| | NOVEMBER 20248CIOReviewIN MY OPINIONAs an executive leader in data, I am constantly asked, "Can AI do that?" Yes, Generative AI (GenAI) is poised to revolutionize numerous industries, from content creation and medical advances to technology automation and financial modeling. However, in my experience (or the "school of hard knocks"), I know that for enterprises to harness the true potential of GenAI, they must first ensure their data is ready. Unlike traditional AI applications that rely on labeled datasets, GenAI thrives on massive amounts of high-quality, clean data--highlighting the importance of using accurate and well-structured data to train GenAI models.In other words, there is a lot (and I mean A LOT) of hard work to do before one can harness the power of GenAI.The Data Readiness ImperativeEnterprise data, by its very nature, is often complex. It can encompass a vast array of information from multiple sources with varying degrees of structure and consistency. In my experience, enterprise data is a mishmash of legacy databases, third-party sources, and many "snowflakes" from different departments. (You might be surprised at the number of ways the advertiser Coca-Cola could be listed in a database.) This inherent complexity can create significant roadblocks for GenAI adoption. Imagine trying to train a large language model to generate realistic dialogue if your data consists of siloed spreadsheets, poorly documented legacy systems, and inconsistently formatted records. The results would carry a high likelihood of AI hallucinations and confabulations.Key Components of Data Readiness for GenAIAt this early stage of GenAI experimentation and adoption, I strongly advocate that it's not about the "new and shiny tech" but the investment in data readiness. (I might suggest this is the "measure twice, cut once" approach for enterprises.) Here are some critical aspects to consider when preparing your enterprise data for GenAI:Data Quality and Integration: Investing in data-cleansing techniques and establishing data quality standards can significantly improve GenAI performance. GenAI is highly sensitive to data quality issues like errors, inconsistencies, and biases. Inaccurate data can lead to skewed or nonsensical outputs from your GenAI models. Additionally, integrating data from disparate sources into a unified and accessible enterprise data warehouse, with data that is governable through upstream normalization techniques, is essential for GenAI to function effectively.UNLEASHING THE POWER OF GENERATIVE AI: HOW TO PREPARE YOUR ENTERPRISE DATABy Karen Pfeifer, SVP, Enterprise Data, Audacy, Inc Karen Pfeifer has over 25 years of extensive experience in the media industry. With a proven track record in leadership roles at renowned companies such as Disney, Hulu, Comcast/NBCU, and DirecTV/AT&T, Karen is a seasoned expert in data strategy, product development, governance, data science, and analytics. A proud graduate of UCLA, where she earned her B.A. in Music, Karen further honed her analytical skills by obtaining an M.S. in Analytics and Data Science from St. Joseph's University. Her unique blend of creativity and technical expertise positions her at the forefront of transforming how data drives decision-making in the media landscape.Karen Pfeifer
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