One of the hurdles for issuers to overcome when tagging or reviewing the XBRL tags (i.e., "elements") of their filings is to step back and: (1) compare the tags in the current filing to tags selected in prior filings; and (2) compare their own tags to that selected by peer companies for similar line items.
Why is this important? XBRL data quality is not just about ensuring that you don't inadvertently use an XBRL element to tag a negative value when that element should only be used for a positive value. It is also important to keep in mind that the data needs to be useful to investors and analysts.
That means the data should be consistent and comparable.
Compliant with DQC is just the Beginning
The XBRL US Data Quality Committee (DQC) Rules are now embedded into the US GAAP Taxonomy published by the FASB. The DQC rules help ensure compliance with XBRL validation logic. These detailed rules help issuers use correct combinations of dimensional elements to tag a fact, avoid tagging negative facts with certain elements, and set up proper calculation rules, amongst others. While these validation rules are of extreme importance for data quality, is merely achieving zero DQC validation errors sufficient for data consistency and comparability?
A Close Look at an Example of Data Inconsistency and Incomparability
Here is how an airline company reported airline related inventory on the balance sheet in their 10-Q filings: Q1, Q2 and Q3 2020.
In Q1 2020, the company reported airline-related inventory as they did in the past eight years, with the standard US GAAP element "AirlineRelatedInventoryNet". The same element has been used by its peer companies as well. In other words, the tagging has been both consistent and in line with other airlines.
In Q2 2020 and Q3 2020, the company changed from using the standard tag to a custom tag that can't be directly compared to any other companies.
So What? What's the Impact of Using a Custom Tag?
Simply put, because this airline switched to a custom element in more recent filings, it will not be comparable to other airlines. At least unless additional "normalization" is applied to make the data comparable.
Therein lies the challenge - companies nevertheless do change the underlying reporting/disclosures over time. Normalization is needed to make complex financial data comparable. The inconsistency and incomparability in the XBRL data tagged by issuers does pose additional challenges. In a perfect world, investors and analysts should be able to use the as-tagged data. The reality is that companies do not always tag nor report consistently with peer companies. Does this render the XBRL data unusable? We don't think so.
What gives XBRL the advantage is the metadata underlying the tagged data. With machine learning and business rules, vendors like idaciti can leverage the metadata to normalize and bridge the raw XBRL data and consumable data gap.
With proper normalization, despite the fact that this airline used a custom tag, vendors like idaciti are still able to provide a consistent time-series for inventory.
We Wholeheartedly Believe that Innovation and Creativity can Help Solve the Data Quality Problem
Our single focus is on finding solutions to the data quality problem. To prevent inconsistent data being submitted to the SEC, we developed the Inline XBRL Viewer to help filers and their service providers quickly and easily "see" and fix the issues in their tagging over time and in comparison to their peers. We also recognize that the raw XBRL data is not easily comparable and useable for investment analyses, which is why we have normalized the data and smoothed out the noise to help analysts and investors gain insights from the data.