Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
The raging Australian and Amazon wildfires have raised a burning question for all of us - why the very technology, that has been a major facilitator to human evolution and growth could not predict, manage or control its destruction? To those of us who are in the business of technology, it is time to ask a few tough questions in our boardroom meetings and take ownership of solving the problem. After all, what is growth worth if the planet itself is in peril? As someone who has witnessed the digital revolution unfold, I may not have a full-proof plan to address the climate emergency, in fact, we don't even have the visibility of all evolving technologies that may be required to solve the climate emergency. But, I am clear and convinced that we have to start now and start with the available technologies which in their own right are very powerful and transformational.
Location: US, Central Region OR Toronto, Canada Talend is a 600 employee, recent IPO, big data integration software company with deep open source roots. Well-funded, with over $100 million raised to date and continued rapid growth, Talend is the second largest independent open source company in the world. We are hiring Pre Sales Engineers to continue to build a proactive, customer-facing organization that ensures customers are getting value from Talend's products and solutions. We are seeking Engineers to join the sales team and support the increasing demand from our direct sales. Our portfolio of products has expanded from purely Data Integration to include Data Quality (DQ), Master Data Management (MDM), Enterprise Service Bus (ESB) and Big Data.
Using cognitive, cloud-based solutions -- such as outage prediction models -- gives utilities the opportunity to take a proactive stance against impactful weather. It is critical for utilities to determine the level of impact weather can have on their system and take the appropriate actions in advance of both major storms and everyday changes in weather patterns. This reality introduces a key question for energy providers: How can predictive analytical tools create operational and financial benefits for their organizations?
With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you'd think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.
From minimizing accidents, traffic management, ticketing and preventive maintenance of fleets, AI has the potential to transform the transportation sector. The adoption of new technologies has helped the transportation sector innovate and evolve over the years. Today it is time for the industry to leverage Artificial Intelligence ( AI). AI, a technology that provides machines the ability to think like humans, is transforming the industry. The application of AI in transportation can help the industry in several areas including passenger safety, traffic management, and energy efficiency, amongst others.
In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, May 31-June 4, 2020, we asked Kumaran Ponnambalam, Analytics Architect at Cisco, a few questions about their deployment of predictive analytics. Catch a glimpse of his presentation, Using Association Rules Mining for Segmentation and Profiling, and see what's in store at the PAW Business conference in Las Vegas. Q: In your work with predictive analytics, what behavior or outcome do your models predict? A: My models deal with natural language understanding for contact center voice calls. They transcribe the calls and derive call summaries, intent and sentiment based on the transcriptions.
An algorithm to predict which people may experience a mental health crisis has been trialled in the UK and found effective enough for routine use. A version that would track people's mobile phone calls, messages and location in a bid to improve accuracy is now being considered. Birmingham and Solihull Mental Health NHS Foundation Trust worked with Alpha, a division of Spanish telecomms firm Telefonica, which owns O2, to see if there was any benefit in automatically flagging the people thought most at risk of experiencing a mental health crisis to NHS staff. The results of the Predictive Analytics project, released under freedom of information rules, suggest there is. The project ran between November 2018 and May 2019.
Adding artificial intelligence to supply chains is delivering tangible benefits for companies putting it in place. Recent research out of McKinsey finds 61% of executives report decreased costs and 53% report increased revenues as a direct result of introducing artificial intelligence into their supply chains. Areas generating revenue in supply chain management include sales and demand, forecasting, spend analytics, and logistics network optimization. I ran this question past Arnaud Morvan, senior engagement director at Aera Technology, a company that focuses on AI. "A reliance on obsolete legacy technologies creates a great deal of time-consuming and error-prone manual work for supply chain practitioners," Morvan points out. "They often spend about 50% of their time collecting and crunching numbers from disparate global systems. That adds weeks or months of delay to core processes that need to run faster to keep up with market demand."
There's an old adage, "good help is hard to get," that is making something of a comeback in today's increasingly dynamic and competitive global human resource industry. In 2016, the HR industry's total operating income reached €491 billion, while 2007-2016 CAGR was about 9 percent. Flexible labour accounts for about 71 percent of modern market share, 20 percent comes from management service providers, 8 percent from high-end talent search, and 1 percent from recruitment process outsourcing and specialized services. Recruitment and staffing are challenging areas that have been getting the most market investments. There are numerous derived services and platforms catering to recruiting: headhunters for high-end talents, background investigation services, consulting firms, and of course the popular online recruitment platforms such as LinkedIn and Glassdoor.