Here is how Netflix uses data to drive success

| July 10, 2018

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With over 100 million subscribers, there is no doubt that Netflix is the daddy of the online streaming world. Netflix’s speedy rise to dominance has movie industry leaders taken aback – forcing them to ask, how on earth could one single website take on Hollywood? The answer – big data. One of the major success stories in technological disruption, Netflix is a prime example of how big data can be leveraged to totally transform an industry. Netflix offers something wholly unique, in that it provides a service that is completely tailored to every individual user’s taste.

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How big data is empowering better business intelligence

Article | March 24, 2020

Business intelligence (BI) is nothing new to enterprises that have been relying on data processing and analysis to deliver insightful reports that reflect business performance.These tools are a great match for enterprises that value the data their operations generate. BI software and programs work together to turn data into actionable insights that can drive better business decisions and market strategies and, ultimately, drive revenue as a result.Combined with the masses of external data amassing every second whether that’s customers’ feedback and experience, competitor intelligence, seasonal buying habits, or otherwise businesses can have a huge amount of data at their disposal.While BI systems draw specific data from pre-defined sources to turn them into insights, big data technologies capture data from a variety of sources in real-time, regardless of their formats or structure.

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Deep Dive Digital-First Banks Harness The Power Of Data Analytics

Article | April 2, 2020

Data analytics has many purposes in the banking industry, ranging from improving cybersecurity to reducing customer churn. Every interaction from ATM withdrawals to loan applications — provides FIs with valuable data about customers’ financial lifestyles. Banks can even harness external regulatory, trading and social media engagement data, all of which can be processed and analyzed to benefit their operations.Financial data is useful in helping banks develop wide-reaching marketing campaigns, but social data is critical to developing offers for specific customers. Santa Rosa, California-based Redwood Credit Union, for example, found that social data was particularly important when offering auto loans. It initially extended preapproval for such loans every two years based solely on members’ credit scores and vehicle purchase histories, but it soon discovered that there was a much more reliable indicator and updated its preapproval frequency accordingly.

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MODERNIZED REQUIREMENTS OF EFFICIENT DATA SCIENCE SUCCESS ACROSS ORGANIZATIONS

Article | February 23, 2020

Does the success of companies like Google depend on that of the algorithms or that of data? Today’s fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Amalio Telenti, Chief Data Scientist and Head of Computational Biology at Vir Biotechnology Inc. argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms and that they forget the critical infrastructures that are needed to create and to manage data in the first place.Data management and infrastructures are the little ugly duckling of data science but they are necessary for a successful program and therefore need to be built with purpose. This requires careful consideration of strategies for data capture, storage of raw and processed data and instruments for retrieval. Beyond the virtues of analysis, there are also the benefits of facilitated retrieval. While there are many solutions for visualization of corporate or industrial data, there is still a need for flexible retrieval tools in the form of search engines that query the diverse sources and forms of data and information that are generated at a given company or institution.

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AI and Predictive Analytics: Myth, Math, or Magic

Article | February 10, 2020

We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.

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