Data is an Asset — But Only if You Treat It Like One
We love to talk about data as the new oil, the new gold, the most valuable asset a company can have. But here’s the thing: data is only an asset if it’s treated like one — strategically, responsibly, and with full awareness of the operational risks it carries.
Today, companies generate massive amounts of data without fully understanding its value, its vulnerabilities, or its potential. As someone who works at the intersection of risk, strategy, and systems, I’ve come to believe that the biggest untapped opportunity for businesses isn’t just using more data — it’s managing it better.
Let’s talk about how.
The Hidden Reality of “Data as an Asset”
If you listed your company’s most valuable assets on a balance sheet, where would your data show up?
It likely wouldn’t — not in any meaningful way. Yet in many business models today, especially in fintech, SaaS, and AI-driven industries, data is the lifeblood of operations, product development, and customer insight. Despite this, data often lives in fragmented systems, is governed by unclear ownership, and is rarely audited for quality or risk exposure.
We celebrate companies for being data-driven. But ask them for their data risk map, and you’ll often get a blank stare.
Why This Matters: Fintech, Fashion, and the Data-Driven Edge
In data-rich sectors like fintech, how data is managed is the business model. Credit scoring, fraud detection, customer onboarding — all of it depends on clean, secure, and governed data. Founders often pitch their platforms as intelligence engines, yet few invest early in the risk and governance practices that make this intelligence sustainable. For fintechs aiming to scale or seek funding, demonstrating operational maturity in data management is increasingly a competitive advantage.
Even in fashion and lifestyle—where creative branding reigns—data plays a growing role in everything from supply chain traceability to customer personalization. Brands that want to promote conscious consumption, build resale ecosystems, or optimize their inventory need to rely on reliable product and consumer data. If that data is fragmented or biased, the sustainability promises or the user experience quickly fall apart.
Whether you’re building a payments app or a circular fashion platform, data isn’t just a resource — it’s your infrastructure. And like any infrastructure, it needs to be stress-tested, governed, and built for scale.
Data Due Diligence: Not Just for M&A
Data due diligence is usually mentioned in the context of mergers and acquisitions — a quick check to make sure customer and product data can be migrated or monetized.
But that mindset is outdated.
Data due diligence should be an ongoing strategic practice for all companies, especially those looking to scale, raise funds, or build long-term brand trust. It should answer questions like:
- What data do we collect and why?
- Who owns the quality, privacy, and security of this data?
- What’s our exposure if the data is misused or lost?
- How easily can we generate value from this data — and prove it?
For founders and leadership teams, this is less about compliance and more about clarity: understanding your data landscape is understanding your business model’s foundation.
The Operational Risk Layer
From a risk management lens, the implications are sharp.
Unstructured, poorly governed data introduces risk at multiple levels:
- Reputational Risk: Data breaches are trust destroyers. Customers don’t forget.
- Strategic Risk: Faulty or biased data leads to bad decisions, flawed models, or failed product features.
- Regulatory Risk: As regulations like the EU AI Act and data localization laws expand, data missteps can get expensive.
- Concentration Risk: Relying on one third-party vendor to process or host critical datasets without a contingency plan? That’s a systemic risk waiting to surface.
In my work, I’ve seen how operational risk frameworks often overlook data because it doesn’t feel as “tangible” as other business inputs. But in reality, it’s deeply embedded in every operational process — and its fragility is often invisible until something breaks.
How to Actually Operationalize “Data as an Asset”
Let’s move this from philosophy to practice. Here’s how companies can treat their data more like an asset — and less like digital clutter.
- Inventory Your Data
Map what you have. Know where it lives, who accesses it, and what condition it’s in. You can’t value what you don’t understand. - Create a Data Responsibility Matrix
Who is responsible for quality? For protection? For monetization? Assigning clear roles makes a huge difference. - Audit Data Quality Regularly
Like financial audits, data quality checks reveal hidden weaknesses — outdated records, missing fields, duplication. - Align Data Strategy with Business Strategy
If your company is scaling, entering new markets, or developing new products, your data plan should evolve too. Static strategies are dangerous in fast-moving sectors. - Build in Risk Controls
Apply operational risk thinking: stress test data availability, implement access controls, and prepare response plans for data incidents. - Treat Data as a Long-Term Investment
This means budgeting for governance, tooling, and talent — not just chasing growth through volume.
Final Thought: Data is Not the New Oil — It’s the New Infrastructure
The companies that win tomorrow aren’t the ones that simply have data. They’re the ones that know how to govern, trust, and extract insight from it — responsibly.
Just like no serious business would ignore their financial books, no serious business should ignore the state of their data.
So, ask yourself: if data is one of your company’s core assets, what’s your plan to protect and grow it?
