Fivetran, the automated data integration provider, announced enterprise software veteran Bob Muglia, former CEO of Snowflake, will join its board of directors. Turbine Labs, an AI-powered software and managed-services platform, announced a data integration and collaboration with Tableau, to support the massive amount of COVID-19-related data. Mark Marinelli, Head of Product and Chief Data Evangelist at Tamr, talks about Machine Learning and the breakthrough in Enterprise Data Management, in this article for Forbes. Brian J. Dooley, author, analyst, and journalist with more than 30 years' experience, talks about data center management-as-a-service and data management-as-a-service, in this article for TDWI. Arawan Gajajiva, Principal Solution Architect in Matillion, presents a practical framework for centralising BI and analytics inside the enterprise, in an article for ITProPortal.

Dark data is a type of unstructured, untagged and untapped data that has not yet been analysed or processed. It is similar to big data, but differs in how it is often neglected by business and IT administrators in terms of its value. With 80 per cent of data considered as dark data, there is undeniably enough information for companies to utilise to their advantage. Therefore, it is now time to bring light to the unknown gold mine of dark data. For most businesses, understanding the vast amount of dark data can be an overwhelming challenge.

Increasingly, businesses are using ML to strengthen their competitive advantage and drive innovation. Is your organisation embracing this shift or are you falling behind? If you are on the "bias-for-action" side of the scale and have already started steering your organisation towards digital & ML transformation, are you confident you are doing so in the right way? Over the past decade, data has become increasingly important and has even been described as the "new oil". Organisations with extensive user data can leverage data to increase sales and customer retention.

Artificial intelligence (AI) is no longer advancing in baby steps. In fact, it's starting to leap forward and sprint toward the future. Its recent applications in medicine have been remarkable, especially as a diagnostic tool. For instance, AI can detect melanoma -- a form of skin cancer -- in its early stages, allowing quick response and increased survival rates -- up to 98 per cent! AI is now also able to diagnose diabetic retinopathy (DR) -- a form of blindness caused by diabetes.

Businesses face an ever increasing challenge to protect their assets from cyber criminals. The sophistication and frequency of attacks continue to elevate as these criminals take advantage of rapidly advancing technologies. Even using the latest machine driven security systems, it is becoming increasingly difficult for businesses to differentiate between a genuine employee or website visitor and a criminal seeking to breach or bring down their network and systems. Cyber security professionals are facing the prospect that they have reached a glass ceiling in terms of what humans can achieve. Does the future of cyber security defence now depend of robots?

Consider the following expenses claims: registration fees for a cancelled seminar, two separate claims for mileage when the employees travelled together, and a sandwich-and-coffee dinner claimed as the full per diem. While it's easy to believe that a few dishonest claims won't hurt, for individual victims, expenses fraud can be costly. Research conducted by the National Fraud Authority suggests that exaggerated expenses claims cost the British economy around 100 million annually; the private sector alone lost 80 million in 2013. Imagine if 20 per cent of your staff added 10 per cent to each mileage claim; the cumulative loss for the company would quickly become significant. Existing fraud detection systems flag dubious-looking expenses according to a set of rules, such as challenging claims in excess of a fixed cash amount or those that are 5 percent higher than claims submitted by peers in similar positions.

With recent developments like the launch of Facebook's chatbot store, Apple's acquisition of Emotient, and the release of Viv, a virtual assistant from the founders of Siri, there's no doubt that artificial intelligence (AI) has started to quickly proliferate across consumer applications. Nonetheless, many of these new applications are still more novelty than necessity as their functionality is rudimentary at best, though we've come to rely on them daily – from Amazon product recommendations to Facebook facial recognition (auto tagging). Until consumer-facing AI can usher in new technological advancements that provide deeper and more human-like interaction, it still has a long way to go before it reaches its tipping point. Enterprise AI, however, offers immediate applications that help solve problems that many companies and workers face today, such as data overload. Companies across a wide range of industries have already taken advantage of AI capabilities in order to help improve both internal and external processes.

Artificial Intelligence has been heralded as a game changer in the drive toward the intelligent enterprise. While AI and machine learning has been around for more than five decades, it's today's increasingly interconnected world and the continuing explosion of data that is driving an increase of applications powered by AI. AI promises to deliver exciting opportunities to IT as well as the business world, and many believe the reality is not far off. According to a recent report by Research and Markets, the AI market is estimated to grow from 419.7m ( 280m) in 2014 to 5.05bn ( 3.5bn) by 2020, at a CAGR of 53.65 per cent from 2015 to 2020. Major factors driving growth include diversified application areas of AI, improved productivity, and increased levels of customer satisfaction.

There are two kinds of office work: the uninspiring, but necessary stuff that takes long hours to do and requires little in terms of judgement or emotional intelligence; and the creative part, which is challenging and flexes the human mind. Software bots have already started to take on some of the routine tasks. We believe that within a decade, Artificial Intelligence will take care of all the repetitive office work so that we can focus our minds on higher value and rewarding activity. AI goes way beyond automation; it brings intelligence to the job at hand, with a truly transformational impact. The news that chatbots are coming to Facebook Messenger made quite a few waves.

With recent developments like the launch of Facebook's chatbot store, Apple's acquisition of Emotient, and the release of Viv, a virtual assistant from the founders of Siri, there's no doubt that artificial intelligence (AI) has started to quickly proliferate across consumer applications. Nonetheless, many of these new applications are still more novelty than necessity as their functionality is rudimentary at best, though we've come to rely on them daily – from Amazon product recommendations to Facebook facial recognition (auto tagging). Until consumer-facing AI can usher in new technological advancements that provide deeper and more human-like interaction, it still has a long way to go before it reaches its tipping point. Enterprise AI, however, offers immediate applications that help solve problems that many companies and workers face today, such as data overload. Companies across a wide range of industries have already taken advantage of AI capabilities in order to help improve both internal and external processes.