{"id":72510,"date":"2024-04-12T08:48:56","date_gmt":"2024-04-12T06:48:56","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=72510"},"modified":"2024-07-29T12:28:24","modified_gmt":"2024-07-29T10:28:24","slug":"the-rise-of-domain-specific-llms-for-data-analytics","status":"publish","type":"blog","link":"https:\/\/intellias.com\/domain-specific-llms-for-data-analytics\/","title":{"rendered":"The Rise of Domain-Specific LLMs for Data Analytics"},"content":{"rendered":"
Ironically, businesses aren\u2019t getting better at making decisions even though they have more access to data and analytics tools than ever. Sixty-four percent of chief financial officers surveyed by Deloitte<\/a> name inadequate technologies\/systems as one of the three greatest challenges in turning data into insights.<\/p>\n You might ask: Aren\u2019t data analytics budgets growing year over year?<\/em> They are. But so is the volume, variety, and complexity of data.<\/p>\n Businesses are paying a lot for data infrastructure and business intelligence (BI) tools, but they often see a small return on investment (ROI) and a big list of complaints from end-users about the tools\u2019 complexity, lengthy setup cycles, etc.<\/p>\n What if your team could write text-based questions instead of complex SQL queries? The combination of large language models (LLMs) and data analytics promises to commoditize access to analytics.<\/p>\n Traditional data analytics tools work with structured and numerical data. Large language models (LLMs), in turn, can interpret human language and extract sentiments, speech patterns, and specific topics from unstructured textual data.<\/p>\n By fusing LLMs with data analytics, businesses can use more data points, plus create a conversational interface to explore them<\/strong>.<\/p>\n Data Analytics<\/p>\n It\u2019s quite possible that you have already asked ChatGPT to perform some analytics tasks in your industry and had mixed results.<\/p>\n General-purpose LLMs like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) weren\u2019t explicitly trained to run route optimization scenarios<\/a> for different types of electric vehicles (EVs), for example. But thanks to various model fine-tuning techniques, you can teach general-purpose models to:<\/p>\n Effectively, fine-tuned LLMs help accomplish two things for data analytics<\/strong>: Improve access to different data assets through a conversational interface and help deliver more comprehensive, contextually relevant insights.<\/p>\n That\u2019s exactly how many data-driven companies already use LLMs. Colgate-Palmolive, for example, uses generative AI<\/a> to synthesize consumer and shopper insights and better capture consumer sentiment. Morgan Stanley has launched an AI workforce assistant<\/a>, which can handle a range of research inquiries (What\u2019s the projected interest rate increase in April 2024?<\/em>) and general admin queries (How can I open a new IRA account?<\/em>).<\/p>\n According to a 2023 global study by Amazon Web Services, 80% of chief data officers<\/a> expect that generative AI will positively transform their organization\u2019s business environment \u2014 and for some good reasons.How LLMs and data analytics enable data-driven workflows<\/h2>\n
How LLMs enhance data analytics<\/h3>\n
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