Data Quality Validation with DataBuck | FirstEigen

ML-Powered Data Trustability Platform:

Measure the trustworthiness and usability of data on the cloud, without IT intervention.

Autonomous alation data quality on azure Validation with DataBuck Eliminate unexpected data issues Contact us to schedule a free demo.

Data errors get amplified as it flows downstream through the data pipeline

In spite of investing in DQ and Observability tools, due to a lack of trust in data:

40% failure of data initiatives

~20% drop in labor productivity

What is Trustability?:

Data Trustability is sought by Catalog teams and Data Management teams:

Data Profile

Objective Data Trust Score (DTS) for every DQ dimension with AI/ML

Aggregate DTS

Trustability throughout the Data Pipeline:

Data fingerprint

Self-learning

Dynamically evolves

Known-known errors

Unknown-unknowns

Objective Data Trust Score

Challenge with existing non-ML tools to determine Trustability:

Challenges with Traditional Approach

Knowledge Gap:

Many times, data quality analysts are unfamiliar with the data assets obtained from a third party, either in a public or private context. They need to engage with subject matter experts extensively in order to build data quality criteria.

In a snowflake data quality tools Cloud, as organizations share datasets with each other, data quality analysts may not have access to subject matter experts from another organization.

Processing Time:

Time to Use the Dataset: Even if you are intimately familiar with the dataset, it can take between 2 to 5 business days to analyze the data quality.

Snowflake Data Cloud reduces the data exchange time drastically. However, adding additional days to manually perform the data quality adds to the timeline and defeats the purpose.

Why is it Important to Use a Machine Learning based Approach:

Machine Learning is known for solving complex problems and executing results faster than intended without any human error.

Using ML in Snowflake Data cloud has some advantages:

Machine Learning helps to objectively determine data patterns or data fingerprints, and translate those patterns to data quality rules.

Machine Learning can then use the data fingerprints to detect transactions that do not adhere to the rules.

Implementing an ML approach can help to quickly assess the data health check

ML is usually more comprehensive and accurate than a human-driven data quality analysis.

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