DQC hat dies direkt geteilt
𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤𝐬… 𝐘𝐞𝐬, 𝐰𝐞 𝐜𝐚𝐧! 𝐁𝐮𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞? 🤔 Traditionally, data quality was a collaborative effort: domain experts outlined what a “relevant” data check should look like, and data engineers turned those requirements into SQL/Python scripts or system configurations. Now, with advances in technology, we 𝘤𝘢𝘯 auto-generate data checks. But the question remains: 𝘴𝘩𝘰𝘶𝘭𝘥 we? It depends…🔍 Many automated checks can lead to issue overload and alert fatigue, especially if they target data that’s rarely used or low-value. This is where data quality automation becomes counterproductive. 𝐁𝐞𝐟𝐨𝐫𝐞 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚 𝐝𝐚𝐭𝐚 𝐜𝐡𝐞𝐜𝐤, 𝐚𝐬𝐤 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟: 📊 𝘞𝘩𝘢𝘵 𝘥𝘢𝘵𝘢 𝘪𝘴 𝘨𝘦𝘯𝘶𝘪𝘯𝘦𝘭𝘺 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴-𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵? 🎯 𝘞𝘩𝘦𝘯 𝘪𝘴 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢 𝘧𝘪𝘵-𝘧𝘰𝘳-𝘱𝘶𝘳𝘱𝘰𝘴𝘦? Including these considerations in the automation of DQ checks isn’t trivial. At DQC, we blend fine-tuned GenAI models, Machine Learning and statistics to automate data checks 𝐏𝐋𝐔𝐒 human feedback to generate 𝘉𝘶𝘴𝘪𝘯𝘦𝘴𝘴-𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘤𝘩𝘦𝘤𝘬𝘴. However, no tech is a silver bullet to solve all data issues. Automated checks don’t replace people—they empower them. Collaboration and control are still essential. But with smart tech, we can make the data quality process and work 𝘮𝘰𝘳𝘦 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵, 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘧𝘶𝘭, 𝘢𝘯𝘥 𝘦𝘷𝘦𝘯 𝘦𝘯𝘫𝘰𝘺𝘢𝘣𝘭𝘦. 😊 Let's chat about how you can harness the power of 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 for your business—by combining artificial with human intelligence. Reach out!