Learning to "speak data" via Gartner’s Data & Analytics Summit
Posted by Elise Toulman
Addressing an audience full of data pros and analytics leaders across varying industries, Gartner analysts Carlie J. Idoine, Kurt Schlegel, and Rita Sallam discussed the importance of learning to master and “speak data” fluently at the Gartner Data & Analytics Summit in Texas.
With artificial intelligence, automation, and machine learning taking centre stage in technological innovation, we’ve never had so much information at our disposal. In order to really use it to its full potential, those using solutions built off the back of this data must be capable of communicating it in a unified way.
Gartner conducted their third annual chief data officer survey and found that poor data literacy was one of the biggest hurdles organisations are attempting to overcome in their journey to progress. Carlie J. Idoine suggested that data analysts must think of data as a second language, and one that is integral to digital transformation. Without a common language, communication in organisations becomes ineffective, and all that data is essentially useless.
Business intelligence today, very generally speaking, is based on past data discovery and past results. But as more modern business intelligence is taking shape, it invites more self-service or internal data analysis, which changes what organisations need (and will benefit from most) in terms of their software solutions.
Advanced analytics models, created by experts, build this innovation into business intelligence tools and self driving business technology, to the benefit of the many. The principles of artificial intelligence, automation, and machine learning in business software brings simplicity, flexibility, and unity to organisations, by augmenting human analysis, evolving with the business, and supporting data literacy by helping with translation.
Business models are transforming, customer expectations are surging, and the way we operate and compete is turning upside down. Our solutions are built around the people that use them. We work to connect the right data to the right people with the right ideas, closing the gap between the data available and the people who can use it.