According to recent Gartner reports, an estimated 85% of artificial intelligence (AI) projects will fail through the year 2022. This estimation is likely to cast some doubt on the realm of artificial intelligence and its ability to deliver value. However, the reason that so many AI projects fail is not due to the AI processes themselves, but rather the lack of strong data governance, collaboration, and problem definition. Organizations that successfully leverage AI typically start their design with the goal of AI in mind. This approach allows organizations to work backwards, guiding them through the foundational components that help contribute to the end goal.
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When you hear the term “machine learning,” do you think to yourself, “How does machine learning really work?” Well, machine learning uses historical data, and what I mean by this is past data. This data could be from databases, Hadoop systems, CSV format, or streaming data from a social media website.
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Machine learning is changing the way the world thinks and operates. Let’s face it, when the words “Machine Learning Algorithm” present themselves in a room full of peers, everyone takes a deep breath. Some are confused by the thought of math; others are thinking, “Oh no, which algorithm do we use for this problem?”
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