Digital Financial Reporting

(Albert Einstein was famous for the thought experiments he used to explain complex situations using simple stories. The purpose of this thought experiment is to help you better understand how XBRL instances actually work. The first thing the experiment shows you is the important pieces impacting the usability of XBRL instances. The second important aspect the thought experiment shows is that the way business users implement XBRL determines the results received from that XBRL-based information.)

In chapter 18 of my book XBRL for Dummies (which is now shipping!) I use a thought experiment to help you see what it takes to make XBRL achieve what many business users tend to want from XBRL which is to reuse the information within some automated process.  Now, if you don't care about information reuse, then you may not care about these sorts of things.  But if you do, what does it really take to achieve reuse of information?

Let's look at financial reporting as an example.

Again, the ultimate goal you're trying to achieve with XBRL is to automate some process. You many times reap significant benefits from realizing this goal. I am NOT saying that all business processes are automatable or that all processes should be automated. What processes to automate and where to include humans is up to those creating business processes.

But, errors, inconsistencies, ambiguities, and other such factors sometimes keep processes that can and should be automated from being automated, requiring human intervention to execute the process. This is not because you want to involve humans; it's because you haveto involve humans due to errors, inconsistencies, ambiguities, and other factors. The humans need to resolve the errors, inconsistencies, ambiguities, and other factors which prevent the automation of the process.  Again, this is should you choose to automate some process.

Think of the Web. Imagine that every company in the world created quarterly and annual financial information and put that information in an XBRL instance on its Web site. Forget about whether you could get every company to make this information available or even if they should make it available. (It's a thought experiment, so just play along.) Imagine that you wanted to analyze all that information for some purpose.

Here are the challenges you would face:

• Finding the XBRL instances: You need to find those XBRL instances. How do you do that? You have two possibilities: push and pull.  Push means that in some way, perhaps via an RSS feed that pushes this information to you, you're made aware of each of the XBRL instances. Pull, for example, is when you discover the XBRL instances via a search engine and then pull the information to where you can use it.  But somehow you need to discover the complete set of XBRL instances you need for your analysis. For our experiment, say that a search engine finds all the right XBRL instances, and only the right XBRL instances, and makes them available to you. So, you have all the information.
• Having comparable concepts and relations: You have all the XBRL instances, delivered somehow by your search engine. If each XBRL instance uses a different XBRL taxonomy, comparisons between XBRL instances are more challenging. You can still compare information by mapping each company's XBRL taxonomy to an XBRL taxonomy you create as a master comparison taxonomy. You'd have to create this mapping for every XBRL instance in this case.  An alternative is that every company agrees to use the same XBRL taxonomy. Say they did that; in fact, say every company used the IFRS XBRL taxonomy for financial reporting. But now say that companies are allowed to extend the base IFRS XBRL taxonomy. Thus, in effect, now each company is using a unique XBRL taxonomy, and you're back to having different concepts and relations again. However, for this experiment, say that extension isn't allowed, so you have perfect comparability.
• Putting all the instances together: You have a complete set of XBRL instances, and you have only one XBRL taxonomy with no extensions allowed, so you have perfect comparability at the XBRL taxonomy level. You now put all the XBRL instances together into one massive, combined XBRL instance containing all information for all companies in the world. Easy enough: After all, XBRL's purpose is to achieve this sort of result.
• Resolving entity conflicts: You pull all the XBRL information together into one big XBRL instance. Do you have conflicting contexts? Theoretically, no. Each company reports only its information, not the information of others. Each context should be of the reporting company; therefore, each company provides its own entity identifier within its context. Again, say that every company had one and only one unique identifier, and that every company can be effectively uniquely identified and identified only once (meaning no duplication). So, we can have no entity conflicts.
• Dealing with period conflicts: What if companies had different fiscal year-ends? We all use the same calendar, right? That is because the world standardized on one calendar for the most part. (It was not always that way, however.)  Well, in the real world run into the situation where different companies use different fiscal year-ends, not ending on the same calendar date. A fiscal year is some financial period (say July 1 through June 30); it may not be a calendar year. Further, many retailers use a 52/53 week year end.  Let's ignore this.  For this thought experiment, say that every company has the exact same fiscal year-end, which is December 31.
• Handling unit conflicts: Different countries use different currencies; therefore all this information is reported in all sorts of different units, from U.S. Dollars, to UK Pounds, the Euro, Japanese Yen, or Chinese Renminbi, or something else. But say that you can convert to some standard currency - say, the Euro for this comparison. For the sake of this experiment, assume that this conversion was done in real time and accurately. As such, you have no unit conflicts.
• Agreeing on standard metadata: For this thought experiment, say that you've standardized industry sector identifiers (identifying companies as being a bank, an airline, in retail, and so on), geographic areas (used to differentiate operations in Europe, Asia, and so on), standardized entity information for identifying parent companies as opposed to a subsidiary, and any other thing that can cause a comparability issue. Your meta data is perfect. As such, you can identify parent companies and which industry sector a company is in, you can differentiate between budgeted and actual information with XBRL instances, and so on. You use XBRL Dimensions to construct this dimensional information, which is totally standardized across all companies reporting information. (Remember, it's a thought experiment. Einstein pretended he was riding on a light beam, for Pete's sake!) So, all this meta data is standardized enabling comparability.
• Coming up with an analysis interface: You have a huge set of information, all in XBRL, for all the companies in the world. All the companies are uniquely identified. All the companies use exactly the same XBRL taxonomy to report their information, and no extensions are allowed. Everyone uses the same fiscal period for reporting their information. All the numeric values use one standard currency. All parent companies are clearly identified, and all actual information is differentiated from any budgeted information. Basically, everything is perfect: You've achieved information comparability nirvana. You have an easy-to-use business-user interface that's even better than the popular Apple iPhone in terms of usability. It's the perfect business-user application!  Isn't life grand!!!

So what is your point, you ask?

One point is that reality can be messy. Reality isn't perfect. All the issues pointed out in the list do exist.  These issues are real issues which XBRL has to deal with.

Another point is that many things are possible technically but maybe not politically. Technically, you can do everything we mention as we walk through the issues to resolve each issue in some way with XBRL. In fact, that is typically quite easy. The harder part is actually doing it - for example, agreeing on a standard format like XBRL, standardizing how entities are uniquely identified, standardizing on industry sectors and geographic areas, and so on.

Some agreements are already being reached in the area of financial reporting. XBRL itself is a step in that direction. IFRS is another step. But financial reporting is only one business domain. There are many, many other business domains.  Some are easier to create comparability within than others.  Some desire comparability, others do not.  The desire for compatibly is the responsibility of the domain.  Technically, XBRL can deliver comparability, should some business domain desire it and work to create it.

This experiment points out the major moving parts of working with XBRL instances. I use financial reporting only as an example in our thought experiment. Each different business domain will decide how to employ the technology of XBRL within their unique domain.