Thomas Redman
As financial scandal, driven by Enron and Worldcomm, unfolded in the early 2000s, I decided to explore the applicability of my area, data quality, to financial reporting.
From my work as a consultant, I was well aware that the financial services industry, like many others, was bedeviled by poor operational data. I wondered if data quality applied to larger issues as well. My first step was to make sure I understood an income statement, balance sheet and cash flow. I purchased some books and spent an hour every morning reading. But after a month, I was more confused than ever. I was sorely embarrassed. After all, I am a Ph.D. statistician and have spent half my life working with financial services firms. How could I not understand something as simple as a balance sheet? The first person to whom I admitted my lack of understanding was a Wall Street veteran who responded, "Don't worry, Tom. Eighty percent of Main Street investors don't understand them either." I talked to many others, but matters only grew bleaker. A pitiful few, at best, understood financial statements. Worse, many statements contained such serious errors that they required restatement. I find the whole situation paradoxical: Despite the critical needs of investors, business leaders, regulators, and the markets themselves for trusted data, the financial community is stunningly tolerant of poor data quality.
In publishing my book, Data Driven, I hope to expose the paradox. As Data Driven points out, when they are of high-quality and "put to work," data are assets on par with capital and people. Bad data, in contrast, are liabilities.
To be sure, bad data come in many forms. Sometimes the data are simply opaque, as with financial statements. Sometimes the "facts" just aren't so. A recent example is the news report on Sept. 8 of United Airlines' (UAUA Quote - Cramer on UAUA - Stock Picks) bankruptcy. It sent United's stock reeling. Trouble is, the report was 6 years old. A third category of data quality problem is that the data one really need are simply unavailable. Who really knew, for example, what was in those soon-to-be-toxic CDOs (collateralized debt obligations)?
One possible explanation for the paradox is that some people make money when data are bad. True enough. A billion dollars changed hands in the United incident. More generally, the best way to make money in the market is to create and exploit an "information asymmetry." It's quite simple really. You discover something that no one else knows about the true value of a product and trade based on that knowledge. Having data the other guy doesn't, having correct data when the other guy's are incorrect and having deeper insights into what the data mean all qualify.
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