Poor data = big border risks | SITA
 
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Poor data = big border risks

Published on  24 April by Peter Sutcliffe , Head of Border Management Portfolio, ATS, SITA
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Governments rely on data to perform a risk assessment for potential threats moving across the country’s borders, such as fraud, organized crime and international terrorism. Given that this allows them to make an informed decision on whether or not a person should be allowed into or to leave the country, the quality of data is of critical importance.

Data quality has several different dimensions. These include completeness, time, format and meaning. Addressing all four allows governments to get the best quality data for their risk assessment.

High Government confidence in the quality of their risk assessment to eliminate the majority of travellers as a concern, allows swifter processing of travellers across the border.

Completeness

The first dimension determines whether the data is complete - meaning that all the required data has been provided for all crew and passengers. If it hasn’t, the analysis is delayed or incomplete, leading to poor confidence in the risk posed.

Time

The second dimension is time. Does the government have adequate time for analysis to detect previously unknown risks posed by travelers, and to check passengers against their government data?

Carriers can provide updates to data after initial submissions have been made, as updated data improves in accuracy. Important updates include correcting mistakes made with self-entry at the time of booking. In the future, more automation and improved identity technology means that passengers will provide better information and at multiple stages of the travel process, which may lead to improved data quality.

Format

The third dimension refers to syntax. Is the data correctly structured? Is data in the right fields in the right format? Are all mandatory fields present? Importantly, correct syntax means that data in the mandatory fields conforms to what’s expected for that field.

Meaning

The fourth dimension refers to semantic data quality which is the meaning of the data. Passport numbers, for example, follow very specific guidelines for each country, so the validity of any passport number can be checked by matching the number against the issue date and the country’s own guidelines. Wider spread automation and improved identity technology will help improve the validity and correctness of supplied data.

Helping to build a better understanding

Once a government receives the data it can carry out its analysis. This involves parsing the data and applying rules on the four dimensions to identify any errors. Governments can improve their risk analysis by correlating different types of data. This helps build a better understanding of the person under review.

The increasing use of electronic travel documents with embedded quality identity data allows the capture of passenger data to be automated, thereby improving data quality. Combined with biometric identity verification, this provides greater confidence that the holder of the passport is the person matching the verified data, while helping detect issues such as fraudulent documents.

Steps to improving data quality

The first step to improving data quality is recognizing that there’s a problem and identifying the scale of the problem. The best way to enhance the data is to ensure it’s entered correctly at source. Some data quality issues, particularly around manual data entry, can be addressed by appropriate training to help understand the implications of entering poor data.

Once data quality problems are identified and quantified, governments need to focus on two things:

  1. The highest priority errors: those likely to impact risk assessment most severely.
  2. The trends of errors over time: which errors are increasing or decreasing.

This allows governments to focus on working with carriers to resolve the highest priority and most common errors, and ensure that any negative trends are reversed. These improvements have the greatest impact on border security.

The only way forward: collaboration and understanding

A collaborative effort between governments and carriers is at the heart of improving data quality. There needs to be an understanding of the different motivations of each stakeholder. Carriers collect the data to get passengers from A to B, whereas the government uses the data to identify potential security threats.

Data quality has great benefits for all stakeholders involved in the process. For vastly enhanced border security, it’s imperative that air travel’s stakeholders work together to improve it.

To understand more about data quality and what you can do to mitigate against it download our white paper.


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