Crypto-Inspired Data Strategy: What Top Teams Do Differently

In today’s data-driven environment, a strong data strategy is critical to business success. Not only do teams struggle to move beyond reactive roles, but they also face pressure to deliver measurable results. Accordingly, crypto data teams—who operate under volatile market conditions and strict regulation—offer compelling lessons on proactive leadership.

Lessons from Crypto’s Frontline

In this article, you’ll explore how Emily Loh, Director of Data at MoonPay, applies a disciplined data strategy that turns volatility into opportunity. Specifically:

• She implements a 20/40/40 resource allocation model

• She escapes the cycle of ad-hoc work

• She uses AI intentionally to boost value

• She builds adaptable, future-ready systems

Moreover, these methods apply well beyond crypto—to fintech, AI, and e-commerce alike.

From Literature to Leading Data Teams

Emily Loh leads a 15-person data team at MoonPay, covering engineering, machine learning, and analytics. Previously at Coinbase, she brings a literature background that enhances strategic storytelling. As a matter of fact, Loh believes effective communication is key: “It’s just storytelling. It helps us focus on outcomes, not just outputs.”

MoonPay, often called the “Stripe of crypto,” handles real-time, irreversible transactions. Consequently, the firm needs a resilient and forward-looking data strategy to manage risk and maintain trust.

Data Strategy in Action: The 20/40/40 Model

Loh’s team uses a structured time allocation approach:

• 20% on business-as-usual tasks

• 40% on strategic project work

• 40% on long-term R&D and innovation

This allocation ensures teams avoid burnout while maintaining innovation velocity. In other words, they protect space for growth.

How to Deploy the Framework

To clarify the model’s implementation:

• First, track team time for 2–3 weeks to set a baseline

• Then, identify and automate repetitive tasks

• Also, develop a scoring matrix to prioritise based on ROI and alignment

• Reserve time blocks for focused innovation (e.g., “Research Wednesdays”)

• Finally, launch internal showcases to promote outcomes

Although the percentages may vary (e.g., 25/50/25), the principle remains: consistent time investment enables strategic execution.

Strategic Use of AI

While many teams rush into AI without purpose, Loh applies it with precision. In fact, every implementation is expected to yield measurable returns.

Her Four-Step AI Framework

1. AI Value Audit – Identify 3–5 repetitive tasks per team member

2. Start Small – Use tools like Cursor to reduce effort on simple code

3. Augment, Don’t Replace – Empower people, not sideline them

4. Track Results – Measure pre- and post-implementation gains

For instance, reducing YAML file configuration frees time for strategic thinking. Thus, a targeted data strategy improves productivity and morale.

Future-Proofing with Intent

Future-ready systems are not just a goal—they are essential. Undoubtedly, crypto is one of the most challenging environments for data teams. Nonetheless, its strategies offer transferable insights.

Future-Proofing Tactics

• Modular Design – Build systems with loosely coupled services

• Scenario Planning – Run quarterly workshops to anticipate change

• Data Governance – Monitor quality and manage metadata diligently

Hence, teams that adopt these practices can adapt swiftly—without compromising on data integrity. With this in mind, Loh emphasises clarity: “We need laser focus on priorities.”

What Top Teams Do Differently

All things considered, successful teams execute data strategy with intention. They:

• Allocate time strategically

• Say no to low-impact activities

• Deploy AI with clear ROI goals

• Build systems that are modular and adaptive

Therefore, these practices shift teams from tactical delivery to strategic leadership.

Who Benefits Most?

Both mid-level data managers and senior technical leaders can benefit. Whether you’re in a startup or a scaled enterprise, this approach fosters sustainability and innovation. What’s more, it offers a replicable path to long-term impact.

Take Action Now

To begin with, conduct a team time audit. Then, test the 20/40/40 model. Next, assess your AI initiatives for strategic alignment. And above all, anchor your progress with a robust data strategy.

To hear the full conversation with Emily Loh, listen to the latest episode of the Data Matas podcast.

#Blog #Analytics Leadership #Crypto Tech #DataStrategy #Innovation Frameworks