Smart DataOps: How AI Can Enhance Enterprise Data-workflow Management

Artificial Intelligence (AI) in a data environment holds great promise. The vendor marketing message is seductive: “We use advanced technology to make things better for you.”

But it’s where the AI rubber meets the data process road that the value proposition can get muddled. In evaluating data software, integrated AI technology must be more than a marketing evaluation checkbox. Understanding how AI can impact DataOps value delivery must be a fundamental objective in the data-driven enterprise. Otherwise, the value-add part becomes meaningless.

In this report, we’ll assess AI’s potential to impact DataOps environments. (Here’s a hint: It’s about the process.) Enterprises that adopt a DataOps approach to collaborative data-workflow management establish a commitment to continuous improvement. AI can help enterprises establish efficient DataOps, but only if the value AI provides goes beyond predictive analytics (and simple marketing promises).

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About Toph Whitmore

Toph Whitmore is a Blue Hill Research principal analyst covering the Big Data, analytics, marketing automation, and business operations technology spaces. His research interests include technology adoption criteria, data-driven decision-making in the enterprise, customer-journey analytics, and enterprise data-integration models. Before joining Blue Hill Research, Toph spent four years providing management consulting services to Microsoft, delivering strategic project management leadership. More recently, he served as a marketing executive with cloud infrastructure and Big Data software technology firms. A former journalist, Toph's writing has appeared in GigaOM, DevOps Angle, and The Huffington Post, among other media. Toph resides in North Vancouver, British Columbia, Canada, where he is active in the local tech startup community as an angel investor and corporate advisor.
Posted on August 2, 2017 by Toph Whitmore

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