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Pristine data quality is not just a priority, it is table stakes for SonarX’s institutional clients. Amongst data experts in the blockchain industry, it is well known that data quality is a major challenge. Our clients need the utmost accuracy and completeness from their on-chain data infrastructure and they trust SonarX for our unwavering commitment to superior data quality. This is a responsibility we take with the utmost seriousness.
The SonarX Trust Center transparently outlines our data quality assurance processes, provides insights into common issues and their causes, and allows our clients to benchmark their data accuracy.

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SonarX Enterprise Data Quality (DQ) Framework

On-chain data, even when taken directly from nodes, tends to be inconsistent. Factors such as network latency, node synchronization, and variations in data formats can cause inconsistencies and diminish data cleanliness. The SonarX Institutional DQ Framework is a systematic approach to identifying and rectifying all on-chain data issues across all blockchains. The framework is designed to continuously perform rigorous checks ensuring reliability and completeness across vast amounts of data. Below are the core tenets of our framework: 1.      Block Sequence Validation
Objective: Confirming the relationship between block and parent hash on the block level.
Problem Solved: Ensuring the integrity of the blockchain structure and its linkage. 2.      Token Metadata Refresh
Objective: Updating and refreshing metadata associated with tokens on the blockchain.
Problem Solved: Ensuring accurate and up-to-date information about tokens for precise analytics. 3.      Handling Reorganizations
Objective: Handling data adjustments resulting from blockchain reorganizations.
Problem Solved: Mitigating disruptions caused by conflicting blocks and ensuring accurate data representation. 4.      Duplicates
Objective: Ensuring there are no duplicate entries in our Snowflake database.
Problem Solved: Preventing the recording of identical information more than once, maintaining data integrity. 5.      Gaps
Objective: Identifying and addressing missing or incomplete data sequences in our Snowflake database.
Problem Solved: Ensuring a continuous and complete record, eliminating gaps in the dataset. 6.      Checksums
Objective: Verifying data integrity by calculating and comparing checksums.
Problem Solved: Detecting any corruption or tampering of data, ensuring the accuracy and reliability of information. 7.      Table Flow Validation
Objective: Confirming consistency between raw data and end-state curated data.
**Problem Solved: **Ensuring that ingested attributes and information remain consistent after data processing. 8.      Nulls
Objective: Identifying and addressing instances where essential data fields are empty.
Problem Solved: Ensuring completeness and integrity of information by eliminating null values. 9.      Match Checks
Objective: Verifying that block and transaction hashes match, indicating data tampering.
Problem Solved: Ensuring the security and reliability of blockchain data by detecting any unauthorized modifications.

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Diving Deeper into Quality

A Deep Dive into Sonarverse’s Data Completeness Checks

In the dynamic world of blockchain, Sonarverse prioritizes data accuracy and completeness through a meticulous Data Quality process, overcoming challenges like network latency and data format variations to provide reliable insights for users.

How SonarX's Data Quality Practices Address Issues on the Blockchain

SonarX is steadfastly dedicated to superior data quality, exemplified through its rigorous procedures and the comprehensive oversight provided by the Data Quality Trust Center.