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DataOps (data operations) brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and skills to enable the data-driven enterprise.
Business intelligence (BI) enables companies to harness insights from massive amounts of data. But doing so requires overcoming a range of strategic and tactical challenges.
Organizations are accelerating their ability to make data-driven decisions by offering analytics capabilities directly to business users. Here’s how to do it right.
IT and analytics leaders seeking to convey actionable insights from their organization’s data must learn how to tell compelling stories with data, emphasizing context and narrative.
Industrial companies of the future will leverage data to eliminate downtime and boost efficiencies, writes Matt Newton, Director, Artificial Intelligence and Optimization, at AVEVA.
Few organizations are truly positioned to deliver on the promise of data-driven decision-making. Here’s how to tell if yours is one.
The market for data science talent is tight. To stand out, IT leaders advise establishing innovative, purpose-driven roles that ensure data scientists can thrive at your organization.
The chances are that most of the data you collect — from human communications to machine logs — is piling up with little plan for actualizing its potential. Good governance and AI can help.
Data visualization is the presentation of data in a graphical format to make it easier for decision makers to see and understand trends, outliers, and patterns in data.
The ongoing data scientist shortage sees enterprises reconfiguring data teams, upskilling promising employees, and partnering to improve talent pipelines.
Putting data and visualization tools in the hands of business people is one thing; getting IT out of the business of running reports is quite another.
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