How to choose a data analytics platform

A brief guide to the analytics lifecycle, the expanding array of tools and technologies, and selecting the right data platform for your needs

How to choose a data analytics platform
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Whether you have responsibilities in software development, devops, systems, clouds, test automation, site reliability, leading scrum teams, infosec, or other information technology areas, you’ll have increasing opportunities and requirements to work with data, analytics, and machine learning.

Your exposure to analytics may come through IT data, such as developing metrics and insights from agile, devops, or website metrics. There’s no better way to learn the basic skills and tools around data, analytics, and machine learning than to apply them to data that you know and that you can mine for insights to drive actions.

Things get a little bit more complex once you branch out of the world of IT data and provide services to data scientist teams, citizen data scientists, and other business analysts performing data visualizations, analytics, and machine learning.

First, data has to be loaded and cleansed. Then, depending on the volume, variety, and velocity of the data, you’re likely to encounter multiple back-end databases and cloud data technologies. Lastly, over the last several years, what used to be a choice between business intelligence and data visualization tools has ballooned into a complex matrix of full-lifecycle analytics and machine learning platforms.

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