advanced analytics
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Artificial intelligence is reshaping how we work — but not in one single, unified way. Beneath the surface of the current hype, two very different AI waves are unfolding. The first is visible and noisy. It is filled with influencers, prompt guides, and off-the-shelf tools promising instant productivity gains. The second is quieter but far
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Advanced analytics is being deployed in a range of use cases across business units and industries, especially as data types and volume increase. One of the top use cases for advanced analytics we see at TDWI is predictive analytics to understand customer or operational behavior. Statistical as well as machine learning models are used to
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There are organizational and technology components critical for a business to succeed in becoming data-driven. On the organizational side, a key component to succeeding with data and analytics is to create a culture that supports these efforts. Companies that succeed are typically goal-driven, transparent, empowering, and collaborative. They have strong leadership that believes in data
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TDWI research indicates that if users stuck to their plans around predictive analytics, adoption would be at 75–80% versus the 35–40% we currently see. Predictive analytics is on the cusp of widespread adoption. Many organizations are excited to make use of the power of predictive analytics (including machine learning) because they understand the
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My colleague Dave Stodder and I recently lead a Webinar in conjunction with our best practices report about becoming a data-driven organization. Audience questions included several about self-service. In particular, attendees were interested in how to make self-service more accessible to managers and leaders in their organization. Let me set the stage for self-service analytics
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2018 will be the year of the three As: AI, Automation, and Advancing analytics skills In 2017, advanced analytics maintained its momentum in the enterprise. Open source technologies such R and Python gained ground. Other technologies such as machine learning continued to pique interest. Use of the cloud become more mainstream. TDWI expects these technologies
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Dave Stodder and I just finished writing our 4Q Best Practices Report on “What it Takes to Be Data-Driven: Technologies and Practices for Becoming a Smarter Organization.” What struck me in analyzing the data for the report is that although organizations have embraced BI and analytics, they still have a journey in front of them
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What does it take to achieve analytics maturity? Earlier this week, Dave Stodder and I hosted a webcast with a panel of vendor experts from Cloudera, Microstrategy, and Tableau. These three companies are all sponsors of the Analytics Maturity Model; an analytics assessment tool that measures where your organization stands relative to its peers in
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I recently completed TDWI’s latest Best Practices Report: Next Generation Analytics and Platforms for Business Success. Although the phrase “next-generation analytics and platforms” can evoke images of machine learning, big data, Hadoop, and the Internet of things (IoT), most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. For
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Today, I participated in a webinar with Actuate on the skills needed for business analysts to perform predictive modeling. This is a hot topic and there were hundreds of participants on the call. In my part of the presentation, I outlined some major trends in predictive analytics (including the fact that the tools are much easier
Fern Halper's data makes the world go 'round 