Is Data Really the New Gold?

Insights from MIT on leveraging data in your business with or without AI.

Everywhere you turn in tech today, the buzz is deafening - generative AI, new waves of Nvidia GPUs, hyperscale data center buildouts, and billion-dollar investments from Amazon, Microsoft, and Google. These headlines feed the belief that we’re on the verge of a data-driven Supercycle. The future, it seems, is being written in silicon and powered by data.

Salesforce’s recent campaign with Matthew McConaughey claims, "Data is the new gold." It’s a catchy phrase, but it deserves scrutiny. Is data truly the new gold, or is this another Silicon Valley soundbite chasing sentiment?

To evaluate that, I recently completed a course on data monetization from MIT’s Center for Information Systems Research (CISR). The course frames data monetization as a three-level continuum:

1. Improving: Using data to enhance internal decision-making and operations. Think faster time-to-insight, better forecasting, and more efficient workflows.
2. Wrapping: Embedding data and analytics into existing products to increase customer value. This includes dashboards, benchmarks, and predictive insights.
3. Selling: Packaging and commercializing data as a standalone product or service, whether directly or through APIs, exchanges, or marketplaces.

Leveraging the Maturity Model

This continuum is not just a framework—it’s a maturity model. Most companies start with Improving, but few reach the Selling stage. Even among the software giants, progress is uneven.

Let’s look at some examples:

• Improving: SAP has invested heavily in using internal telemetry to optimize its cloud operations and professional services. Yet, much of this insight remains behind the curtain. The opportunity to make this transparent and actionable for customers is still underleveraged.
• Wrapping: Salesforce offers robust embedded analytics through Tableau and Einstein AI. Their benchmark dashboards and predictive scoring are examples of wrapping, but adoption often requires costly customization and integration. The potential exists to democratize these insights with easier out-of-the-box offerings.
• Selling: Oracle, with its Data Cloud (formerly BlueKai), was one of the early movers in commercializing third-party data. But the platform has faced criticism over privacy concerns and shifting regulatory headwinds, which have dampened its growth. Selling data remains the hardest and riskiest part of the continuum.

The common thread? These companies have barely scratched the surface. Despite sitting on massive data reservoirs, many SaaS vendors still treat data as an operational byproduct rather than a monetizable asset. Technical debt, organizational silos, and unclear data ownership are common culprits.

Practical Progress

To progress, companies must invest in key capabilities:

• Data assets: Know what you have, where it is, and who owns it.
• Data platforms: Scalable infrastructure to store, process, and govern data.
• Data science: Analytical expertise to generate actionable insights.
• Customer understanding: Context for how customers benefit from those insights.
• Acceptable data use: Frameworks for ethical and legal data handling.

Each step on the Improving–Wrapping–Selling continuum should be tied to measurable business outcomes. That means starting with cost savings (e.g., operational efficiencies), and advancing to revenue generation, whether through better customer retention, price optimization, or direct monetization of data products. If you move all the way on the continuum, building up many of the capabilities listed above, you can actually sell a subscription to the data.

Let’s not overlook the cultural shift underway as the value-chain in technology evolves. Early in my career, I was consulting for a major software vendor during a meeting with a prospective client and a representative from what was then the largest hardware company. Midway through the meeting, the hardware rep—unaware of my role—leaned over and said, “I’d give away the software and consulting if I can close the hardware deal.” Even then, the value chain was shifting. Today, it’s shifting again—this time from product features and functions to data. (Ironically, that hardware company is now one of the largest consulting and software firms in the world.)

The most effective data monetization programs don’t just provide insights—they prompt and/or automate action. This is where Agentic AI shines. For example, imagine a SaaS tool that not only benchmarks a user’s KPIs but also suggests next steps—or even executes them—such as reallocating ad spend, rebooking travel to cheaper alternatives, reordering inventory, or triggering outreach to at-risk customers. As companies like Salesforce are not shy about telling you, Agentic AI has enormous potential, but it will need data to thrive.

Call to Action

If you are a SaaS company that houses customer data, you are not just in the software business—you’re in the data business. It’s time to think and act like it. Start by Improving with internal data-driven decisions. Then move toward Wrapping, enhancing your customer experience with insight-rich tools. Finally, explore Selling, when and where responsible and ethical data-sharing is possible. Use an incremental approach and cost-justify each step. Also, if your strategy is a vertical-industry focus – this will greatly improve your customer intimacy and show your commitment to those verticals.

Gold only has value when extracted and refined. The same is true of data.

Duane Kotsen, Partner

Marlborough Street Partners

Marlborough Street Partners is not a consulting firm. We are a team of senior operating executives that works with venture and PE firms on behalf of portfolio companies and directly with senior management teams to address the Inflection Points they face- from strategic challenges to operational dysfunction to capitalization issues. Our blend of fresh perspective and long experience Turns Inflection Points into Breakthroughs. 

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