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Data science techniques are getting better, cheaper, and easier to use.
Even small and medium sized organizations can now tap these technologies.
Enterprise software development teams have historically had trouble ensuring the code that runs well on a developer's machine also runs well in production.
Dev Ops has promoted more collaboration between developers and IT operations.
Data quality, data privacy, and advanced technologies such as AI, machine learning, neural networks, and more, are of top concern to data analytics pros and IT managers, says Karen Lopez, Data & Analytics Track Chair for Interop ITX 2018.Here are some ways that data professionals can gain the support they need.Ebates, the business that provides discounts and incentives to customers of online retailers, needed to move from its SQL server to a system with more processing capacity.Here's how to get ready for a future of new technology, including AI, natural language processing, and Io T.
Alexa's popularity among consumers serves as a wake-up call for businesses.Download our report to learn about the biggest challenges and how savvy IT executives are overcoming them.Is Dev Ops helping organizations reduce costs and time-to-market for software releases? Find out in this Information Week and Interop ITX infographic on the state of Dev Ops in 2017.But, if you fail to properly introduce, support, and integrate data science capabilities, a lot of money can be wasted.