What's Next for Big (and Small) Data
There's no bad time to build trust. In financial reporting, leaders, regulators, investors, and the public have to trust that companies and organizations are reporting the information clearly, in a way that allows them to interpret it correctly.
When the Federal Energy Regulatory Commission (FERC) announced its XBRL® mandate for financial data to be submitted in a machine-readable, standardized format, it was a big moment for making data more usable. To understand why, it's probably worth taking a quick jog down memory lane.
How far back should we go?
Humans have been recording and sharing "data" or observations since they made cave paintings more than 3,000 years ago.
Aristotle brought more structure to methods of recording observations, so he could draw conclusions about what he was seeing.
Bringing structure, or standards, to how we report data has paid exponential dividends when it's time to use and analyze that data. It gives us the potential to harness machine learning to pick up patterns in the data faster.
Just as a bike, car, or airplane helps scale the amount of ground you can cover in an hour, artificial intelligence (AI) can help us scale the amount of data we can analyze. We just need some guardrails to direct us where we should and shouldn't go.
Where do we go from here?
So, what will it take to reach the next level of disclosure modernization? I've written a new paper on where we go from here. What we really need, now that we have data standards, is to structure the compliance standards themselves.
At Workiva, we're constantly pushing the envelope. We're intrigued by the possibilities of using machine learning within our connected reporting and compliance platform to help our customers do their jobs better. More to come on that front. Scroll down to subscribe to the blog, so you can stay in the loop.
XBRL® is a trademark of XBRL International, Inc. All rights reserved. The XBRL® standards are open and freely licensed by way of the XBRL International License Agreement.
About the Author
Dean Ritz is a subject matter expert in information modeling with over three decades of experience in various data-dominated domains, including artificial intelligence, expert systems, object-oriented programming, and most recently the modeling of financial information. As a Senior Director at Workiva, he applies his expertise to product strategy for collaborative work management and the management of the company’s expanding patent portfolio. His interests extend to the topics of rhetoric and ethics, with scholarly work in these areas published by Oxford University Press (2011, 2009, 2007), and Routledge (2017).