Companies worldwide still seem to struggle with the idea that technology can improve operations, profitability, and in many cases, even employee morale. It may be that those needing the technology don’t have a voice to express how painful their process is without it, or it could be that leadership doesn’t feel the pain and therefore concludes that it doesn’t exist. These situations are all too common today, in a world that professes to be “technologically advancing” with AI, we are still “stepping over the dollar to pick up the dime” when it comes to data technologies that can solve real problems.
Validatar is a prime example of this phenomenon, a cutting-edge solution designed to streamline the ETL testing process and alleviate the burdens faced by QA professionals. By leveraging automation technology, Validatar empowers teams to enhance their testing workflows, minimize errors, maintain data integrity, and automate so many very time consuming and tedious tasks.
Let’s take a quick snapshot of a QA Analyst in a large insurance company named “Jessica”. Every morning, Jessica logs in before the coffee finishes brewing. Her inbox is already a battlefield—overnight data loads have triggered alerts, and the offshore dev team has dropped a fresh batch of ETL scripts into the staging environment like a box of tangled wires. The monthly release cycle is only ten days away, and the pressure is mounting.
This is a repeated cycle that happens month over month the business requirements are captured by the front end business analysts, the code is hastily written to meet the deadline, and often the ETL QA Analyst is left with very little time to make corrections and updates prior to the production go-live, creating immense pressure and often impacting the morale of the team. Nobody likes to be the tail of the dog.
Nonetheless, Jessica digs in and starts with the usual suspects: row count mismatches, null values where none should exist, and that one lookup table that always forgets to join properly. Her test cases are meticulous—she’s built them over years of hard-earned trial and error, and hundreds of hours of manual coding. The trouble is, the business logic keeps evolving, and so do the edge cases. One missed transformation, and downstream reports can have errors that may translate into massive financial implications, regulatory fines and more. This potential for disaster only heightens Jessica’s stress levels and those of her team members with each passing cycle. Their hair is on fire, and they are ultimately going to be accountable in the end.
Midday, she’s deep in SQL, comparing source-to-target mappings, validating Slowly Changing Type 2 Dimension changes, and chasing down a rogue timestamp format that’s breaking the load. The dev lead pings her: “Can you sign off on the new pipeline by EOD?” She sighs. The pipeline works—but only if you ignore the fact that it overwrites historical data. She flags it, documents it, and pushes back. Again, because if everything is an emergency, nothing is.
By late afternoon, she’s in a release planning meeting. The product owner wants to fast-track a new data feed from a third-party vendor. Jessica asks the uncomfortable question: “What’s the SLA on that feed? What happens if it’s late or flawed?” Blank stares. She knows she’ll be the one catching that fallout when it breaks as well.
As the day winds down, she updates the regression suite, logs three new defects, and writes a polite-but-firm note to the offshore dev team: “Please reprocess with the corrected logic. The current load fails validation on key dimensions.” She’s not just testing data—she’s guarding the integrity of decisions made by executives, analysts, and auditors. Jessica closes her laptop. Tomorrow, the cycle continues, same pressure, same risks. But for tonight, the data is clean, the pipelines are stable, and the dashboards and reports will be accurate.
This is not an isolated incident and happens more often than most can imagine. Tools and technologies like Validatar can help situations like this by creating repeatable processes, reusable test-templates that can be updated quickly and scheduled to run on regular intervals – so she would come into an organized list of scripts needing attention instead of the “Spaghetti” referred to earlier. Everyone in a role similar to Jessica faces bottlenecks, undue pressure, and slowly declining morale over time. They are the tail of the dog and get nothing but pressure to complete testing prior to the production release. Business requirements for each cycle consume a significant amount of time, while the usually much larger ETL teams quickly code the requirements and crush the QA team just before the deadline. This invariably leads to mistakes, resulting in inaccurate reporting and reputational damage to the data teams.
Validatar isn’t just a tool—it’s a turning point. For QA analysts like Jessica, who live at the mercy of chaotic release cycles and last-minute ETL changes, Validatar offers a structured, repeatable, and proactive approach to data quality assurance. By replacing brittle, manual test cases with reusable templates and scheduled validations, it transforms firefighting into foresight. Instead of waking up to inboxes full of spaghetti logic and broken pipelines, Jessica can start her day with a prioritized dashboard of actionable insights—organized, automated, and aligned with business logic. Validatar empowers her to shift from being the last line of defense to the first order of business, safeguarding data integrity while reclaiming time, morale, and strategic influence– not to mention, better data for all!
See how Validatar can streamline and automate your process in just a matter of a few weeks, and for much less than you might imagine! Visit www.validatar.com today for a no obligation consultation and demo.