope: If you decide one thing is out of your control, it's easier to accept that another thing might be as well. Before you know it, you're leaving too much up to chance and not enough up to your problem solving skills. The second problem is that the idea that accidents just "happen" of their own accord is silly and untrue. There is always something you can do, and you should always be actively improving your safety by finding these things as they come along. One counter example to this that people sometimes come up with is a case in which an employee does something really boneheaded and gets themselves into a dangerous situation. "Isn't that kind of out of my control?" one might ask. Actually, there are two possible situations here: 1. The worker deliberately put themselves in harm's way for some unknown reason. In this case, a resu

Are you AI-First? By editor Posted October 13, 2018 In Artificial Intelligence, Digital Strategy, Technology & Design 0 An AI-First company is an enterprise that has transformed to believe and understand the incredible and disruptive potential of Artificial Intelligence. Such an enterprise not only sees the value but can see the destructive impact of being left behind. An AI-First Company understands that it might not know all the answers but realizes that it needs to seek out a path forward with AI or risk being marginalized. Key Characteristics of an AI-First Company A-First companies might not be any different from their previous form but think and act differently. Here are some key characteristics of such companies. Approach to Problems and Planning An AI-First company evolves its approach to problems. AI-First companies realize that determining the existence of a problem and selecting the most consequential problems is a function of data and analytics. An AI-First company invests in building predictive mechanisms that can signal current or upcoming problems including the severity and priority of these problems. Building these predictive mechanisms becomes the first step in determining how and when to prepare for problems or upcoming issues. In addition, these companies leverage news and information that is generated inside and outside the enterprise as it is generated and ensure that their employees and customers have access to the insights embedded in the news and information. Approach to Products and Product Development An AI-First company understands how a prediction or classification could help them deliver a better solution to a problem faced by their customers and how their existing products can be adapted or new products created that change behavior based on the predictions and classifications. Enterprises that understand the power of AI ensure that data scientists come part of the core product ideation and development team with a heavy infl

So here's how today started out… We got an email from a guy who took issue with one of our recent infographics, 10 Astonishing Facts About Arc Flash. Here's what he wrote: "Have you seen the commercial for the insurance company that uses the premise "If it's on the the Internet, it must be true"? I have still not seen the source of these statistics. The Capschell report is what all of these websites, all of which involved in the business of Arc Flash for profit ($$$$), including yours, use without merit. […] Even NFPA 70E does not not cite the source of the statistic regarding "2000 injuries per day". Don't just give me another website that says that again, show me source (ie the study) that produces those statistics. I am not, by any means, attempting to reduce the importance of arc flash, arc flash studies or PPE. I just have a problem when companies use these types of statistics without regard to the source or veracity of the information. Fear Mongering is the term that come to mind. It's on the internet so it must be true." I believe whole-heartedly that our marketing team did a significant amount of research on Arc Flash prior to creating this infographic, and I don't believe the intent of our marketing team to promote or perpetuate bad information. However, I can understand this person's sentiment, and I wanted to know myself what the real numbers were. So, the hunt for accurate data began. Before I go further, it should be addressed that our infographic says that there are 2,000 injuries each year, not every day. Still, where did this figure come from? I started searching to uncover where the 2,000 injuries per year came from. I asked the marketing team for their source, and they provided me with the official NFPA 70E 2012 Handbook. In Annex K(3), it states plainly that 2,000 people a year are injured as a result of arc flash. But, as our accuser points out, there is no citation. Because this is NFPA, a standardization or

Modern, RESTful APIs are not considered standard, table stakes and expected out of any new project, effort, application, system, service or product. It has become so normal to talk about developer interfaces, developer adoption, application development and innovation in the same breath as APIs that a distinct effort to build APIs for a new product or service seems out of place and abnormal. APIs are the defacto standard of app development. So where do we go from here? We believe that APIs are about to enter the second growth spurt. APIs will evolve from not just interfaces and integration enablers into the rockets that propel enterprises towards innovation and market dominance. Here are three key trajectories that will lead the next API evolution and revolution. Innovation - Starts, and Ends with APIs All modern technologies such as Artificial Intelligence, Machine Learning, ChatBots, Analytics, BlockChain etc. begin and end their stories with APIs. APIs are what enables the communication between front-end user interfaces and the backend technology services. All new machine learning capabilities offered out of the big four tech companies have seen the light of day through APIs. Intent & Sentiment extraction, Topics, Categories, Summarization, Image Recognition, Entity Extraction etc. are all capabilities powered by Machine Learning, Natural Language Processing that is ultimately being delivered as APIs to application developers. Similarly, ChatBots are typically designed to get the user entered text, use an intent API to determine intent and then use a service API to respond to the user conversationally or with a service. Clouds - Multi-Cloud, Hybrid Cloud As the big three cloud providers grow their market share and attempt to attract attention, increasingly, enterprises need to think about how they minimize their risk by building in the flexibility to switch their cloud provider if and when they need. In addition, hybrid architectures or a cloud migration

Lean Six Sigma for Non Profits We always like to hear about people applying business methodologies and mechanics in arguably unorthodox ways or markets, and today I stumbled across a LinkedIn discussion that happens to exemplify exactly that sentiment. User William Falquero seemed to be onto something interesting as he posed a question for other professionals: Could non-profit organizations benefit from Lean Six Sigma thinking? If so, how would they be implemented, what would the challenges of this sort of endeavor be? Only a few users jumped in right off the bat, but, lucky for our purposes today, they were some critical thinkers with some useful insight and examples to offer up. Today, we're going to take an in-depth look at the use of Lean and Six Sigma thinking and tools within non-profit and charity organizations. Are Charities Good Candidates for Lean? One of the first and most important things to establish when considering implementation of Lean in any company or organization is to figure out how they could benefit from the systems. In the case of charities, one user points out that room for improvement from Lean is readily apparent simply in the mindsets of the people who work at them: "The sweat and tears the non-profit workers and volunteers shed and the effort they put forward is focused on those they serve, with less thought on HOW they are served" This is what Richard Petty had to say of an organization he actually had the experience of implementing Six Sigma with. One thing he goes on to highlight and elaborate on is the fact that many volunteers and non-profit workers are so focused on those they want to help that they may get blinded as to the mechanisms that can take them there (and these are the places opportunities for improvement present themselves). Even in other more traditional workplaces, we see this sort of "tunnel vision" as fairly common: It can be hard to get workers to take a step back from the endgame results they're