How much of the in­for­ma­tion re­ported by a filer in its fi­nan­cial report is read­ily avail­able to you, the user, as dis­tinct data fields from your data vendor?

XBRL-First Process Outperforms Document-First Process

XBRL in­stances in­clude, as data fields, every fact re­ported in the face of finan­cial state­ments and foot­notes to the financials. When a data vendor adopts the XBRL-first process, it har­vests every last one of these data points and can normalize the data points to make them comparable and serve them up ready to be used to its users.

On the other hand, traditional ven­dors may only pro­vide selective data that they have de­cided is worth in­vest­ing their hu­man an­a­lysts’ time and their cus­tom developed data­bas­es’ ca­pac­ity. Every fi­nan­cial state­ment line item and foot­note fact rep­re­sents an ad­di­tional ex­pense for a traditional data vendor, be­cause it takes human ef­fort and cus­tom cod­ing to turn that item into a data field in the vendor’s data­base. Thus, these ven­dors may only clas­sify the line items and foot­note facts for which they believe have a large enough market.


Segment Reporting Footnote

Fre­quently, the traditional data ven­dors do not pro­vide all the facts from foot­notes as independently-iden­ti­fied data fields. For example, the segment reporting footnote where the company identifies company segments and each segment's operational results.

Investors and other financial statement users view the segment footnote as very important to their investment decisions. Investors use segment information for a variety of analyses, including understanding business activities, making judgments about the company as a whole, and understanding future growth prospects.
A Roadmap to Segment Reporting June 2020, Deloitte

Since the segment footnotes are XBRL-tagged, XBRL data vendors would be better at delivering the data to the users. In addition to making data available, a purposedly-built chart can also intuitively surface the data for drill-down analysis.

Screenshot from idaciti Research Application with data visualization of segmented revenue by geographical regions, reporting segments and product & service.

Pension Footnote

Another prime ex­am­ple is the notoriously detailed pen­sion foot­notes reported by hundreds of pub­lic compa­nies. One of the ways to measure investment risk is by analyzing a company's pension, which promises to pay a specific (defined) benefit to retired employees.  The maintenance of a pension creates significant liabilities. What is the fair value of the plan's assets? What's the assumed discount rate? What is the target asset allocation of the plan's assets in equities, fixed income, or cash? These are all crucial questions to ask as we analyze a company's financials. With the XBRL-first process, data vendors can help to answer these questions by capturing a comprehensive set of pension data from the footnotes of the financials to help make better-informed investment and risk analyses.

Screenshot from idaciti Research Application with embedded financial data charts. idaciti applications are designed to provide detailed data from pension and all the other footnotes to the financials. After the normalization process, the data points are comparable across companies.

COVID Disclosures, the Unusual yet Important Disclosures

As COVID-19 continues to wreak havoc on the economy, many companies are experiencing lower revenues, resulting in lower future cash flows and longer collections cycles. From the footnotes, we learned the unavoidable restructuring and layoff or furloughs. These detailed data points are also tagged in XBRL and can provide valuable information on how companies deal with workforce management in response to COVID-19.

Screenshot from idaciti Research Application with embedded financial data charts. idaciti applications can provide detailed data from the unusual disclosures critical to making investment decisions in this uncertain time.

Cut out the noise and cut to the chase

When it comes to the need for reliable and accurate data, you must drown out the noise of the endless streams of data available to you and focus on the streamlining of the processes that efficiently and accurately get you the data you need and will best serve your company.

idaciti has released a new solution "XBRL Accelerator" - Next Generation Machine-Learning, Machine-Readable, and API Delivery Financial Data Product. This new API product can streamline trading, empower broader coverage, enable high-speed quantitative analysis, and address quality concerns that come with traditional vendors.

If your business consumes financial data, contact us to learn more about how this sophisticated technology can “accelerate” your business strategy!