The Next SaaS Enterprise Value Imperative: Building Data Assets

May the Data Be With You

In 2017, The Economist proclaimed, “The world’s most valuable resource is no longer oil, but data.”

Back then, I was knee-deep in enterprise software, playing the role of a technology researcher.

Since this time, I’ve pivoted towards data, getting my hands dirty in broader B2B (as well as government and non-profit) data assets as an analyst, founder, investor, and board member.

I can count on one hand the times in my academic and professional life when I’ve had a “spidey-sense” that something big was coming.

This is one of those times—think of it as the nerd’s version of a Wall Street “gut feeling,” except with less risk of a felony indictment. You know, the kind that folks like Nancy Pelosi or my old Penn honors history seminar classmate Josh Gottheimer (another member of Congress) might have if they weren’t apparently playing the stock market with cheat codes.

For the past year, this sense has been tingling. It’s not just from advising businesses inside and outside the procurement and supply chain domains, where I’ve spent most of my career, but also from diving back into the operational side of a start-up for the first time in over 20 years within direct materials procurement technology.

It turns out, going back into the SaaS trenches has a way of shaking loose some pretty potent ideas—kind of like how flying Spirt or Ryanair reminds you that the extra few bucks spent on Southwest are worth every penny.

Here’s Looking at You, Data

Data assets today aren’t just the realm of traditional data providers like price reporting agencies such as MetalMiner (where I'm cutting my teeth on the ground) or Expana (formerly Mintec).

Increasingly, they’re being generated by software companies as well.

In procurement, Coupa Software was the first to create shared community insight at scale by industry (not only a key selling point, but one I would argue has played a vital role in making the core P2P application so sticky and appealing on a vertical basis, even without a deep verticalization app strategy). 

Consider some examples of unique data assets in the broader B2B sector, including but not limited to procurement, supply chain, and Office of the CFO technology:

• Commodity price information (ingredients, raw materials, semi-finished materials, finished materials, labor, etc.)
• Transactional or network data, which could include price trends or volume trends (either in aggregate or by customer segment/industry)
• Transactional data and operational performance
• Trade flow (cross-border data)
• Spend analytics
• Supplier performance data (on-time performance, lead-time, quality, etc.)
• Customer sentiment data (e.g., customer feedback, reviews, and surveys)
• Demand forecasting data (lead-time, inventory, etc.)
• Inventory data
• Product lifecycle data
• Logistics and transportation data (routes, pricing, delivery performance)
• Fulfillment information
• Risk management data (trading partner information/insight, geopolitical risks, weather, natural disasters, financial stability of suppliers, etc.)
• Correlation analysis (across data sets)

I Love the Smell of Data In the Morning 

When leveraged individually or together in new ways, all of these data assets (listed above) represent a “win, win, win, win” — except for those who aren’t focused on building or leveraging them. 

There is virtually no way to lose for those who embrace data (provided the data is valuable).

For software companies, data can create new or auxiliary revenue streams while boosting core sales and customer satisfaction—think of it as adding a little guac to your SaaS Chipotle burrito (pricey, but actually the best part about the meal). These assets may even spawn entirely new businesses or business lines. Just make sure your customer agreements allow for aggregate usage!

For existing data businesses, now is the time to shine. Think about provisioning your data in new ways—like an API or other data platforms. If you’re still selling data the old-fashioned way, you might as well be hawking Cardinals gear at the Cub’s annual winter convention (when they convince loyal season ticket holders like me to believe this “will be the season”.) 

For the investment community, including the quantitative trading funds, data can enable alpha in cases where others don’t have it—or don’t have it as quickly. In a world where milliseconds matter, the right data is like having the winning Powerball numbers a day before the draw.

And for corporations using enterprise technology, some of you may be sitting on potential gold mines of data assets you can leverage or even proprietary information sets. These can help improve operating performance or even uncover entirely new opportunities for expansion.

Need a place to start? Gulp Data is doing some pretty cool things (I just started working with them in one of my software ventures).

Here’s Looking at You, AI

By now, you’re probably asking what the role of GenAI (and broader AI/ML) is in all of this. 

In short: AI is not only sprinkling value on data assets but is sometimes turning them into pure gold.

If data is the new oil, then GenAI and broader data science (e.g., AI/ML including neural nets) is the refinery that transforms crude into high-octane fuel—fuel that powers everything from decision-making engines to predictive models with uncanny accuracy.

At MetalMiner, for example, we are using the power of neural nets to bring a new level of accuracy to price forecasts and sourcing/pricing strategies in metals.

AI doesn't just analyze data; it creates new possibilities from it. Whether it’s generating insights from massive unstructured datasets or simulating market conditions that haven’t yet occurred, AI/ML is taking data assets and supercharging their value.

As The Economist recently put it, “The true value of data is only realized when AI can be applied to make sense of it.”

Why is AI such a game-changer? It’s because it brings automation, scale, and a level of creativity that’s impossible to achieve with traditional data analytics. Imagine an AI that doesn’t just tell you what happened or even what will happen but also explores “what could happen if..."

For a procurement twist, consider commodity data insights combined with operational data (and broader forecasting data) becoming the basis of a "virtual" category manager in direct materials—one who constantly not only runs scenarios but also comes up with strategies and executes on them.

This ability to not just predict but to create (and act) is why AI/ML is becoming the must-have tool for anyone serious about leveraging their data assets. As a Harvard Business Review article notes, “Companies that integrate AI into their data strategy are not just playing catch-up—they’re leaping ahead in ways that could redefine entire industries.”

In essence, AI/ML is turning data into a living, breathing entity that can adapt, learn, and even innovate. 

So, to all those in the SaaS and data business world: It’s time to take a good, hard look at how you’re building, using, and monetizing your data assets.

With AI/ML in the mix, you might find that you’re sitting on a treasure trove waiting to be mined—or, in this case, a goldmine waiting to be minted.