Problem: As data moves through the systems, errors sneak in
Challenge: It’s hard to find a needle (errors) in a haystack (high volume flow), and its much harder when there many haystacks (flowing through multiple platforms, like SQL/noSQL/Hadoop/ Cloud/etc.)
Untrustworthy data is very expensive. Gartner reports that 40% of data initiatives fail due to poor quality of data and affects overall labor productivity by ~20%. That is a huge loss on which it’s hard to even put a cost figure on! Forbes and PwC report that poor Data Quality was a critical factor that leads to regulatory non-compliance. Poor quality of Big Data is costing companies not only in fines, manual rework to fix errors, inaccurate data for insights, failed initiatives and longer turnaround times, but also in lost opportunity. Operationally most organizations fail to unlock the value of their marketing campaigns due to Data Quality issues.
In the regular-data world, data is validated without introducing significant delay as the data-volume and velocity are smaller and manageable. When data flows at a high volume, in different formats, from multiple sources and through multiple platforms, validating data is a nightmare. Incorrect data can cause serious problems. Imagine the regulatory consequences when a bank reports their risk exposure using incorrect data. Big-data teams rely on manual/ad hoc methods to validate the data quality using hard-coded, big-data based scripts. These are susceptible to human errors or system-change related errors, and are ineffective in detecting outliers.
Why data needs to be validated?
“the ETL process for credit risk data warehouse has ~25 releases each qtr. Even with quality assurance processes, error creeps in.”
– IT Director, a major Financial Institution
Like it or not, errors creep in because change is constantly happening somewhere in your system. One of the biggest problems with big data is the tendency for errors to snow ball. Using inaccurate data is a serious hidden financial risk. Either you double check and weed out the errors or brace for the risks.
– As data is copied between different systems, at different locations, which are updated at different times, data gets out of sync
– Additional New Challenges are posed by data flowing
• at a high volume
• in different formats
• from multiple sources (internal, external)
• through multiple platforms (SQL, No-SQL, Hadoop)
• via On-Premise or Cloud
• And none of which play well each other
Above complexity of data makes it harder to validate every piece of data
– Many copies creates more management headache
– Out of sync data breaks established process
– IT and data team have to spend significant time to ensure data is accurate, which delays the ability to deliver simple requests for data
– Above described complexity of data makes it harder to validate every piece of data due to lack of time and tools
– There is a finite possibility that business gets inaccurate data to work with!
If you’d like to learn how the only Big Data Validation tool in the market can help you, ping us…
DataBuck is hosted on the Cloud and can be setup in under 30mins. We can help you run a proof of concept (POC) to evaluate if DataBuck is right for you.