{"id":79169,"date":"2024-07-02T06:00:00","date_gmt":"2024-07-02T13:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=79169"},"modified":"2025-08-01T11:26:45","modified_gmt":"2025-08-01T18:26:45","slug":"unstructured-data","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/news\/stories\/unstructured-data\/","title":{"rendered":"Why Unstructured Data Makes AI More Intelligent"},"content":{"rendered":"\n

Editor\u2019s note: Einstein 1 Platform is now the Salesforce Platform. For the latest, go <\/em>here<\/em><\/a>.<\/em><\/p>\n\n\n\n

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It\u2019s no secret that great AI requires data<\/a>. From asking an AI model to build a marketing plan to having it summarize postings in a Slack channel at work, AI can\u2019t be successful without data.  <\/p>\n\n\n\n

But not all data is created equal, and to get the right results from generative AI, companies need more than what\u2019s found in spreadsheets and other structured data formats.<\/p>\n\n\n\n

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But not all data is created equal, and to get the right results from generative AI, companies need more than what\u2019s found in spreadsheets and other structured data formats.<\/p>\nRaveendrnathan Loganathan, EVP, Software Engineering<\/cite><\/blockquote>\n\n\n\n

Within every business, there\u2019s also a massive amount of information trapped in \u201cunstructured\u201d data<\/a>, such as documents, images, audio and video recordings, and social media feeds. This unstructured data could be highly valuable, providing businesses with AI insights that are more accurate and comprehensive because they are grounded in customer information. But many organizations lack the technical wherewithal to see, access, integrate, and make use of their unstructured data in any trusted way.\u00a0<\/p>\n\n\n\n

It can be a real Catch-22. Here, Raveendrnathan Loganathan<\/a>, EVP of Engineering for Salesforce Data Cloud<\/a>, shares how businesses can effectively tap into unstructured content, gather knowledge, index the data efficiently, and pull insights with retrieval augmented generation (RAG)<\/a>. <\/p>\n\n\n\n

Q. First thing\u2019s first \u2013 tell us more about unstructured data.<\/strong><\/h4>\n\n\n\n

Traditionally, companies have handled data that is found in tabular format with rows and columns. That includes customer engagement data gathered through our CRM applications. However, businesses have vital information in other formats like PDFs, emails, audio files, videos, images, and more. The data in those formats is called unstructured, and there’s huge value in unlocking it. And, thanks to the power of large language models (LLMs)<\/a> and generative AI<\/a>, we can now do just that.<\/p>\n\n\n\n

Q. What\u2019s an example of how businesses should tap into that unstructured data to enhance customer experiences and business outcomes?<\/strong><\/h4>\n\n\n\n

Imagine a customer who needs help with a recent purchase. Typically they start the conversation with the company\u2019s chatbot, which is powered by AI<\/a>. For the experience to be both relevant and positive, the entire exchange needs to be grounded in that specific customer\u2019s data: Their recent product purchase, their warranty information, any past conversations they\u2019ve had, and more. The LLM should also be tapping into company data, such as the latest learnings from other customers who have bought similar products and internal knowledge base articles. <\/p>\n\n\n\n