Trustworthy Data in the Cloud

Importance of Cloud Data Validation

Data errors and inconsistencies accumulate as data moves in and out of the Cloud (and/or Data Lakes). This causes <40% of data Clouds (and Lakes) to be reliable and usable. Lack of Cloud data validation is an existential threat to data sensitive organizations.

Challenges with Cloud Data Validation

  • 1000’s of tables and files within each data repository have uniquely different data validation rules. Making it very hard for SME’s to identify 100,000’s of rules for even medium sized repositories.
  • Compounded by the lack of sufficient number of SME’s in any organization.
  • Most critical data validation checks have to be specific to the organization’s business context. Data reasonableness based on what has been acceptable historically is a lot more reliable than static data validation checks that are unchanging as the business context evolves.
  • Majority of vital DQ checks are dynamic, hard to code and needs constant update.

Best Practices for Cloud Data Validation

Increase trust by validating every piece of data as it moves in and out of the Cloud (and/or Lake) autonomously. Based on extensive interactions with Cloud and Lake users we have compiled the best practices to keep the data clean and not a swamp. What separates the most successful organizations is their ability to validate data reasonableness based on what has been acceptable historically to their organization’s specific business context. The data validation checks of the best organizations are dynamic, the checks evolve as their business context evolves.

The challenges for good data quality and governance are:

  • Knowing the Data Quality Validation rules
  • Coding it
  • Executing the rules optimally so it does not choke up the source and related systems
  • Maintaining and updating the rules over time

It’s most commonly mistaken that the first step is the key challenge. With 20 yrs of DQ consulting experience we have seen failure happening at all 4 steps. They are all complex, requiring different skills and different processes to resolve.

The Best Practices Guide will show you the cognitive checks the best organizations have put in, which evolve as the business context evolves. They are very hard to program using traditional tools.

For a Free Copy of the Best Practices for Cloud Data Validation

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Write to us ( or call +1-385-393 4436) and you’ll have it in 24 hrs.

Solution for Cloud Data Validation

Autonomous Cloud Data Validation with DataBuck

FirstEigen’s DataBuck solves your AWS and Azure Cloud data validation problems with out-of-the-box functionalities, without needing any coding or configuration. DataBuck lives in your Cloud environment within your firewall. It connects to the data directly and creates detailed Hyper Fingerprints of every data set, capturing the patterns and relationships between the various elements and microsegments of data. DataBuck:

  • Reduces the usual data validation program from >9 months to just a few weeks.
  • Over 10,000 rules based on historical context are automatically created for every data set.
  • These rules are dynamic, and autonomously updated over time.
  • Reduces error rates by over 90%.
  • DataBuck’s DQ specific Spark algorithms deliver >10x speed over any other traditional approach to DQ.


2 min video of how the Data Quality best practices are automated with AI/ML

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