I recently wrote about the role data observability plays in generating value from data by providing an environment for monitoring its quality and reliability. Data observability is a critical functional aspect of Data Operations, alongside the development, testing and deployment of data pipelines and data orchestration, as I explained in our Data Observability Buyers Guide. Maintaining data quality and trust is a perennial data management challenge, often preventing organizations from operating...
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Topics:
data operations,
Analytics & Data,
Data Intelligence
Enterprises are embracing the potential for artificial intelligence (AI) to deliver improvements in productivity and efficiency. As they move from initial pilots and trial projects to deployment into production at scale, many are realizing the importance of agile and responsive data processes, as well as tools and platforms that facilitate data management, with the goal of improving trust in the data used to fuel analytics and AI. This has led to increased attention on the role of data...
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Topics:
data operations,
AI & Machine Learning,
Analytics & Data
The emergence of generative artificial intelligence (GenAI) has significant implications at all levels of the technology stack, not least analytics and data products, which serve to support the development, training and deployment of GenAI models, and also stand to benefit from the advances in automation enabled by GenAI. The intersection of analytics and data and GenAI was a significant focus of the recent Google Cloud Next ’24 event. My colleague David Menninger has already outlined the key...
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Topics:
Analytics,
natural language processing,
AI & Machine Learning,
Data Platforms,
Analytics & Data,
Generative AI