Data Quality: Why it Can Keep Airlines and Carriers Grounded

As a data company deeply immersed in the industry, we've seen firsthand how long-standing digital challenges like siloed communication, delayed updates, and data "black holes" hindering real-time tracking are always caused by one thing.

Data quality.

Improving data quality is the key to streamlining communication, enhancing real-time tracking, and boosting profitability for airlines and carriers. Joel Sellam recently shared his thoughts on data quality, referring to it as, "A thorn to any long-term relationship with suppliers… are the inevitable discrepancies around overcharges or shipping terms. It's no one's fault—unruly, unstructured data is the culprit." 

At Stargo, we’ve seen how gaining control over this overwhelming flood of data invariably leads to more accuracy, faster response times, consumer trust and revenue for airlines and cargo carriers. 

What is Data Quality in Air Cargo?

In air cargo, data quality refers to the accuracy, completeness, and timeliness of the information carriers collect and store.

High-quality air cargo data is essential for:

• Efficient route planning

• Accurate demand forecasting

• Effective resource allocation

• Reliable automated systems

The five qualities of high-quality air cargo data:

1. Accuracy: Ensures data reflects the correct shipment details, such as cargo weight and pricing.

2. Timeliness: Data must be up-to-date to support real-time pricing, tracking and decision-making.

3. Completeness: All relevant information, like documentation and customs details, must be fully captured.

4. Consistency: Data should be uniform across platforms to prevent discrepancies in cargo operations.

5. Relevance: Information must directly contribute to operational tasks like route optimization or pricing adjustments.

For airlines and carriers, high-quality data ensures accurate route planning, customs compliance, pricing, and load optimization. But what can poor data quality cost you?

What is Low Data Quality in Air Cargo?

Low-quality data is a major issue in air cargo, costing businesses up to 20% of their annual revenue. That’s due to the reliance on manual processing of unstructured data, such as multilingual free-text emails, PDFs, and Excel sheets. 

Over 80% of all incoming data is unstructured, often overwhelming cargo teams, especially during market disruption. This leads to incorrect shipment or pricing details, leading to cargo delays, extra fees, and dissatisfied customers. 

Why is Data Quality Important in Air Cargo? 

Data quality has always been important in cargo operations, but AI's rise has become critical. As Saurabh Abhyankar, Chief Product Officer at MicroStrategy, states, “The reason you need clean data is because GenAI has become so common that it's everywhere. That is why it has become supremely important."

Because AI’s effectiveness relies entirely on the data quality it's trained on, conversations involving AI/GenAI invariably lead back to data quality. 

Why Data Quality Matters for GenAI

Generative AI models require high-quality data to produce valid insights. Unlike traditional analytics tools that may rely on individual data points, GenAI aggregates data to form its responses. As David Menninger, an analyst at ISG’s Ventana Research, points out:

“Data quality affects all types of analytics, but now, as we're deploying more and more generative AI if you're not paying attention to data quality, you run the risks of toxicity and bias. You've got to curate your data before training the models, and you have to do some postprocessing to ensure the quality of the results.”

Biased or inaccurate data can lead to misleading AI outputs, resulting in poor decisions that can impact business operations. With AI being deployed at unprecedented speeds and scales, the risks of poor data quality are multiplied.

Key Challenges to Data Quality for AIrlines and Carriers

Data quality is the foundation of efficient air cargo operations. Accurate information—whether from flight logs, maintenance records, or cargo manifests—is essential for avoiding delays, optimizing fuel usage, and ensuring compliance. 

However, inconsistent data formats and errors can create significant challenges that affect the performance of AI models and overall operational efficiency.

Data Accuracy and Consistency 

Saurabh Abhyankar, Chief Product Officer at MicroStrategy, highlights the importance of clean data: “The reason you need clean data is because GenAI has become so common that it's everywhere. That is why it has become important.”

AI models used by air cargo carriers and airlines rely on large volumes of accurate, consistent data to function effectively. Discrepancies in cargo manifests or flight schedules can disrupt flight planning, hinder cargo optimization, and lead to costly delays or compliance breaches. Ultimately, poor-quality data undermines the gains of AI-driven decision-making.

Bias and Ethical Concerns 

Poor data quality can lead to bias in AI outputs, which may result in discriminatory or unethical decisions. Toxicity and bias in AI outputs can damage a company’s reputation and lead to legal ramifications. Businesses must implement stringent data governance policies to mitigate these risks.

Data Security and Privacy

Security and privacy are two more pillars of data quality because AI systems handle sensitive information. Ensure your AI is trained on secure and compliant data sets to ensure the models meet regulatory requirements, such as GDPR, while delivering accurate outputs.

StarDox Transforms Data Quality for Airlines and Cargo Carriers

We developed StarDox to address the specific data quality challenges airlines and cargo carriers face. Our solution automates unstructured data processing into structured, reliable data formats, such as multilingual emails, PDFs, and Excel sheets. 

With StarDox, airlines can:

• Improve Accuracy: Automate data capture and validation, reducing errors in cargo manifests, flight logs, and pricing details

• Enhance Real-Time Tracking: Ensure timely and consistent data for accurate route planning and demand forecasting

• Streamline Compliance: Maintain data privacy and security in line with regulatory standards like GDPR

• Optimize AI Insights: Provide high-quality data to AI models for better decision-making in load optimization, fuel management, and customs compliance

Contact us for a demo to see how StarDox can transform your data management.