Be deliberate about the way you handle ideas, where you focus, and how you innovate with your customers for validation. Consider this six-step process to bring the best ideas to reality.
Big data and AI: 7 common misunderstandings
Some false notions have emerged about how AI and big data work together, leading to potential confusion. IT leaders, now's the time to clarify these seven points
5. Your organization may already be combining AI and big data and not even know it
“There are software solutions with AI capabilities already built into them, ready to be installed, trained, and used,” Burnett says, pointing to Intelligent Document Processing (IDP) software. “These solutions speed up adoption of AI and help organizations deal with specific business needs.” In these cases, you don’t necessarily need to understand the science of AI to reap the benefits.
6. Humans prove essential to combining big data and AI
Trust and transparency are key when it comes to the intersection of big data and AI. “You need solid data foundations to drive AI to the right insights,” Franco says. “And you need to bring the human into the loop with data governance to take control of the data (data quality, representativity, data privacy) and the algorithms (use explainable AI to be able to understand what’s under the cover of the algorithms.)”
7. Not all data is useful for AI
“There is generally a fine balance between having data and having the right data to provide insights when used in conjunction with AI,” Butterfield says. “AI isn’t the panacea for every problem – at least yet – and it can’t create something out of nothing. Business leaders need to be aware of this.”
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