Last Updated: June 22, 2018
Twice a month, we bring you headlines from around the industry to keep you informed of the latest trends and conversations. Whether you are in IT, finance, sales or marketing, we are your go-to source for predictive analytics, machine learning and AI, combined with human intelligence, can transform business planning and performance.
Business intelligence vs. business analytics: Where BI fits into your data strategy
hen forming a data strategy, companies traditionally lean heavily toward leveraging business intelligence tools. However, as predictive and advanced analytics become increasingly popular, and the technology continues to evolve via machine learning and artificial intelligence, executives are beginning to make business analytics a focal point of their data strategies.
While both of these solutions are vital to staying competitive in a connected market, understanding the distinctions between business intelligence and other analytics platforms, as well as the value each brings to the enterprise, matters significantly in getting your data strategy right.
Predictive and big data analytics have been hot topics in the business and IT worlds for a few years now with no shortage of content and input from those in the industry. However, early adopters of the technology struggled in many areas, leading to low ROI and myths about predictive analytics’ limitations.
In most cases, these misconceptions were due to an error in judgment made by a number of early adopters. In other situations, myths formed out of a shortage of skills and tools required to run a big data project. Read this list from InformationWeek to see if any of these myths are still being communicated in your company and learn the real truth.
Earlier this year, Elon Musk warned about the dangers of artificial intelligence and its capacity to become an all-knowing force. However, the danger on the horizon right now is an over-reliance by humans on machine learning and expert systems.
AI techniques can greatly add to business productivity via pattern recognition and automated decision-making. For example, many Amazon customers have already experienced how machine learning-based analytics can be used in sales through Amazon’s recommendation engine. This article from Ars Technica highlights how machine learning can train and learn on data sets where even a human expert can’t verbalize how the decision was made. Read it here.
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