Companies no longer question the importance of data analytics for their business success. With the help of data, brands can predict business outcomes, detect purchasing patterns, track customer behavior, and improve overall decision-making. However, many organizations still struggle with implementing the needed steps for robust data analysis. They often lack the time and expertise to use data to its fullest potential.
Organizations across all industries are racing to understand large language models (LLMs) and how to incorporate the generative artificial intelligence (AI) capabilities provided by LLMs into their business activities. Thanks to LLMs’ broad utility in classifying, editing, summarizing, answering questions, and drafting new content, among other tasks they are being embedded into existing processes and used to create new applications and services.
How our analytics engineering team uses ChatGPT to write the most efficient dbt packages for your most common analytics use cases.
It’s no surprise that cloud spending is rapidly increasing, so it’s also no surprise that controlling those rapidly increasing cloud costs is a top priority for business, technology, and data leaders. According to Gartner, the use of public cloud computing has increased IT spending for most organizations (54%) over the last three years, with only 29% reporting that the cloud decreased IT spending.
