Cleaning Your Data Swamp with a Multidimensional Data Observability Platform

  • Excess Cost: Unneeded and redundant data costs money, but delivers no value
  • Delay: Friction in finding, exploring, and validating data wastes time
  • Missed Opportunity: Data that goes unused due to poor visibility, access, or trust could be delivering value to the business
  • Catalog: Inventory data assets to improve data management and data discovery
  • Size and Cost: Identify data that’s consuming the most storage and processing resources. Prioritize optimization efforts there for the biggest cost savings.
  • Temperature: Identify infrequently used data (“cold data”) to optimize storage for cost savings.
  • Context: Automate the classification and tagging of data assets, create business glossaries, enrich, and crowd-source to make data discovery and governance easier
  • Utilization: Identify which people and processes are creating and accessing data
  • Redundancy: Identify similar data assets for potential consolidation
  • Interdependencies: Identify relationships and data lineage to understand dependencies and impact to change
  • Data Trust: Build trust in data by monitoring and analyzing a comprehensive set of data reliability concerns. Automate the creation of data quality rules, reconcile data from source to target, monitor schema drift and data drift, identify anomalies and much more.
  • Accelerated Transformation: Better organization, visibility, and reliability makes it easier for users to find data, trust and innovate with data.
  • Higher Data ROI: Eliminating cold data and redundant data leads to lower costs for infrastructure and maintenance.
  • Better Outcomes: Analytics and automation for improved data reliability ensures the processes and decisions driven by data are of the highest quality.



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The Data Observer

The Data Observer

Thoughts and trends on data observability