What is Data Observability?
Data observability is the ability to see and understand data as it flows through an organization. It enables data professionals to identify and track metadata issues, ensure data quality, and optimize data pipelines.
Data observability is crucial to data-driven organizations because it allows them to see how their data infrastructure works and identify areas for improvement. By understanding the dependencies between data sources and systems, data teams can optimize data pipelines and avoid data quality issues.
Why does Metadata matter so much in Data Observability?
It’s important to know where data comes from, how it flows through different systems, and how it’s transformed along the way. However, data pipelines are often complex and can change quickly, making it difficult to keep track of everything manually. This is where metadata comes in. Collecting metadata at every stage of the data pipeline can help give you a complete picture of your data landscape. This data can then be used to build dashboards and identify issues with data quality.
Achieving data observability requires a platform that can collect and store metadata across the data landscape. This metadata includes information about data sources, data pipelines, and data quality. A metadata management platform can help organizations to build a complete picture of their data landscape and identify opportunities for improvement. When exploring observability, it’s important to prioritize features that will make it easier to collect and track metadata. Businesses are encouraged to look for a platform that offers an easy-to-use interface for managing metadata, as well as report generation and alerts to help you identify problems early on.
Why does Data Quality matter in Data Observability?
Data quality is a key concern in data observability. Poor data quality can lead to inaccurate insights and decision-making. When exploring data observability, it is important to prioritize features that will help to ensure data quality, such as data profiling and cleansing, and metadata management. Prioritizing these features is highly recommended when exploring observability as a design concept.
Data observability is a crucial tool for data-driven organizations. By understanding the dependencies between data sources and systems, organizations can optimize their data pipelines and avoid data quality issues. A metadata management platform can help organizations to build a complete picture of their landscape and identify opportunities for improvement. Quality should be a key concern in any data initiative, and a platform that includes features like profiling and cleansing should be prioritized when exploring data observability.