Two well-known figures in the world of financial data, Shruti Thaker and Jeremy Baksht, recently got new jobs. Nothing surprising about that, you might rightly say. But what is surprising is not their new roles—both are in data strategy—but rather the companies where they’re now performing those roles. Thaker and Baksht left careers in the financial data realm to work at large corporations outside financial services.

Thaker, most recently global data manager at JO Hambro Capital Management, who has also served as head of alpha capture and alternative data for active equities at BlackRock, and spent seven years at Citi and almost six years at UBS in systems and business analyst roles, has just joined brewing company Molson Coors as global information strategy architect.

Meanwhile, Baksht—who previously served as global head of alternative data at Bloomberg and chief revenue officer at Estimize, worked on tech deals for major industrial firms at Citi and JP Morgan, and most recently founded his own startup venture capital fund to invest in alternative data and analytics companies—has joined Walmart as head of strategy for its Walmart Data Ventures division.

So why are companies in industries outside the capital markets recruiting data professionals who’ve honed their skills on Wall Street? Alternative data consultancy Neudata recently published a report on the use of external data by corporations, and identified how different types of industries from industrials to e-commerce, real estate to retail, and healthcare to tourism can benefit from various types of datasets available on the market.

However, while “investment management firms have poured vast resources into their data science capabilities,” with dedicated teams of data scientists, analysts and strategists to cover sourcing, testing and compliance, Neudata notes that other industries often don’t have those resources in place, these skills are costly to recruit, and there’s a shortage of skilled professionals.

In addition, data providers surveyed by Neudata reported that potential corporate clients often aren’t in a position to get the most out of their data, citing a lack of systems to store, process and share data, and a lack of skilled data management professionals to map and process it. According to the report, companies plug these gaps by upskilling existing staff, or outsourcing some of their needs to third-party specialists. But for those wanting to build a data organization in-house, a logical step is to recruit people who’ve already done that elsewhere—from competitors, or (more likely, if an industry sector as a whole is new to data science) from other industries with more experience.

Neudata’s report focused on the challenges and opportunities facing companies that buy external data from vendors and other sources. But for some of the larger corporations, they are the de facto data source—and they also need data professionals to manage vast quantities of internally generated data (and also potentially, to combine that with externally purchased datasets).

For example, as one of the world’s largest retailers, Walmart has been investing in capturing and analyzing data on consumer trends for years, and more recently began a push to monetize its data assets. Last year, Ad Age estimated that for every $1 billion in sales, Walmart would earn $1 million from selling data. With 2022 revenues of $572.8 billion, that would generate more than half a billion dollars in data sales.

Here’s what Walmart says about the Data Ventures business in job postings on its website: “Data Ventures exists to unlock the full value of Walmart’s data by developing and productizing B2B data initiatives that empower merchants and suppliers to make better, faster decisions for the business.”

Walmart declined to allow Baksht to give an interview about his role. But the company already has a large data analysis arm, and likely wants his experience to create a distribution strategy (that is, how it gets that data to paying consumers), and perhaps to create data products it can sell specifically to financial consumers. After all, why would a hedge fund pay a small fortune for alternative data on parking lots, GPS data from mobile devices to show foot traffic, and anonymized point-of-sale information on credit card transactions—all of which the fund would need to connect together and interpret—to understand a store’s sales and revenue projections if the retailer is willing to sell it to you directly?

Molson Coors, on the other hand, was happy for Thaker to be interviewed. Perhaps that is because her role, as it stands today, is more about understanding how the brewing company can use the data it already has, and potentially acquire additional datasets to drive new product developments and consumer opportunities, rather than directly monetizing data derived from consumers. This doesn’t mean, however, that Molson won’t look to commercialize some of its data in the future. For example, would the company want to create a dataset representative of the brewing industry and its supply chain with its peers—and would it want to sell that data to its competitors, or would it be more valuable and useful to financial firms looking for insights into the industry or specific companies?

“I know people in the financial industry who would be very interested in data that comes from this industry. There are a lot of these industry datasets that are not consumer-facing that the financial industry hasn’t discovered yet,” she says.

Thaker’s new role involves architecting the company’s data strategy globally, designing both the technical and governance aspects that will allow it to generate insights, and exposes her to new types of data—obviously consumer consumption data, but also supply chain datasets, such as ingredients like wheat and hops, and the supply of raw materials that might impact bottle or can manufacturers.

“Any company with a supply chain worth its salt has data. The question is, how do you use that to build the next generation of the company?” she says. “So, for the first few months, one of my goals is to listen and figure out what people do and what data they have … and whether there are other datasets out there that we could buy to augment our own.”

Ultimately, much of her role will be establishing data governance practices around the business and supply chain data, so the company can use it to better understand consumer behavior and identify new opportunities.

“If an industry wants to survive and thrive, it needs to adapt and be looking for the next opportunities,” Thaker says. “For me, what’s interesting is creating a data environment that allows business decision-makers to see plays that were previously unavailable, such as markets where you don’t have the brand recognition or data on those markets. So, part of my role will be to look at how to achieve these insights … and how that can help other teams.”

Brain drain

Industry observers say they’re not surprised to see people start moving into corporate roles. These companies are increasingly large consumers and producers of data. They need the expertise to manage the incoming flow of data, to clean and organize it so it can be used by those who need it; to distribute it within their organization; and, in some cases, to commercialize it and ensure it is compliant and fit for use outside their organization.

That requires a certain level and type of expertise, but it also requires a certain mindset that’s hungry for new challenges.

“Everyone’s a data supplier. Everything turns out to have a finance angle, to a certain degree,” says Mark Etherington, CTO of data processing company Crux Informatics. “Finance definitely has a voracious appetite for data, and that’s why it’s been such a good breeding ground. The banks have always had the data, the compensation to attract the best staff, and deep pockets that allow them to experiment.”

But for those in data science who relish a challenge, banks may no longer offer the most attractive opportunities. “Banks have a lot of data, but companies like Walmart have more. And that might be more interesting to data professionals. That data is those firms’ competitive advantage, if they use it correctly. It’s the same as algorithm development: You need to test it, so there is huge demand for historical data,” Etherington says.

And just as all industries want to collect historical data—or more likely, organize what they’ve already been storing for years—to identify customer trends now that they have the technology to productively analyze it, financial firms will also want historical data to feed their investment analysis and trading algorithms. And, of course, they want it now. The problem is, it takes 30 years to produce 30 years’ worth of data. And the people who’ve been capturing that are these other industries—thus far, for their own use, but now they have vast historical datasets that are arguably just as valuable to others as to them.

David Murray, chief business officer at data science platform provider Devron, says Thaker and Baksht are probably not isolated cases, and that he expects to see other industries aggressively target professionals with experience of managing data within capital markets.

“It’s widely understood that there is tremendous value potential in data. Data science professionals are in high demand. There’s a shortage of suitable people. And in capital markets, there is a longstanding history of managing and manipulating data to glean actionable insights for areas such as pricing analytics, risk optimization, and customer segmentation. So, for those people who have data science skills within financial services, their experience makes them very appealing to other industries,” Murray says.

He adds that the appeal of broader and more meaningful roles in other industry sectors may be a key factor in luring data professionals away from capital markets.

“Financial services is at the sophisticated end of the continuum, and others are trying to catch up,” he says. “So, there can be an opportunity to take on a bigger, more senior role, and to have a bigger impact in other industries that are still progressing along the data science curve.”

This may be true in terms of the capital markets’ consumption and use of data, but Thaker says financial services may actually be playing catch-up when it comes to identifying data that has value and commercializing that data.

“Some firms may offer datasets of anonymized flows data. But from what I’ve seen, the sell side overall has only started bringing in data product managers in the last four or five years to look at things that previously may have been used to support things like, say, proprietary trading and figure out whether those can also be used elsewhere, or even sold to clients,” she says.

And as financial data professionals learn these skills, that makes their experience more portable and industry-agnostic.

“Data is a space where I think you can be industry-agnostic and be good at your job. I think it’s the start of a trend of people moving across industry in data roles—partly because the people coming into data roles in finance now are actual data professionals who understand the data, rather than being financial professionals who understand the technology,” she says. “True data professionals can move between industries. The way you manage data and make it available is the same, but the terminology differs.”

Simon Burton, co-founder and managing director of UK-based specialist recruitment agency CB Resourcing, says other industries—notably law firms and insurance companies—are heavily recruiting data management and data science talent.

“There is such high demand for that data science background, so they are looking to recruit from finance and other sectors. Industry knowledge is less important than data experience. They especially like people who have worked in data science at consultancies where they might have been exposed to 20 different projects and have dealt with governance issues around data,” Burton says.

One of the key governance challenges is supply chain risk management, or “know your supplier,” just as know-your-customer is a big issue for financial firms. Another crucial governance challenge where Etherington says the experience of financial data professionals will pay dividends for other industries is entity matching, which is key to being able to understand who or what a company’s data refers to. For example, a retailer or healthcare provider dealing with data about individual consumers or patients—even if that data is anonymized—still needs to be able to connect sales or treatments to the right individual profiles.

“Even banks struggle with entity matching. You need it to bring datasets together. That’s hard to do, and it appeals to a very highly intelligent type of person—the type who gets really excited about the results,” Etherington says.

So, rather than searching for something different among the same types of financial firms, perhaps data professionals seeking something new should look beyond finance, to industries where their talents and experience are even more in demand.

“It’s a good time to move jobs if you have that kind of skillset,” Burton says.