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There is no single form of Data Analytics in business intelligence. For some, e.g., executives and analysts seeking immediate insight, it can mean interactive dashboards, often built on commodity platforms like Tableau, Looker or PowerBI.
For those diving behind the data, it can mean an Excel/VB macro or SQL database query, old-fashioned though that sometimes may seem. For code and math-savvy data scientists fresh out of college, it could be a fun Python, R or MATLAB script running the latest and greatest statistics and machine learning methods, possibly abstracted into customizable apps, web interfaces, or Jupyter notebooks.
Whatever your preferred form of analytics, interface preferences, coding skills, or architecture – cloud, on-premises or hybrid – handling more data more simply is in easy reach.
In this session, we will explore, with examples from multiple industries, 3 simple steps data scientists, analysts and their teams can take to run powerful, scalable real-time analytics that reduce compute costs, data complexity, and risks.