Scrubbing numbers is an important and expensive activity, necessary to the work of an investment banker.
First, there is the fee banks pay to the market data vendors for the information (and often it’s more than one vendor). Then, there is the time cost of junior bankers manually reviewing the data, checking numbers, and in some instances overriding values and updating calculation definitions. It’s time-consuming work and, due to lack of institutionalized sharing, is often repeated multiple times for the exact same data for the exact same company. For junior bankers, it’s one of the most annoying parts their day (something to remember when considering the junior banker problem).
If you ask why scrubbing is done this way, usually, you’ll receive one of two reasons (or both!):
“It standardizes the data”
In theory, if the values are reviewed by a junior banker who has sat through all the analyst training and survived the rigors of the hiring process, then the senior banker can be confident in the accuracy of the numbers. In reality, numbers are probably being scrubbed at 3 a.m. after a full day in the office. Sleep deprivation doesn’t lead to accuracy very often.
“It’s required to learn the ropes”
By scrubbing numbers, junior bankers get to know their way around financial statements and better know their clients. I have pragmatic concerns with this. Bankers are smart; they don’t need to do things over and over again to master something. I think this rationale doesn’t give junior bankers a lot of credit, and instead unnecessarily adds to the grueling work schedule they already have.
There is a section of the Pellucid platform called the Data Hub that makes it easier to scrub numbers accurately without robbing bankers of the hands-on experience. We built it robust enough to handle masses of global market data, yet versatile enough to offer the bespoke customizations needed by bankers. Knowing that numbers that appear in pitchbooks are often highly scrutinized, we worked closely with a handful of bulge brackets in the development of the Data Hub to see where data discrepancies could occur.
First, this is how data providers gather numbers for their database:
Data providers are very rigorous with the data collection so really there are only three potential areas for concern:
1. Data taxonomy
There can be vast differences in how companies report their financial statements. As such market data vendors have to standardize financial statements to allow for apples to apples comparisons. If a banker has a definition that veers from this standardized version, though, the calculation has to be manually updated.
2. Data mapping
Standardized financial statements are rarely rich enough to capture all of the data fields bankers need to plot into content. Because of this, many fields are compressed into an encompassing number--which generates disagreement. We found that the further down the P&L you travel, the greater the likelihood for methodological disagreement.
3. Different data
Occasionally there are times when a banker wants to use a different number (such as values provided by the client that are not available to a wider audience). In this case, there has to be the ability to replace a number with a different value.
The first two areas account for about 95% of disagreements, which basically boil down to, “I don’t agree with how you’ve treated this.” While the final one just requires a flexibility to edit.
So we built the Data Hub to accommodate all of the above. You still get to scrub numbers and define metrics they way you need to—reviewing the source and how it was calculated, making any changes, overriding values—but everything is stored and easily used over and over. Junior bankers continue to brush up their financial analysis skills but have time to also develop other areas pertinent to their career, and senior bankers can be confident in the data as it comes from their preferred data provider before customized definitions and overrides are applied.
Basically, it’s your data, your way, when you need it, and it’s now available with Pellucid.
Email me for a demo at email@example.com.
Great content begins with flawless data. Scrub numbers once and use over and over with Pellucid. Visit www.pellucid.com.