How to choose a data analytics platform

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.

The importance of analytics and machine learning increases IT’s responsibilities in several areas. For example:

  • IT often provides services around all the data integrations, back-end databases, and analytics platforms.
  • Devops teams often deploy and scale the data infrastructure to enable experimenting on machine learning models and then support production data processing.
  • Network operations teams establish secure connections between SaaS analytics tools, multiclouds, and data centers.
  • IT service management teams respond to data and analytics service requests and incidents.
  • Infosec oversees data security governance and implementations.
  • Developers integrate analytics and machine learning models into applications.

Given the explosion of analytics, cloud data platforms, and machine learning capabilities, here is a primer to better understand the analytics lifecycle, from data integration and cleaning, to dataops and modelops, to the databases, data platforms, and analytics offerings themselves.

Copyright © 2020 IDG Communications, Inc.

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