Big Value in Big Data? Here's how to signal the market about it
By Josiah Rudge
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Executive Summary
As McKinsey reports, the “Big Data” applications for hospitals in triaging and payment authorizations are likely to make the healthcare industry one of the top early beneficiaries of AI. The Tobin’s Q (market cap relative to asset replacement value) of public US stocks is down over 10% in the last year due to the current high interest rate environment and tougher future economic outlook, thus making any impact on the perceived value of a company all the more important today.
Research published in the Journal of Product Innovation Management suggests a significant positive impact on the Tobin’s Q of S&P 500 companies based on their percentage of workforce dedicated to “Big Data” analytics.`The effect is most pronounced amongst companies with lower stock Beta’s and higher R&D intensity, which uniquely describes the healthcare industry (and hospitals in particular).
Actively tracking your own percentage of workforce “Big Data” scientists, along with that of your competitors, is key - but there may be other similar ways to signal the market as well.
The bottom line: hospitals will have outsized potential impact from “Big Data” use
A McKinsey report suggests that hospitals systems could stand to make large gains financially (in terms of time savings and revenue) from using “Big Data” and AI to better triage patient procedures or better speed and predict collections from insurers via prior-authorizations (“AI ushers in next-gen prior authorization in healthcare”, McKinsey, April 2022).
According to the database maintained by Aswath Damodaran at NYU Stern School of Business, low Beta sectors tend to be in consumer goods (food processors, food wholesalers, grocery stores), utilities (power, general utilities), and healthcare (hospitals, health insurance, healthcare technology). Looking for overlap of these sectors with high R&D intensity sectors from EIB’s “R&D Spending as a Percentage of Revenue By Industry [S&P500]” reveals the Healthcare sector is somewhat unique in having BOTH lower stock Betas and higher R&D intensity. Of course the higher R&D intensity is not an indication of spending on “Big Data” analysis, rather of the health sciences that healthcare systems do as normal part of their medical research.
The overall backdrop is a decrease in market value in general as attributed to rising interest rates and greater risk of future economic contraction since 2022. A high Tobin’s Q (at least over 1) signals that the market is confident a firm is leveraging its current assets to grow and a low Tobin’s Q signals the company is valued at less than the value of its current assets. While Tobin’s Q is not really a “bottom line” measurement, it is prudent to keep it high. Currently (2023 Q1) Tobin’s Q is 1.38 while a year ago it was 1.54 (-10%) in the market in general - any push to signal increased ability to gain financially is more important now than before.
The research: the market signal of hiring “Big Data” scientists is the perfect fit
“Big Data” (particularly as it feeds machine learning) is often assumed to be a must-have for all modern companies. But there is a difference between having data and using data to create value. A recent paper published in the Journal of Product Innovation Management developed and collected unique metrics of volume, variety, and veracity of “Big Data” on 114 S&P 500 companies. They measured these metrics’ correlation with the company’s 2018 Tobin’s Q to see if, or how the market valued these metrics. Tobin’s Q is a ratio of the market value of the company to the replacement value of its assets - a Tobin’s Q of greater than 1 signals the market expects future growth or perceives intangible value in the company.
The paper defined Volume as the number of mobile device applications downloaded from the Google Play Store. Variety was the number of data permissions the app requests from the user (GPS, device model, etc.). Veracity was the percentage of employees in the company devoted to “Big Data” analysis based on open positions or job holder listings on LinkedIn. The sample was limited to S&P 500 companies with just one primary business app, but the 114 companies spanned finance, business, healthcare, hospitality, industrial products, consumer goods, entertainment, and lifestyle. The 2018 data from the 114 sample S&P 500 companies averaged 0.91 Tobin’s Q with a standard deviation of 2.37 and ranges from 0.13 to 11.32 while at that time the overall US stock market had a Tobin’s Q of 1.15.
An ordinary least squared linear regression model was used to find correlations between these variables and Tobin’s Q. Several control variables were included such as firm age, employee turnover, stock Beta (stock volatility relative to the market as a whole), and R&D intensity (R&D expenses relative to sales). The author’s three major takeaways were: (1) large volume correlates with lower Tobin’s Q, (2) volume * variety keeps Tobin’s Q steady, and (3) high veracity correlates with higher Tobin’s Q. Veracity proves to be the most interesting metric in terms of practical implication, statistical significance, and magnitude of impact on Tobin’s Q, while the Beta and R&D intensity control variables were significant and warrant discussion here too.
Veracity scales 1:1.20 with the Log2 of Tobin’s Q. This means doubling the percentage of big data scientists more than doubles a firm’s Tobin’s Q. Veracity’s impact is dampened, however, by stock Beta, which correlates positively with veracity but scales 1:-0.97 with Log2 Tobin’s Q. So while veracity increases Tobin’s Q, it also increases Beta, which in turn decreases Tobin’s Q. R&D intensity scales 3.3:1 with Log2 Tobin’s Q but does not correlate to veracity or Beta.
The bottom line is that not all companies stand to realize these gains on Tobin’s Q equally – specifically the most primed for potential impact have (1) lower percentages of “Big Data” scientists currently, (2) lower stock Betas, (3) higher R&D intensity. Those companies with a low veracity will have the largest marginal percent increase in Tobin’s Q per hire of “Big Data” scientist, and such companies tend to have a lower Beta already. Those companies with higher R&D intensity are expected to already have an increased Tobin’s Q, thus multiplying this by gains from increasing veracity will be greatest in absolute terms. Though not mentioned in the paper specifically, hospitals are this perfect fit.
Turning the dials: when and how to make the signal heard
The research presented was a cross-sectional study which provides correlations between variables at a single time point. Therefore, we cannot say that veracity causes Tobin’s Q to rise, and expecting a multi-million dollar rise in valuation per hire is unreasonable. But it is reasonable to think that additional hiring in “Big Data” can signal to the market that a hospital system is well poised for future growth in an increasingly data driven economy. To put this to the test, executives at companies trading well below the current market Tobin’s Q would be well served to do the following:
Day One: If your firm’s Tobin’s Q is below 1 or trending downwards greater than the entire market movement, it may indicate the market is not confident that your company is ready to leverage the latent value of “Big Data.” Increasing the percent of your company that are “Big Data” scientists may boost market sentiment in your favor. We foresee low stock Beta and high R&D intensity industries to be particularly well suited by this change and certain parts of the healthcare industry fits this description, but check your own company’s metrics.
After: Start to actively track the percentage of “Big Data” scientists on staff in your own firm and others. Unlike standard metrics like Beta or R&D intensity, this metric is probably new, particularly monitoring jobs listings of competitors which in-of-itself may be useful to see how they may be adapting.
Finally: While this study investigated the veracity metric based on LinkedIn job listings, there may be other ways to signal to the market besides a hiring spree of “Big Data” analysts. For example, announcing the intention for specific projects to leverage existing data or the intention to contract consultants to assess the current data use in your company may be sufficient and should be explored as well.
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Josiah Rudge is a PhD student in BioEngineering at the Georgia Institute of Technology and a member of the GA Tech PhD-2-Consulting Club. The research applications proposed in this article are solely the views of the author and do not necessarily reflect the views of the original academic journal article authors nor any individual member of our Editorial Board.