Cleaning Up Our Messy Data: How AI Is Changing the Game

We are drowning in data. Every platform, smartwatch, and smartphone fragments our lives into quantifiable tidbits, yet most of it remains incoherent and unusable. 

Companies know this, which is why tech giant Meta invested $14 billion USD last summer to acquire a 49% stake in the data-labeling startup Scale AI, making a calculated and strategic move to secure high-quality training data for its AI models.

The reliability of large language models depends entirely on the quality of the data they are fed – in short, “garbage in, garbage out.” Today, however, the real challenge companies face is turning a flood of raw information into actionable data. 

The solution may be hiding in plain sight: AI itself can help by generating strategies to bypass the tedious task of labeling massive datasets or combing through endless spreadsheets, turning chaos into usable, human intelligence. 

When data gets messy: The hidden costs for companies

According to Gartner research from 2020, poor data quality costs organizations at least $12.9 million USD a year, impacting productivity and leading to poorly informed decisions and inaccurate reporting. 

The consequences of messy data are all the more evident in sectors such as healthcare. Incomplete health records, billing details, and mismatched data across systems can lead to misdiagnoses, treatment errors, and inefficient resource allocation. In the long term, this drives up costs and erodes trust in these systems.

Meanwhile, in logistics, mismatched data among suppliers and distributors can result in delays or inventory shortages. An incorrect delivery address or an outdated stock record can have a ripple effect throughout the entire supply chain, leading to missed deadlines and dissatisfied customers. 

“By being able to anticipate or understand what could happen [along the route] – based on combined, past data – you can really cut these inefficiencies,” Asparuh Koev, CEO of logistics AI company Transmetrics, noted while in conversation with Unite AI.

In more practical terms, messy data is costly. The 1-10-100 rule illustrates this: it costs $1 to check the data as it is entered, $10 to clean it up afterwards, and $100 if nothing is done.

What AI-powered platforms bring to the table

As businesses grapple with growing amounts of dirty data, they are turning to AI for solutions. Emerging AI-powered platforms now automate the data cleaning process, ensuring cost-effectiveness and improving accuracy.

Robert Giardina, founder of Claritype, one such platform, explained AI’s process: 

“It converges data into a common format: part of the process is to convert each datum into a canonical format that suits the business.” 

Claritype’s AI goes beyond simple standardization, however. The platform’s supervised repair allows organizations to cross system boundaries in pursuit of answers to their most urgent questions, breaking down silos. 

“Systems that were previously kept separated each hold a piece of the answer for questions that span the entire business,” Giardina told Unite AI. 

If a key supplier is affected by a shipping delay, for instance, only by connecting suppliers to orders and customer history can a company determine which of their top customers should be notified first about the delay.

“Our ultimate goal is to extend this interconnected thinking to unify every shard of data in the enterprise so we can make every question easy and immediate to answer,” Giardina said. 

This kind of interconnected thinking is representative of the broader change in mindset occurring in companies today, as they transition from ad hoc data cleaning to systematic data governance. Rather than treating data quality as a one-time fix, organizations are developing structured processes to ensure consistency and reliability across all their systems.

Data governance is now considered a valuable business process, not just an IT chore. By integrating data management into their overall strategies, firms can make better decisions and gain more meaningful insights from their data.

How AI cleans up data and the challenges it faces

Overrelying on AI can be dangerous. For Giardina, “the worrisome automated data conversions are those that go beyond standardization into guesswork.” 

For example, some shorthand could easily be misinterpreted. “International Business Machines, Inc.” or “I.B.M.,” for example, would usually be converted into “IBM,” but if the conversion were automated and “I.B.” were accidentally converted into “IBM,” it could cause significant problems for both companies.

Missing and inaccurate data are two of the most common problems, and solely relying on AI to fill in the gaps according to context can easily backfire. As Giardina points out, “when the effects are in any way significant, we need a human to approve each guess.” 

Balancing automation with human insight

Messy data highlights deep flaws in the way organizations handle information. To move forward and improve decision-making, businesses must stop viewing data as a purely technical issue and move towards governance models that combine human expertise, ethical awareness, and a long-term strategic vision. 

Cleaner data creates more effective AI, which in turn helps improve data quality; this mutually-reinforcing cycle is promising, but serves as a reminder that automation alone will not solve our messy data problem. This potential can only be realized by pairing algorithmic precision with human judgement and a consciousness of the biases it can introduce, ensuring transparency and more trust in the systems we build.

Alex Sandoval, CEO of manufacturing intelligence AI firm, Allie AI, also stressed how generative AI copilots do not run on algorithms alone, rather relying on human fluency in the factory’s logic. 

“Today’s most successful deployments aren’t just about feeding models with vast programmable logic controllers (PLC) data, operator notes and compliance protocols. They depend on a new kind of frontline worker: one who can translate between machine behavior and digital intuition,” he concluded.