BLOG: Digital Financial Reporting
This is a blog for information relating to digital financial reporting. This blog is basically my "lab notebook" for experimenting and learning about XBRL-based digital financial reporting. This is my brain storming platform. This is where I think out loud (i.e. publicly) about digital financial reporting. This information is for innovators and early adopters who are ushering in a new era of accounting, reporting, auditing, and analysis in a digital environment.
Much of the information contained in this blog is synthasized, summarized, condensed, better organized and articulated in my book XBRL for Dummies and in the chapters of Intelligent XBRL-based Digital Financial Reporting. If you have any questions, feel free to contact me.
Entries from October 29, 2017 - November 4, 2017
WIRED: Data is the New Oil of the Digital Economy
In his WIRED article Data is the New Oil of the Digital Economy, Joris Toonders points out the value of data in today's digital age for those that learn to extract and harvest that data. But there is one thing that is even more valuable than data. Metadata.
Fortune has a similar article, Data is the New Oil, by Jonathan Vanian. The tag line of that article is, "Artificial intelligence is only as good as the data it crunches."
While it is true that data is important; metadata is even more important. The key ingredient in a knowledge based system is domain knowledge. Metadata organizes domain knowledge.
What is not in dispute is the need for a "thick metadata layer" and the benefits of that metadata in terms of getting a computer to be able to perform useful and meaningful work. This is simply science.
But what is sometimes disputed, it seems, is how to most effectively and efficiently aquire that thick metadata layer. There are two basic approaches to getting this metadata layer:
- Have the computer figure out what the metadata is: This approach uses artificial intelligence, machine learning, and other high-tech approaches to detecting patterns and figuring out the metadata.
- Tell the computer what the metadata is: This approach leverages business domain experts and knowledge engineers to piece together the metadata so that the metadata becomes available.
And this is not an "either/or" question. Both automated and manual knowledge acquisition methods can be used combined into a hybrid approach to aquiring knowledge. The question is how to best combined the two approaches to most effectively and efficiently get the important metadata you need.
Because knowledge acquisition can be slow and tedious, much of the future of artificial intelligence and expert systems depends on breaking the knowledge acquisition bottleneck and in codifying and representing a large knowledge infrastructure using automation. But, domain professionals are still going to need to participate. And to participate, they need to understand knowledge and knowledge science.
Charlie
in Digital Financial Reporting
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