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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. But, if you fail to properly introduce, support, and integrate data science capabilities, a lot of money can be wasted.
Enterprise software development teams have historically had trouble ensuring the code that runs well on a developer's machine also runs well in production. DevOps has promoted more collaboration between developers and IT operations. Data scientists and data science teams face similar challenges, which DevOps concepts can help address.
Alternative and virtual realities provide organizations with new opportunities to reimagine product demos, employee training and more. As the systems collect and generate data, there are opportunities to use the new streams in innovative ways.
Alexa's popularity among consumers serves as a wake-up call for businesses. Eventually, voice interfaces will replace keyboards, taps and swipes, but organizations must be wary of approaching voice interface design the same way they've approached web and mobile design. Before you begin, consider these points.
Some enterprises struggle to drive business value from data science efforts because the business and data scientists are not communicating or collaborating well. Here are five things you can do to improve the cross-functional relationships and ROI.
2021 State of ITOps and SecOps ReportThis new report from InformationWeek explores what we've learned over the past year, critical trends around ITOps and SecOps, and where leaders are focusing their time and efforts to support a growing digital economy. Download it today!