Artificial Intelligence is the Future of Business Intelligence

October 26, 2018
Roman Stanek's picture
Founder & CEO
Roman Stanek is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).

For many of us, artificial intelligence (A) in business intelligence has been top of mind for quite some time. It’s prominently featured in Gartner’s hype cycle, and it’s clear that AI will play a big part in the future of business intelligence. For example, some of the recently developed AI capabilities seem to come straight out of a science fiction movie: voice recognition and digital assistants, task automation, medical diagnoses, and facial recognition systems—and systems to fight those facial recognition systems.

Not only is AI capable of doing all those fantastic science-fiction things, but it’s also capable of making a big difference in our analytics world. Artificial intelligence in business intelligence helps democratize data and improve analytics adoption. Radical advances in computing power, predictive analytics, machine learning, and AI have opened the door to a new generation of BI tools. If implemented properly, AI pulls actionable insights from complex data sets and automatically recommending next actions. AI provides data points exactly where and when they are needed most with very little work required by the user.

AI bridges the gap for business users who don’t have deep technical knowledge

Because AI can analyze massive quantities of data and deliver recommendations based on that data, it makes analytics and big data insights accessible and understandable to the average user—not just data scientists. Where a sharp divide once existed between two groups of users—those familiar enough with data science to glean value from the data themselves, and those who are not—AI bridges that gap and delivers insights in a digestible format.

AI is already getting better at identifying trends and extracting insights from complex data than humans are, and these abilities will only improve over time. Rather than continuing to pour money into tools that require employees to spend their time manually analyzing data and making mundane decisions, business organizations should invest in the future of BI and ai-powered systems that automate the bulk of these processes and allow their talent to focus on the strategic problems that really move the needle.

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AI enables you to put your data to work

By analyzing all the data that’s been collected and delivering real time recommendations, AI enables you to actually use your data to your competitive advantage. If your competitors aren’t using AI, they’ll be stuck sifting through mountains of data on their own. By using AI, you’ll be able to harness the power of the data you collect, identify more trends, build recommended actions based on those trends, and ensure that those actions and surrounding context are delivered to your end users exactly when and where they need them.

While AI, like any technology,  certainly has some limitations—most notably when it comes to cybersecurity or inherent bias—the potential benefits are enormous. In spite of this, many companies are lagging behind when it comes to recognizing AI as a trend in the future of business analytics and intelligence. Those companies that begin to implement AI will soon find themselves outperforming their competitors when it comes to improved overall business performance and higher revenue.

Want to learn more? Listen to the webinar, The Future of Embedded Analytics is Contextual, Actionable and Pervasive: What You Need to Know, with Forrester analyst Boris Evelson to learn more about the next generation of predictive analytics and analytical applications.

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