With data powering just about every modern business, the saying “garbage in, garbage out” is more relevant today than it’s ever been. Any data-based application, whether it’s a simple analytics engine or an advanced AI model, is only as effective as the data it’s fed. For any organization to become truly data-driven, they must weed out the bad data.
The Impact of Bad Data
Using bad data for analytics, AI, and other apps can have catastrophic consequences for any organization. The worst-case scenario is making poor business decisions with that data – whether it’s investments, product changes, or hiring moves. Ignoring and not removing bad data results in misleading insights and misguided choices. It’s like blindly following a GPS without verifying its accuracy or knowing its end goal. You could potentially drive yourself into the ocean.
It also has a broader chilling effect on a company. When bad data leads to skewed or inaccurate insights, employees lose trust in the data and systems more broadly. As a result, they stop relying on the data to make decisions altogether and instead devolve to making decisions based on gut feeling.
At a bare minimum, bad data should be weeded out as often as you use it to make decisions. Ideally, though, it should happen upon the ingestion of the data. Constantly removing bad data as soon as it enters the system is the only way to reliably avoid polluting the clean data source. While some may choose to ingest all data and then clean it later, having a clean source from the beginning is recommended to maintain data integrity.
Using LLMs to Identify and Remove Bad Data
Bad data comes in several different forms. Broadly speaking, there are four main categories:
- Malformed data, i.e., different date formats
- Missing data, i.e., incomplete records or null values
- Anomalous data, i.e., outliers or erroneous entries
- Inappropriate data (data that’s not useful for the analysis at hand), i.e., data that can’t be joined or doesn’t answer the business question being asked
Bad data in any form can be methodically pruned out by using classic programming approaches, data prep scripts and tools, or by using machine learning to detect anomalies. Tools like dbt can also be used to run repeatable transformations on data to take it from raw to cleansed and ready for analysis.
But these are slow processes – and organizations need insights quickly.
Luckily, large language models (LLMs) now offer data-cleaning capabilities that blow these traditional techniques out of the water. LLMs can assist with data prep and augmentation – they can understand the data, automate the cleaning of the data, and even determine what analysis is possible with the data. Now, users can rely on LLMs to do the work of assessing disparate data sets, figuring out how they relate to each other, and joining them together for analysis.
Using LLMs is effective because it removes the drudgery of traditional data prep techniques. Instead of doing manual exploration on each column and writing a transform, LLMs can understand the data schema and statistics and can be used to form an action plan to clean the data for analysis.
Within a year or two, every tool in the data management space will incorporate some form of LLM-based automation. It just makes no sense to ask users to do these cumbersome tasks manually when they essentially can instead leverage an AI data prep assistant to do it for them.
Use Data Wisely or Get Left Behind
Data is becoming increasingly important for decision-making, with models now able to evaluate an exponential number of hypotheses. Today, models can help us explore all the nooks and crannies of any scenario, combine heterogeneous data sets at high speed, and do it all with relatively low computing costs.
Because of this, enterprises are facing competitive pressure to gather more data – quality data – to get better answers. If you have better-quality data than your competitor, then you can uncover better insights and opportunities, and then act on that information to gain a market advantage.
The scalability and repeatability of models make the harmful impact of bad data all the more foreboding. If you’re feeding bad data into a model that’s going to be used hundreds or thousands of times to make decisions, you may be setting your organization up for a vicious cycle of systemically bad decisions.