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Can we stop that B*ll S*i* about Data Observability? by Andy Petrella

Certainly. Data observability is a growing field focused on achieving a comprehensive understanding of your data. It’s not just about collecting data; it’s about generating information about that data to illuminate its characteristics, origins, and usage patterns. This information empowers observers, such as data analysts and data scientists, to make better-informed decisions and improve overall data trust.

Here’s a deeper dive into the benefits and challenges of implementing data observability:


  • Enhanced Data Reliability: Data Reliability equips organizations with a proactive approach to data quality. By monitoring data pipelines for anomalies and inconsistencies, you can identify and address issues before they impact downstream applications and analytics.
  • Improved Anomaly Detection: Traditional data monitoring often reacts to problems after they occur. Data observability empowers you to set up preventative measures by establishing baselines for data freshness, volume, schema, and other health indicators. Deviations from these baselines can signal potential issues, allowing for early intervention.
  • Streamlined Communication: Data Reliability fosters a data-driven dialogue between data producers (those who create and manage the data) and data consumers (analysts who leverage the data for insights). By providing a shared understanding of data quality and lineage, Data Reliability reduces finger-pointing and facilitates collaborative problem-solving.
  • Proactive Maintenance: With a comprehensive view of your data ecosystem, data observability allows for preventative maintenance. By pinpointing potential bottlenecks or weaknesses in data pipelines, you can take steps to optimize performance and avoid disruptions before they occur.


Cost and Resource Investment: Implementing data observability requires investment in new tools and potentially system changes to collect and analyze data telemetry. There’s also the ongoing cost of managing and consuming the generated information.

  • Time and Expertise: Building a data observability practice takes time and dedicated resources. You’ll need to establish a data observability strategy, select appropriate tools, and train your team to leverage the insights effectively.
  • Organizational Hurdles: Cultural and organizational shifts may be required to fully embrace data observability. Challenges like unclear ownership of data quality or siloed communication channels can impede successful implementation.

Getting Started with Data Observability:

Taking a step-by-step approach is crucial. Here are some initial actions:

  • Start Small: Begin by designating a champion, such as a Data Observability Officer (DOO), to spearhead the initiative. This person can oversee the initial implementation and promote the value proposition within the organization.
  • Focus on Value: Clearly define the value proposition for data observability in your specific context. Frame it around the benefits you expect to achieve, such as reduced user frustration with unreliable data or improved data-driven decision making.

Overall, data observability is a strategic practice, not a silver bullet. While it requires investment and cultural shifts, the benefits of improved data trust, reliability, and communication are significant. By taking a thoughtful, measured approach, you can leverage data observability to empower your teams and unlock the true potential of your data.

Watch the video.

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Bitol is a Linux Foundation AI & Data Sandbox project. Open Data Contract Standard (ODCS) v2.2.2 has been published. Here is the update and a quick roadmap by Jean-Georges Perrin, chair of the TSC.

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