Lessons Learned: The evolution of financial data quality
(History doesn’t have to repeat itself)
As CFO of Measurabl, I have spent my career working with financial data. The first half was spent in public accounting, as an auditor with a Big 4 accounting firm, verifying financial data. The second half I’ve spent working within corporations, preparing and reporting financial data. At the outset of my career, I was taught to believe that the role of the auditor was crucial in providing confidence to the market. This belief stemmed from the evolution of financial data quality, which went something like this...
A hundred years ago, industry standards were first set for how financial data should be organized for financial reporting. There was no enforcement backing the standards, so they had little effect. The U.S. stock market crash of 1929 exposed massive accounting errors and frauds. The distrust that developed in the market prompted the creation of the SEC, and the requirement for financial statements to be independently audited by public accountants. Financial statement audits signified that the financial data was high-quality, and could be relied on by the market in investment decision-making.
The crash of ‘29 taught us many lessons for preparing and reporting financial information; but it didn’t teach us how to prepare for what was to come in the new millenia...non-financial data. Non-financial data captures a wide range of areas from the environmental impact of a company’s operations to the composition of its workforce. To an investor, this data is indicative of a company’s ability to provide long-term value creation. The investment community has validated the value of this data, and now we are challenged to provide them confidence that the data is 1) comparable, without consistent industry standards and 2) reliable, without clear regulatory oversight or auditing requirements.
"The crash of ‘29 taught us many lessons for preparing and reporting financial information; but it didn’t teach us how to prepare for what was to come in the new millenia...non-financial data."
The obstacles are clear, but the vision I foresee differs from the path that financial data took. Let’s find comparability through a mutually agreed upon standard that reflects the input of the industry participants. Let’s prove reliability through the use of technology and data science that can ensure high-quality, reliable data.
"The obstacles are clear, but the vision I foresee differs from the path that financial data took."
Let’s work together to find innovative, effective solutions to these obstacles before regulation is imposed.
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