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Data Observability


Challenges Faced

Data Profiling

Traditional systems lack to present the issues and risks associated with those issues due to data distribution.


Data Lineage

Traditional systems hold back the root cause identification as teams don’t have the data lineage over the distributed data layer.


Data Quality

Non-Availability of profiling and lineage tends to lose business Revenue, and the existing systems start building data quality issues.


Data Observability Solutions for Enterprises


Integrate the ability to highlight data quality concerns through its augmented data intelligence platform built over the Data Observability layer.


Modernize the data platform access by deep authentication of data lineage patterns and data access state.


Risks can be well managed in real-time through the intelligent data profiling with tailback of the previous analyses and its summary reports.


The Data Observability platform brings together the teams to share the troubleshooting highlights, which helps in near real-time monitoring of the data platform.


Features of the Data Observability Solutions


Data profiling supports correcting the ETL pipelines by helping in data quality management.


Better data profiling uncovers the data lineage challenges faced during the early stages of data platform development.


Data lineage targets identifying the root cause analysis of the missing link in the data.


Data quality makes the system more reliable in terms of delivering the best of the business requirements from data.


Observability intelligently bundles the automatic data issues and PII solutions.


Cardinality, relationships, and key integrity help in fixing the problematic data issues that bring reliability to the ETL processing.

Data Observability Implementation Strategy

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    Distribution: Observability help identify the data accuracy and consistency.

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    Volume: Validation is quick to understand if all the tables have all data.

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    Schema: Data quality can be managed from the data schema information tab for correctness in data.

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    Lineage: Where the data landed and what sources have the data access or lineage.

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What are the values added by Solution?


Becoming Data-Driven: Data Observability brings in the atmosphere to become a data-driven organization as it integrates data quality with data profiling.


Improved Data Governance: Better the data correctness, the data governance will improve automatically.


Enhanced Data Quality Solutions: Data-Driven decisions bring the comfort to enhancing more KPIs as quality is maintained throughout.


Reduced Troubleshooting Time: Less time to travel in the identification of the issues in data as data mesh solution helps in tracking the near real-time causes.