Data observability has become a critical enterprise capability allowing organizations to monitor and understand the health of their data systems. This has only become more important as enterprise data proliferates exponentially. New research by global leader in data integrity Precisely and BARC shows that 76% of organizations have already formalized their observability programs and data quality and pipelines, reflecting a growing commitment to building more reliable data foundations.
The meteoric rise of new AI solutions—especially generative AI (GenAI)—is undeniably the main driver of this trend. As organizations strive to operationalize next-generation AI technologies, they’re moving into relatively unchartered territory marked by a massive growth of unstructured and semi-structured data. Many AI applications, such as customer service chatbots or employee copilots, require access to vast repositories of proprietary data to be useful. That data needs governance too.
Effectively managing enterprise risk in the era of AI requires that data management and observability be applied across the board, notably to data that’s used in AI model training or inference. Depending on the specific use case, this might include semi-structured data like JavaScript Object Notation (JSON) or unstructured data like text, images, audio, or video.
Observability is now essential for the transparency and performance of AI systems, and the lines have blurred between data observability, data quality, and data governance. Today’s data management solutions must be built around a holistic approach that centers on trust. After all, if you can’t trust the underlying data used to inform an AI model, then you can’t trust the outputs from that model. This is routinely demonstrated in mainstream GenAI applications, such as ChatGPT or Google Gemini, which can ‘hallucinate’, resulting in completely fabricated outputs and misinformation.
For software companies or data teams wanting to ensure the integrity of their data and the AI systems that rely on it, establishing mature data observability practices is vital for success. Basic monitoring is no longer sufficient. Thus, observability must move beyond conventional databases and pipelines to continuously observe the health, quality, and lineage of unstructured data. A fully matured strategy should also include monitoring for data drift, AI model performance degradation, and potential biases in training data.
Observability, quality, and governance are no longer separate goals, so it’s imperative to break down the siloes between these equally critical functions. Fortunately, to help ease the burden of innovation, especially in the realm of GenAI, leading data management platforms are now unifying these functions and supporting diverse data types. This allows for a proactive, automated approach that goes beyond reactive alerting to proactively preventing incidents that can reduce data trust and result in increased business risk.