This article was published as a part of the Data Science Blogathon. The distribution of computer services through the internet is known as cloud computing. Businesses can adopt the cloud computing paradigm, where they can rent IT equipment and services instead of purchasing and operating their data centers. These services cover everything from basic infrastructures like networking, servers, storage, databases, and software to cutting-edge tools like artificial intelligence (AI) and machine learning systems. As less equipment needs to be rented and maintained, the importance of cloud analytics translates into businesses cutting expenses while boosting productivity.

Mark Krupnik, PhD, is one of the world's top experts in advanced analytics & AI for retailers. He is the founder and CEO at Retalon. If you've been hearing rumors that your competition is harnessing AI-powered analytics and you're not doing so already, then you should probably worry. According to McKinsey & Company, front-runners who adopt AI within the next five to seven years will lead their competition by over 120%. As AI becomes increasingly popular, more and more articles appear every day.

This blog post was written by Dr. Maryam S. Jaffer, Director Data and Statistics, Emirates Health Services; Dr. Bashar Balish, Senior Director, Cerner; and Michel Ghorayeb, UAE Managing Director, SAS. The future of health care has never been more exciting. Artificial intelligence (AI) and data analytics have captured center stage for any business planning on surviving and thriving. Given the pace of technological development, AI is transforming the future on an unprecedented scale. And that includes the future of health care.

The perspectives on AI in popular imagination have ranged from a useful tool to a threat, from optimistic to downright dystopian. While things are considerably less dramatic in real life, we would do well to periodically ask this of AI, a question that should be asked of all human innovation: How can it make our lives easier and better? In fact, more dimensions can be added to this very question: How can AI be repositioned beyond automation and number-crunching? How can it be made more relevant to our everyday lives? We have seen that AI excels when it augments, rather than replaces, humans and when it helps amplify our strengths of innovation, creativity, abstract thinking and, importantly, empathy.

As per an AmEx Global Customer Service Barometer, 83% of Americans expressed a willingness to pay more for a better Customer Experience (CX). And if that doesn't speak volumes about the impact a differentiating or a bad CX can have in a 24*7 virtually connected world, then read the next stat. The same study talked about the multiplier effect a positive or a negative experience can ignite and it is bent heavily in favour of a negative CX. Americans on average were likely to share a bad experience with 16 other people in their network as compared to only 9 while experiencing a good service or product. Well, they say the customer is a king for a reason! Not maintaining an impactful and hassle-free CX is an existential threat for brands.

One of the most evolving technologies in the digital age is Big Data technologies. It's not a popular term, but Big Data is simply a term that is used to describe a collection of data that is huge in size and is exponentially increasing in time. It means that this data is so large that none of the traditional management tools are able to analyze, store or process it. Big Data is not just simply a term. It is associated with other technologies such as machine learning, artificial intelligence, blockchain, Internet of Things, augmented reality and a whole lot more.

SKF has acquired Presenso Ltd., a company that develops and deploys artificial intelligence (AI)-based software for improving machine performance. Presenso's AI capability enables production plants to find and act on anomalies that were previously undetectable, automatically and without the need to employ additional data scientists. Presenso's solution is used by industrial plants to increase production output and revenue by reducing the incidence of unplanned asset downtime. Presenso, located in Haifa, Israel, built its solution based on innovations in the field of Automated Machine Learning or AutoML. AutoML accelerates the rate of AI deployment, enabling plants to scale industrial analytics across a large asset base.

In December 2014, I asked whether we were at the beginning of "the end of the Hadoop bubble." I kept updating my Hadoop bubble watch (here and here) through the much-hyped IPOs of Hortonworks and Cloudera. The question was whether an open-source distributed storage technology which Google invented (and quickly replaced with better tools) could survive as a business proposition at a time when enterprises have moved rapidly to adopting the cloud and "AI"--advanced machine learning or deep learning. In January 2019, perennially unprofitable Hortonworks closed an all-stock $5.2 billion merger with Cloudera. In May 2019, another Hadoop-based provider, MapR, announced that it would shut down if it were unable to find a buyer or a new source of funding.

This big data discipline of artificial intelligence gives systems the freedom to automatically gain information and improve from experience without manual programming. Machine learning is literally just that – "letting the machine learn". The definition of machine learning is "the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as'training data', in order to make predictions or decisions without being explicitly programmed to perform the task".

Predictive analytics can be used to reclaim millions of dollars in operational costs for healthcare organizations. As pressure mounts to lower healthcare costs, healthcare delivery organizations are taking a closer look at costs in all aspects of their business, particularly operations. More organizations are realizing there is a huge opportunity to lower operational costs by leveraging machine data and machine learning. By leveraging machine learning, the solution can predict and avoid unnecessary downtime, reduce maintenance costs, and maximize profitability from capital-intensive medical equipment. We've created a Glassbeam Healthcare ROI Calculator so you can easily visualize potential saving.