The Essential Role of Clean Data in Unleashing the Power of AI

In today’s fast paced, data-centric world, it’s not uncommon to prioritize immediate growth over building slower-moving, foundational elements. But, when it comes to embracing artificial intelligence (AI), the relentless pursuit of achieving key performance indicators can set organizations up for short-term gains, but sometimes, long-term losses. As we delve into this new AI era, the results of years of forgoing building clean, consolidated data sets due to budget constraints and resource limitations may begin to catch up with businesses.

Large language models (LLMs) are only as successful as their data is clean. Centralized data observability platform Telmai recently launched an experiment to further understand this. The results demonstrated that as the noise level in a dataset increases, there is a gradual decrease in precision and accuracy, proving the impact of data quality on model performance. The experiment resulted in a drop from 89% to 72% in the quality of predictions with the noise in the training data. LLMs require much smaller training datasets to achieve certain quality when using high-quality data for fine-tuning. This results in reduced costs and time for development.

The Power of Pristine Data

As more businesses integrate AI, the significance of data hygiene becomes even more pronounced. Central to AI applications, LLMs use large data sets to understand, summarize and generate new content, increasing the value and impact of the data.

Organizations face potential risks when proceeding with AI applications despite forgoing a steady data foundation. While these applications offer more users access to data-driven insights – and more opportunities to take action on this data – plowing ahead on shaky data quality can lead to inaccurate outcomes. It’s best to equip users with a robust analysis conducted on a firm, clean, data foundation.

Organizations can ensure their data is clean by establishing a single source of truth versus several tables with similar data and slight discrepancies. When there’s disagreement on the source of truth, there are likely valid assertions that some aspects of each source are more reliable than others. This could be a matter of applied business rules as well as data quality management.

LLMs afford more flexibility in understanding and interpreting user questions, compared to needing to know exact field names or values in traditional querying and programming. However, these technologies are finding the best matches between imprecise user questions to available data and analyses. When you have a crisp, clean data foundation to map to, the technology is more likely to identify and present helpful analysis. Spotty, unreliable data dilutes insights and increases the probability of inaccurate or weak conclusions. When outliers occur in the data, they can come from true changes in performance or poor data quality. If you trust your data, you don’t need to spend as much time investigating potential data inaccuracies; you can dive straight into action with confidence when you trust that the insights accurately represent realities in business.

Cleaner Data, Smarter Decisions

When an organization collects any data, it should define its intended purpose and enforce data quality standards throughout its retention and analysis. Still, it’s worthwhile to clean or repair your data to enhance downstream analysis when data quality issues are identified.

Data cleaning is one of the most important steps to ensure data is primed for analysis. The process involves eliminating irrelevant data; this can include removing duplicate observations, fixing formatting  errors, modifying incorrect data and handling missing data. Data cleaning is not solely erasing data, but finding ways to maximize its accuracy.

The first step in creating cleaner data is to determine the use case. Different organizations have different needs and goals. Some teams may be interested in predicting trends, while others may be focused on sustained growth and identifying anomalies. Once the use case is determined, data teams can begin assessing the kind of data needed to perform the analysis and fix structural errors and duplicates to create a consistent data set.

Data priority matrices can help prioritize which errors to address first and the level of difficulty. Each data issue can be rated on a scale of one to five, with one being the least severe and five being the most. Fixing easy-to-change errors first can make a notable difference without spending a lot of time or resources. It’s also helpful to define “good enough” and not expend too many resources pursuing perfection with diminishing returns. Sometimes, a model can be about as robust with 98% data completeness vs 99.99% completeness. It’s good to weigh the effort holistically among data engineers, data scientists and business users on whether the effort is best spent on the last stretch of data versus moving on to another dataset or feature.

It’s important to keep in mind the consequences of acting on incorrect or incomplete data in each field. Some attributes may be a key detail for the use case, such as the channel through which a customer is engaging. Other attributes may be valid, but relatively insignificant indicators like the version of a web browser through which a customer is engaging.