Peer-Driven Insights

Big Data

About this topic

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.

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Dr Joe Perez

Dr Joe Perez

NC Dept of Health & Human Services

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Clifton Persaud

ISACA

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Arockia Liborious

Arockia Liborious

The International Soceity of Data Scientists

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Community Posts

Can CIOs act as the CDOs for organizations today? Or do companies really need a separate CDO who can partner with the business and IT to drive digital transformation?

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What is the most interesting/helpful/unique tool you're using right now that no one is talking about?

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Will companies shift their focus from big data to small and wide data post-pandemic?

The focus will remain on Big Data.65%

The focus will shift to small and wide data.25%

Unsure.9%

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Do you agree with Google’s claim that the American Innovation and Choice Online Act will make their services less helpful?

Strongly agree7%

Agree54%

Neither agree nor disagree25%

Disagree12%

Strongly disagree

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What do you qualify as "Big Data"? Does it have more to do with the amount of data or the type of data?

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What is the one indispensable action you would emphasize for a successful Data Strategy, which would have a lasting impact on your organization's long term competitiveness?

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Has anyone done a Box to O365 migration? Looking for some ideas/thoughts. Looking for the big data move help!

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Is it vital to capture every stream of data in your organization to create truly continuous intelligence?

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What prevents companies from filtering the massive amounts of data they consistently collect?

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Will the cost of data make smart cities economically feasible?

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How should we approach the problem of excessive data collection?

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What do you use as a data discovery/catalog solution?

Alation11%

Amundsen28%

Collibra33%

Datahub30%

None22%

Others10%

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Is it possible to aggregate Big Data for intelligence purposes without sacrificing risk mitigation?

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What do organizations get wrong when it comes to data lifecycle management?

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Thinking about deep machine learning, how ready is your data (cleaned, prepped and labeled) for compute-heavy data models?

What do consumers need to know about how companies handle their data?