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Business and Tax Identifiers: Empowering Interoperability and Compliance

Product Manager

Accurate and trustworthy data on business partners is essential for meeting compliance requirements, achieving correct and reliable reporting, improving customer relationships as well as implementing efficient processes. A major role in business partner data management play identifiers. 

Imagine identifiers as the puzzle pieces that connect different parts of a company's digital existence. These pieces include things like tax identifiers, business register identifiers, proprietary identifiers such as D-U-N-S, and other third-party identifiers like the Legal Entity Identifier, that help identify and link various aspects of a business.

Think of these identifiers as the bridges between different digital representations of a company

For instance, they connect a company's official records in government databases (e.g. business register), its tax information (e.g. VAT registration), its presence in commercial databases, and even its listing in specialized directories like the global Peppol directory. All these pieces together create a complete picture of the company's digital identity.

Unfortunately, these pieces are often telling us contradictious information

In this article, I'm going to explore how checking these identifiers plays a crucial role in making sure that the information we rely on is accurate and dependable. We'll delve into various methods of verification to ensure these identifiers are correct and match up properly.

The multiple identifier checks outlined below collectively contribute to data integrity, consistency, and compliance. By implementing such checks, organizations can ensure that their data remains accurate and make a major step towards enabling interoperability with business networks and ecosystems.

This process isn't just about accuracy; it's also about ensuring interoperability so that different systems can communicate effectively and business ecosystems can flourish.

Types of Identifier Checks

Let's now take a closer look at the different types of identifier checks and their significance for which we at CDQ have developed more than 2'000 data quality rules that can be used out of the box.

Identifier Format: Upholding Syntax and Accuracy

Correct syntax and formatting are essential elements in maintaining data accuracy. Identifier format checks ensure that identifiers adhere to specific rules and structures, minimizing the chances of errors caused by improper formatting. This process involves "cleaning" identifiers by removing extraneous characters and assessing whether the number of digits, as well as allowed characters, align with the established standards.

01 Examples of format check.png
Examples of format checks

Checkdigit Validation: Ensuring Consistency and Accuracy

Checkdigit validation stands as a foundational pillar in the realm of identifier checks. It involves the verification of identifiers using checksums or checkdigits, which are mathematical algorithms designed to detect errors in the identifier's input. By applying this technique, organizations can ensure that identifiers remain consistent and accurate, reducing the likelihood of errors that could lead to incorrect or invalid data.

02 Examples of checkdigit checks.png
Examples of checkdigit checks

Identifier Consistency: Establishing Data Coherence

Identifiers are often interconnected, forming a web of relationships that establish data coherence. An exemplary instance is the French SIREN number, an integral component of the French VAT number. Through identifier consistency checks, these relationships are verified, ensuring that the values align harmoniously and accurately within the dataset. This verification process bolsters the reliability of the data and prevents inconsistencies that might arise due to discrepancies between interdependent identifiers.

03 Examples of consistency checks.png
Examples of consistency checks

Identifier Existence: Validating Real-World Relevance

The validity and relevance of identifiers are paramount. Identifier existence checks involve leveraging external data sources to confirm the actual existence of identifiers, such as EU VAT IDs or ABN numbers. By interfacing with trusted data repositories like VIES or national business registers, organizations can validate the legitimacy of identifiers, reducing the risk of relying on fictitious or obsolete information.

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Examples of existence checks

Identifier Qualification: Verifying Legitimacy

The authenticity of an identifier becomes more robust when it is cross-referenced with associated company names and addresses. Identifier qualification checks involve confirming that the provided identifier corresponds to accurate and legitimate entity details. This verification enhances trust by ensuring that the identifier is genuinely associated with the entity it claims to represent.

05 Examples of congruency checks (qualification).png
Examples of congruency checks (qualification)

Identifier Schema: Ensuring Proper Formatting

Beyond mere formatting, identifier schema checks delve into the intricate details of separators, such as dots and hyphens. These checks validate that identifiers follow specified reference schemes precisely, emphasizing the correct placement and usage of separators. For instance, a Swiss UID might need to adhere to a specific format, exemplifying the importance of schema validation in maintaining accurate and standardized data.

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Examples of reference schema checks

Identifier Missing

Imagine a company missing a required identifier in its official records. Identifier missing checks highlight such omissions, ensuring that vital information is complete and compliant.

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Examples of missing identifier checks

Identifier Deprecated

Think of an outdated version of an identifier format used in the past. Identifier deprecated checks identify these old formats, helping prevent reliance on obsolete or invalid data.

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Examples of deprecated identifiers

Further readings from Simon:


***This article was originally published on LinkedIn. Follow Simon for more insights on data quality!***


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