Facing Your Data Framework

Machine learning (ML) and artificial intelligence (AI) have evolved from industry buzzwords to strategic tools that companies are implementing to transform data into actionable insights. This is evidenced by a recent study that found 97% of large organizations are making investments in big data and AI initiatives. While these statistics are encouraging, there’s a sizable leap from technology purchase to deployment, and many organizations can fall victim to common pitfalls along the way. Here are three fundamentals to consider when implementing AI, ML and predictive analytics. Understand your internal data infrastructure. Early adopters of popular technologies, like AI and ML can easily fall victim to hype. The most fundamental step toward realizing the promise of new innovations is to understand your internal data infrastructure and align it with business needs.