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8 trends to watch for analytics in 2021

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By Topics | Analytics Artificial Intelligence
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We've turned some of our most notable predictions for next year into a slide show. Click the orange "next" button to see these 2021 predictions from SAS.

 

Who’s brave enough to make predictions for next year after the unpredictable year we just had? We are. After all, the disruptive nature of 2020 brings with it some opportunities as well – and many of them involve data, analytics and digital transformation. We surveyed some of our most forwarding-looking colleagues and selected our favorite predictions for you to use when planning for 2021. Let us know on Twitter or LinkedIn whether you agree with our predictions and share some of your best predictions too. See our first prediction here and click the next button to read more. 

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About Author

Alison Bolen

Editor of Blogs and Social Content

Alison Bolen is an editor at SAS, where she writes and edits content about analytics and emerging topics. Since starting at SAS in 1999, Alison has edited print publications, Web sites, e-newsletters, customer success stories and blogs. She has a bachelor’s degree in magazine journalism from Ohio University and a master’s degree in technical writing from North Carolina State University.

3 Comments

  1. David Pope on

    I agree with this 100% especially since this is the 2nd time this type of miscalculation has happened. This was the same issue with Hadoop. Hadoop wasn't initially designed or engineered to run analytics it was designed to handle traditional database type of storage, queries, reports, and applications. Then one day someone thought they could run all these systems and applications on Hadoop while at the same time run analytic workloads as well. That didn't turn out so well because the analytics needed all the compute and memory resources of the Hadoop cluster and so everything else stopped running. The same problems and challenges that IT ran into when trying to use Hadoop for what it was designed for (data compute on a big scale) and analytics at the same time are the same problems cloud architects are running into now when attempting to run everything including analytics in the same architecture/environment they are running their other workloads and processes. This approach didn't work in the past and won't work in the cloud either. Don't get me wrong you will be able to run any workload in the cloud, but to do analytics successfully you have to have data in a different format and a separate environment with more compute and memory resources then you typically need to run traditional workloads.

  2. Robin Langford on

    Thanks for a fun, informative read, Alison. Maybe you'll do a follow-up piece next January to see how these predictions held up. They seem spot-on to me, and hopeful!

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