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Active metadata management is a set of capabilities that enables continuous access and processing of metadata that support ongoing analysis over a different spectrum of maturity, use cases and vendor solutions. Active metadata outputs range from design recommendations based upon execution results and reports of runtime steps through, and indicators of, business outcomes achieved. The resulting recommendations from those analytics are issued as design inputs to humans or system-level instructions that are expected to have a response.

• Monte Carlo
  4.0

  1 Review

Gartner defines data observability tools as software applications that enable organizations to understand the state and health of their data, data pipelines, data landscapes, data infrastructures, and the financial operational cost of the data across distributed environments. This is accomplished by continuously monitoring, tracking, alerting, analyzing and troubleshooting data workflows to reduce problems and prevent data errors or system downtime. The tools also provide impact analysis, solution recommendation, collaboration and incidence management. They go beyond traditional network or application monitoring by enabling users to observe changes, discover unknowns and take appropriate actions with goals to prevent firefighting and business interruption. Organizations are looking to ensure data quality across different stages of the data life cycle. However, traditional monitoring tools are insufficient to address unknown issues. Data observability tools learn what to monitor and provide insights into unforeseen exceptions. They fill the gap for organizations that need better visibility of data health and data pipelines across distributed landscapes well beyond traditional network, infrastructure and application monitoring.

• Monte Carlo
  4.6

  32 Reviews