Sukhbat Lkhagvadorj on The Hidden Bottleneck: How AI is 10x-ing Data Validation

Canton, Michigan, 28th January 2026, ZEX PR WIRE, For decades, businesses have treated data as a rearview mirror, spending millions to answer a single question: What happened last quarter? Today, the challenge isn’t a scarcity of data; it’s a surplus. Companies are drowning in information, and before any of it can be used to build a game-changing predictive model or a dashboard that wows the board, it must pass through the treacherous bottleneck of data validation.

This is the “dirty work” of data science. It’s a well-known industry statistic that data scientists can spend up to 80% of their time just cleaning and preparing data, leaving only 20% for the actual analysis that drives value. This painstaking process has long been a source of frustration, delays, and significant cost. But what if this bottleneck could be transformed into a strategic advantage?

According to Sukhbat Lkhagvadorj, a data engineer with over eight years of experience at major companies like Uber and HBO, a new generation of AI tools is making this possible. “We are witnessing a fundamental shift,” he states. “Agentic AI coding assistants are not just accelerating workflows; they are fundamentally changing how we approach data integrity. This isn’t just about saving time—it’s about building a more reliable foundation for every data-driven decision.”

The “Garbage In, Garbage Out” Crisis

The most sophisticated AI model is worthless if it’s fed corrupted or inconsistent data. This is the “garbage in, garbage out” principle, a problem that has plagued data teams for years. Traditionally, the validation process has been a manual, mind-numbing ordeal involving:

• Writing hundreds of lines of Python scripts to check for basic errors.
• Hard-coding business rules, such as ensuring a value for “age” is a positive integer.
• Endlessly debugging why a simple CSV upload crashed a data pipeline, again.

This approach is not only slow and inefficient but also brittle. A slight change in data format can break an entire script, forcing engineers to start from scratch. It’s a reactive, error-prone cycle that drains resources and stifles innovation.

The Dawn of the AI Data Engineer

The game-changer isn’t just a smarter spellchecker for code. It’s the emergence of agentic AI assistants, like Claude Code, that can function as a proactive partner in the data validation process. Lkhagvadorj explains that these tools operate less like a simple calculator and more like a senior data engineer sitting right beside you.

“Instead of just flagging a syntax error, these AI agents understand the intent behind your data,” he says. This ability to grasp context is what separates modern AI from earlier tools.

Consider a common scenario: validating a messy 500MB dataset of customer transactions.

• The Old Way: A data engineer might spend half a day writing a Python script to check for null values, validate email formats, ensure currency symbols are consistent, and flag impossible transaction dates.
• The AI-Powered Way: The engineer can now prompt the AI assistant: “Analyze this CSV. Write a Python script using Pydantic to validate the schema. Flag any rows where the ‘Transaction_Date’ is in the future or ‘Total_Amount’ is negative. Then, generate a summary report of all detected errors.”

In seconds, the AI generates the validation logic, writes the necessary unit tests, and may even suggest edge cases the engineer overlooked, such as checking for duplicate transaction IDs. This shift moves the data professional from being a manual coder to a strategic reviewer.

Unlocking a 10x Speed Boost in Data Workflows

This massive acceleration in productivity comes from eliminating the “translation layer” between human thought and code execution. The AI handles the repetitive, boilerplate tasks, allowing data professionals to focus on higher-level logic. The improvements are dramatic across the board:

• Schema Definition: Instead of manually writing boilerplate SQL or JSON schemas, an engineer can prompt the AI, “Here is a sample JSON. Generate the strictest possible schema for it.” The task is completed instantly.
• Complex Logic Checks: Rather than coding intricate “if/else” statements for every column, the prompt becomes, “Write a validator ensuring ‘StartDate’ is always before ‘EndDate’ for all rows.” The time savings can be tenfold.
• Refactoring Legacy Code: Modernizing old validation scripts from 2019 is as simple as asking, “Update this script to use the modern Polars library instead of Pandas.”
• Regex Nightmares: The hours once spent crafting complex Regex patterns to validate international phone numbers are replaced by a simple command: “Create a Regex pattern that validates various international phone formats.”

Going Beyond Syntax with Semantic Validation

Perhaps the most profound capability of AI in data validation is its semantic awareness. Standard scripts can check if a cell contains text, but they can’t determine if that text makes sense in context.

Sukhbat Lkhagvadorj highlights this with a powerful example. “An AI tool can look at a column labeled ‘US States’ and flag an entry like ‘Paris’ as an anomaly,” he explains. “It’s not a code error—’Paris’ is a valid string—but it’s contextually incorrect. This level of semantic validation was previously impossible without massive manual oversight.”

This capability extends to identifying subtle inconsistencies that human reviewers might miss. An AI can recognize that a “Job Title” entry of “12345” or “N/A” is anomalous, even if it technically fits the column’s data type. It can understand relationships between columns and flag logical impossibilities, bringing a new layer of intelligence to data quality control.

Adopting Best Practices for an AI-Driven Future

To harness this 10x potential, organizations must adapt their workflows. It requires a shift in mindset from viewing AI as a simple tool to embracing it as a collaborative partner. Lkhagvadorj recommends three key practices:

1. Treat AI as a Partner, Not a Stenographer: Don’t just ask the AI to write code. Ask it to critique your approach. Pose questions like, “What potential edge cases am I missing in this validation logic?” or “Suggest a more efficient way to validate this dataset.”
2. Maintain a Human-in-the-Loop: AI is incredibly fast, but it is not infallible. Use AI to generate the validation scripts and tests, but always have a human expert review the logic before deploying it into production pipelines. This ensures accuracy and accountability.
3. Iterate and Refine in Real-Time: Use terminal-based AI agents to create a continuous, conversational loop. Run a validation script, review the errors, prompt the AI to help fix the data, and re-run the validation—all within minutes.

The New Competitive Edge

The companies that will dominate the next decade won’t just be the ones with the most advanced predictive models; they will be the ones with the cleanest, most reliable data pipelines. By leveraging agentic AI tools for data validation, organizations are not merely saving countless hours of manual coding. They are building a rock-solid foundation for all their analytics and strategic initiatives.

“This is about reallocating your most valuable resource—your data talent,” concludes Sukhbat Lkhagvadorj. “You stop spending your week fixing broken spreadsheets and start spending it discovering the insights that truly matter.” The hidden bottleneck of data validation is finally being transformed into a source of competitive advantage, and the organizations that embrace this shift will be the ones to lead the way.

To learn more visit: https://sukhbatlkhagvadorj.com/