Guide to using advanced analytics and AI in business applications

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  3 ways to make machine learning in business more effective

  Dun & Bradstreet analytics exec Nipa Basu offers three tips on how to integrate machine learning tools into business processes to help drive better decision-making. Read Now

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  Chatbots need to get more conversational to be truly engaging

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  Key technology components that go into AI systems

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  Comparing four cloud-based machine learning platforms

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1Big-picture views of advanced analytics and enterprise AI

AI in business applications is changing the use of data in the enterprise -- and creating new technology needs and management challenges for organizations. In this section, learn about AI-related trends and issues to be aware of before jumping into deployments, including what's involved in building and maintaining machine learning platforms, how AI tools could affect jobs, and what AI's inherent analytical ambiguity means for analytics teams.

• Article

  DevOps offers a model for developing machine learning models

  Wikibon analyst James Kobielus discusses the growing adoption of AI tools and the need to continually re-evaluate machine learning models to keep them valid. Read Now

2Methods and techniques for making sense of data

Business applications, big data systems and other data sources can produce a great deal of data on customers, products, machinery and more -- but how can organizations take advantage of all that information? In this section, you'll find advice on how to implement predictive modeling, machine learning and deep learning techniques, plus other aspects of managing advanced analytics and AI applications.

• Article

  Must-do machine learning steps for building predictive models

  There are some important processes that analytics teams should be sure to include when developing machine learning models for predictive analytics uses. Read Now

3Putting AI technologies to work in business applications

Rolling out AI in business environments is easier said than done, but business use cases are emerging for AI tools and related advanced analytics technologies. In this section, learn more about those uses and the challenges that some organizations have faced -- plus lessons they've learned, such as the need to tightly tie AI efforts to business objectives and to be aware of technology limitations.

• Article

  Facebook taps deep learning tools to better engage users

  To help boost user engagement, Facebook uses deep learning models that analyze images, videos and text to tailor what people see on its site to their interests. Read Now

4Real-world advice on advanced analytics and AI in business

As technology vendors try to turn all the AI hype into reality, IT professionals are learning how to take advantage of AI, machine learning and cognitive computing platforms. In this section, get advice and points of view from technology and analytics managers who've led the charge in their companies to modernize analytics approaches and deploy AI in business applications.

• Article

  Auto insurer turns to machine learning to improve driving

  The vice president of analytics at HiRoad Assurance Co. details how the auto insurer is using machine learning to promote better driving habits in customers.  Read Now