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Five predictions for AI and process automation in 2020

The development of artificial intelligence (AI) and robotic process automation (RPA) rose rapidly during 2019. In the coming year, this pace will climb even further. Enticed by the potential for the technologies to streamline workflows and improve customer service, organisations will be undertaking deployments in ever-increasing numbers. At the same time, the capabilities of the technologies will continue to grow at a breakneck pace. Where initially they were limited to assisting with very structured activities, they will increasingly be put to work in areas that until now have required human intervention. During 2020, five key trends will shape the field of AI and RPA. They are: The rise of the RPA robot Current projects involving the deployment of RPA robots have tended to focus on replicating existing tasks that have traditionally been completed by humans. The robots have been able to learn a repetitive task and then complete it much more quickly. While their AI capabilities allow…

The Power of Visualization

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A picture really does say a thousand words. “Visualization is really about external cognition, that is, how resources outside the mind can be used to boost the cognitive capabilities of the mind.” — Stuart Card The importance of visualization is a topic taught to almost every data scientist in an entry-level course at university but is mastered by very few individuals. It is often regarded as obvious or unimportant due to its inherently subjective nature. In this article, I hope to dispel some of those thoughts and show you that visualization is incredibly important, not just in the field of data science, but for communicating any form of information.
Visualization Goals Essentially, there are three goals to visualization: Data Exploration — find the unknownData Analysis — check hypothesesPresentation — communicate and disseminateThat is essentially it. However, these terms are pretty vague, and it is thus quite easy to understand why it is so difficult for individuals to master the ar…

Statistics Vs Machine Leaning

Are they both really SAME?

No, they are not the same. If machine learning is just glorified statistics, then architecture is just glorified sand-castle construction.

Machine Learning is built upon Statistics Before we discuss what is different about statistics and machine learning, let us discuss first the similarities.  Machine learning is built upon a statistical framework. This should be overtly obvious since machine learning involves data, and data has to be described using a statistical framework. However, statistical mechanics, which is expanded into thermodynamics for large numbers of particles, is also built upon a statistical framework. The concept of pressure is actually a statistic, and the temperature is also a statistic. If you think this sounds ludicrous, fair enough, but it is actually true. This is why you cannot describe the temperature or pressure of a molecule, it is nonsensical. Temperature is the manifestation of the average energy produced by molecular collisions. …

Digital Transformation with Self Service analytics

The term digital transformation was coined decades ago to express the use of digital technologies as a way to replace and transform older methods to promote efficiency. Throughout the years, more systems have gone through digital transformations including buying through eCommerce, ordering food through delivery apps, or using wearable activity trackers to analyze exercise results through IoT. In a company, the same kinds of transformations can be seen, like advertising through social media instead of print ads, using a chatbot for customer service instead of a person, or presenting a customer with options based on their previous behavior. It should be noted that a digital transformation is not simply the technology put in place, but the entire process that gets set up around using it. An increasingly popular way for companies to expand their digital transformation efforts revolves around the use of self-service analytics – simplified interfaces and BI tools that can be used just as eas…

Traditional BI vs Modern BI

Slow. Inflexible. Time-consuming. Does that sound like any sensible way to get users the business insights they need to do their jobs? A few years ago, this might have been the only option for business intelligence, but now there’s a fork in the road. Users can go one way with centralized BI run by their IT department, or they can strike out with modern BI solutions they can use by themselves. As you might imagine, in the traditional vs self-service BI debate, there are pros and cons to be considered before making a choice. Is Traditional BI a solution?Let’s take the traditional approach first. There are reasons for this controlled BI environment to exist. When you control the data and the BI application, you have a chance of controlling the quality of the results. IT departments concerned about quality (meaning every conscientious IT department) can make sure that data is properly prepared, stored, and secured. They can build systems that offer standardized, scalable self-service repo…

Role of Analytics in Telecom Industry

The telecoms industry is one which not only sees a large customer base but a customer base whose needs and desires are constantly evolving and/or shifting. On top of this, telecom firms face cut-throat competition, making it a highly dynamic and challenging industry. In such a scenario, each decision taken becomes all the more crucial. It is therefore imperative for the firm to make decisions based on extensive data analytics to ensure the efficient and effective use of business resources. Although analytics can be instrumental in the telecom industry in many ways, some of the major applications include: ·Customer retention/improving customer loyalty:With fierce competition between the numerous players in this industry, customer retention is essential. Telecommunications are now much more than making calls and analytical tools can help firms to identify cross-selling opportunities and make impactful decisions to retain the customer. Analytics can also help in identifying trends in cust…

Applications of BI in Aviation Industry

It’s no secret that the airline industry faces many problems. Obviously, there are operational troubles like overbooking, high prices, pilot shortages, and baggage issues. But the list of the industry’s rough spots doesn’t end there. 
An ‘intelligence inertia’ towards machine learning outcomes will be the equivalent of the comet that wiped out the dinosaurs. Businesses must take time to understand its immense value to the business Outdated technologies plague aviation today, as well as cumbersome rules and complexities, infrastructure questionability and airport issues. Dirty, unhygienic planes with uncomfortable seats don’t help much, either. Further, over the past few decades, innovations in the industry have been mediocre, and customer service has been lousy. And perhaps more urgent than any other problem on aviation’s long laundry list are the well-known environmental issues that stem from air travel. Is there hope for the future of the aviation industry? Thanks to solutions and innov…