How To Improve Data Quality (& How It Can Be Used To Strengthen AI)

Data powers everything from enterprise solutions to AI models. Take ChatGPT as an example — it has been trained on billions of web results, books, and similar data sources. However, data alone doesn't suffice; it must be high-quality data for the models trained on it to be relevant, accurate, and efficient. 

But what determines data quality?

“With respect to training AI models, data quality is based on three key metrics: (1) How well organized the data is, (2) The supporting meta-data that goes along with the primary data set (used to give the AI model context and meaning behind each record), and (3) The variety of the data,” said Martin Pellicore, President of Pell Software.  “If you can ensure the data you're providing for training purposes meets this criteria, your model will have a wide range of data to train on with the proper supporting metadata to properly train and produce higher quality results in a shorter time frame." 

Regardless of the type of product you're creating, it's imperative that the data you use for it meets these requirements. Generative AI, in particular, needs high-quality data as it relies heavily on patterns and correlations to generate new content.

In this guide, we share how to optimize data quality, along with its benefits for AI models.

Understanding Data Quality

Typically, data is considered high quality if it is accurate, consistent, complete, reliable, and timely. If one of these traits is missing, AI models may struggle to determine patterns and correlations, leading to less accurate results.

A typical data quality issue is missing information. Gaps in datasets result in biased insights and incomplete AI training. Think of it this way: you're creating a sales strategy, but your customer database is missing phone numbers and email addresses. These gaps make your whole campaign incomplete.

Another issue is duplicate records. Results may be skewed when there are multiple entries for the same entity. They’re also a waste of storage space.

Data inaccuracies, whether due to outdated information or human input mistakes, also reduce the reliability of AI outputs. Similarly, inconsistent formatting can cause processing errors, which again means incorrect insights.

In short, data quality issues can severely impact decision-making and AI performance. The resulting model is then unable to perform the tasks as well as it is expected to. 

Why Data Quality Matters for AI

AI doesn't think on its own. Instead, it learns from the data it is trained on. So, inaccuracies or issues in the data will ultimately mean reduced effectiveness of the AI model. The model cannot recognize patterns, generate proper responses, make predictions, or provide accurate recommendations.

AI models trained on poor-quality data have biases and inaccuracies in their responses. For example, Amazon's AI recruitment system had gender biases as it was trained on male-dominated resumes. The system automatically penalized resumes with words like "women" or "female," leading to gender discrimination in the hiring process, as qualified female candidates were systematically disadvantaged for using those terms. The example highlights why high-quality, unbiased data is imperative in AI training to prevent unfair and inaccurate outcomes.

Poor-quality data also causes "overfitting," which means that the AI model learns and adapts too closely to the specific data it was trained on. It learns patterns that only apply to its training data, not real-world applications. The model then becomes less flexible and more prone to errors when it handles new data.

Such models also fail to generalize across diverse scenarios. AI will struggle to adapt if the training data is too narrow or skewed. A good example of this comes from facial-recognition systems that are predominantly trained on men and light-skinned faces.

An MIT Media Lab study found that facial analysis software has an error rate of just 0.8% for light-skinned men. However, this error rate is a staggering 34.7% for dark-skinned women. These results indicate that the training data wasn't diverse enough for the resulting model to be applicable in real-world scenarios where people of different genders and skin colors are present.

The stark contrast in accuracy shows how biased training data leads to unfair and unreliable AI systems. Diverse and high-quality data helps build models that work accurately for all users, not just a specific group.

How Data Quality Impacts Different Types of AI Learning

AI learning methods differ in how they process and interpret data, but all rely on high-quality data for accurate outcomes. Supervised learning depends on labeled datasets, unsupervised learning finds patterns in unlabeled data, and reinforcement learning improves through feedback loops. 

When data is incomplete, biased, or inconsistent, it affects each learning type differently, leading to flawed predictions, unreliable insights, or reinforced errors.

Supervised Learning

In supervised learning, data is labeled. The inputs are fed into the model along with their corresponding outputs. The model learns by finding patterns in these examples and applying them to new, unseen data.

AI will ultimately learn the wrong patterns if the labels are incorrect, incomplete, untimely, or inconsistent. Its results will then be wrong or inconsistent.

Let's say you're training a fraud detection AI model on mislabeled transactions. If the model learns from these transactions, it will falsely identify legitimate purchases as fraudulent and vice versa.

Unsupervised Learning

Unlabeled data is used in unsupervised learning, and the model learns by finding patterns on its own. Data quality is crucial in this type of learning, as the algorithm has to be able to find meaningful patterns in the data without any guidance from labels.

Poor-quality data would make it difficult or impossible for the model to identify these patterns. A simple example comes from an e-commerce recommendation system. If the data you provide is inconsistent or has gaps, the model will group unrelated products or fail to make accurate recommendations.

Semi-Supervised Learning

Semi-supervised learning uses both labeled and unlabeled data. While the labeled data helps the model learn patterns and make predictions, the unlabeled data refines those patterns and improves the predictions' accuracy.

“Semi-supervised learning is a great technique to improve the quality of training and performance of generative AI models,” said Pellicore. “For instance, we recently developed an application that requests data from an Azure AI model and, based on certain key confidence thresholds we determined, will also send the results to a human to be reviewed. This approach leverages the "human in the loop" model by ensuring someone manually reviews results if they don't meet a certain criteria, thereby improving overall final results while also continually training and re-training the AI model itself.” 

Modern generative AI models also use this approach. The small set of high-quality labeled data helps refine predictions from a much larger pool of unlabeled data.

How To Improve Data Quality

We've established that high-quality data is a must. But how do you achieve it? There are a few steps you can take to improve data quality: 

1. Data Collection & Acquisition
2. Data Cleaning & Preprocessing
3. Data Validation & Standardization
4. Ongoing Data Monitoring
5. Data Governance

Data Collection & Acquisition

Data quality assurance begins at the source, which means that you need to be on your toes during data collection and acquisition. Collect relevant and diverse data to form a strong foundation for your AI model. Your data should be relevant to the end users and come from multiple sources that represent most (if not all) of them. 

For example, if a healthcare AI system is trained only on patient data from urban hospitals, it may not perform well for rural populations with different healthcare needs. Instead, you should collect data from various demographics, healthcare settings, and conditions to create an AI model that delivers accurate and equitable results.

“Data diversity is extremely important to the performance of generative AI,” explained Pellicore. “The more variety you provide your AI model during training, the more experiences it will have to pull from when you ask it to perform real-world tasks.”

Besides diversity, which means using a wide range of representative examples, covering different demographics, behaviors, environments, and scenarios, structure is also fundamental. The data should be labeled consistently, and the labels should accurately represent the content of each sample. As new information or data becomes available, update your datasets accordingly.

Data Cleaning & Preprocessing

Since raw data is rarely perfect, cleaning and processing are essential. Here, you eliminate redundancies, errors, and inconsistencies.

Start by fixing inconsistencies in spelling, formats, categorical data, numerical values, and data types. Remove irrelevant or duplicated data, which can lead to biased results.

Use imputation techniques like mean, median, or mode to handle missing values. Outliers that may distort your model's performance should also be dealt with carefully.

If there's no way to go about incomplete or inconsistent data, it's best to discard those samples. Duplicate records skew results, so you should remove them, too.

How you do this step (manually or automatically) will depend on how much data there is. "The process of cleaning and normalizing data to eliminate noise often comes down to an SME thoroughly reviewing the data either by hand or using an automated procedure," said Pellicore. "How this takes place depends on the storage of the data itself, as well as the amount of data to be processed."

If you're dealing with large datasets, it can be time-consuming to do this manually. In such cases, automating the process using algorithms and statistical tools is usually the best approach.

Data Validation & Standardization

Without proper validation and standardization results, AI models struggle with formatting, units, and data type discrepancies. You can use automated validation rules, such as duplicate detection and format consistency checks, to catch errors before they impact AI training.

Standardize data formats, such as those for measurement units, dates, and currency. Finally, use a clear data taxonomy (the classification scheme for organizing data into categories) to keep data consistent across different sources.

Ongoing Data Monitoring

For AI models to evolve, the datasets they rely on must also be kept up-to-date. Data drift is the phenomenon where data distribution changes over time, and this can directly impact model performance.

You can combat this by defining key data quality metrics, such as accuracy and timeliness. Review your data periodically to update it according to these metrics. You may also implement automated data monitoring tools that can detect anomalies and send alerts so that you can take corrective actions immediately.

Data Governance

Governance simply means having control over the flow of data. You'll need proper policies, responsibilities, roles, and oversight to maintain this flow. Plus, there should be data access, storage, security, and update guidelines.

Data stewards (people responsible for data quality) must have specific roles and responsibilities in this process. For example, Person A may be in charge of setting up data validation rules and monitoring. Person B then creates a proper data taxonomy for consistency, and so on.

Again, this step doesn't have to be entirely manual. AI-powered solutions can automate data validation to detect inconsistencies and alert data stewards for faster resolution.

Additional reading, 'Top 10 Data Governance Tools & Best Practices.'

Building Reliable AI With Better Data Practices

High-quality data forms the foundation of modern AI models. The better the data is, the more efficient and accurate the resulting models will be. Leverage expert advice and best practices at every step of AI model training to maintain data quality. Don't hesitate to automate lengthy and time-consuming tasks with automated solutions when required. 

 

About the Author

Hannah Hicklen Content Marketing Manager at Clutch

Hannah Hicklen is a content marketing manager who focuses on creating newsworthy content around tech services, such as software and web development, AI, and cybersecurity. With a background in SEO and editorial content, she now specializes in creating multi-channel marketing strategies that drive engagement, build brand authority, and generate high-quality leads. Hannah leverages data-driven insights and industry trends to craft compelling narratives that resonate with technical and non-technical audiences alike. 

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