Posts Tagged ‘DataStrategy’

Data Analytics Strategy: How Quantum Thinking Enables Change Without Disruption

This article explores how quantum thinking can inform a modern data analytics strategy, enabling teams to innovate safely without disrupting existing operations. Based on insights from David Draper, Data Science Manager at Iris Software Group, it offers practical guidance on modular system design, embedding innovation in delivery cycles, and improving AI explainability. Ideal for data leaders seeking resilient, forward-looking analytics strategies.

Stop Fixing Data Pipelines – Start Driving Strategy

This article examines how unreliable data pipelines can trap data teams in endless maintenance work, draining strategic capacity. It explores practical solutions for building resilient, self-healing pipelines, allowing engineers to focus on delivering insights and driving business growth.

Crypto-Inspired Data Strategy: What Top Teams Do Differently

This article explores how data teams can adopt strategic practices from the crypto industry, particularly MoonPay’s approach under Director of Data Emily Loh, to move beyond reactive tasks and implement a proactive, value-driven data strategy. It covers resource allocation frameworks, AI implementation, and system design principles to help teams operate more effectively in fast-changing environments.

Turning Data Challenges into Strategic Advantages

In today’s competitive landscape, a robust data strategy is essential. Data teams often struggle to evolve from reactive service providers to proactive strategic partners. Crypto data teams, facing rapidly shifting markets and strict regulatory environments, provide actionable lessons for all sectors.

 

Learning from the Frontlines of Crypto

In this article, you’ll discover how Emily Loh, Director of Data at MoonPay, applies advanced data strategy principles to turn challenges into opportunities:

  • Implementing a balanced 20/40/40 resource allocation framework
  • Escaping the cycle of reactive work
  • Deploying AI for measurable strategic value
  • Building adaptive, future-proof data systems

 

From Literature to Leading-Edge Data: Meet Emily Loh

Emily Loh leads a 15-person data team at MoonPay covering engineering, data science, and machine learning. Formerly of Coinbase, Loh brings an unconventional background in literature that enriches her team’s storytelling capabilities. “This is just storytelling,” she says. “It helps us focus on outcomes, not just outputs.”

MoonPay, the “Stripe of crypto,” processes irreversible transactions in real-time while navigating volatile regulatory environments. These conditions demand an agile and forward-thinking data strategy.

 

Applying the 20/40/40 Framework for Data Strategy

At MoonPay, Loh uses a structured resource allocation model:

  • 20%: Business as usual
  • 40%: Strategic building
  • 40%: Research and innovation

This method creates protected space for long-term projects and strategic initiatives, reducing the risk of reactive overload.

 

Practical Implementation:

  • Track team time for 2-3 weeks to establish a baseline
  • Identify automation opportunities to free up resources
  • Develop an opportunity scoring matrix with ROI and strategic alignment
  • Reserve dedicated calendar slots for innovation (e.g., “Research Wednesdays”)
  • Launch monthly showcases to highlight research outcomes

Whether you adopt a 25/50/25 or 30/40/30 split, the key principle remains: intentionally allocate time to support strategic data strategy goals.

 

Strategic AI: Beyond the Hype

Many companies fall into the trap of implementing AI without purpose. Loh’s approach is more disciplined: AI must serve a clear business function.

Steps for AI Implementation:

  • AI Value Audit: Identify the top 3-5 tedious tasks per team member and calculate total hours spent
  • Start Small: Use tools like Cursor to automate low-risk coding tasks
  • Focus on Augmentation: Enhance, not replace, human capabilities
  • Measure Success: Track pre/post time savings and calculate ROI

“Less time on YAML files, more time on value,” says Loh. A focused AI strategy elevates your data team’s effectiveness.

 

Future-Proofing Your Data Systems

Building systems for uncertain futures is core to effective data strategy. Crypto offers an extreme example, but lessons apply across AI, fintech, and e-commerce.

Future-Proofing Tactics:

  • Modular Architecture: Build loosely coupled components with defined interfaces
  • Scenario Planning: Conduct quarterly workshops to anticipate market and regulatory changes
  • Data Fundamentals: Monitor data quality and manage metadata comprehensively

“We need laser focus on priorities,” says Loh. A future-ready data strategy requires both adaptability and foundational strength.

 

Bringing It All Together

Crypto data teams thrive under pressure because they implement structured, flexible, and forward-looking data strategies. By:

  • Allocating time intentionally
  • Saying no to low-impact work
  • Applying AI with purpose
  • Building modular and adaptive systems

…you can transition from a reactive support function to a strategic business partner.

 

Who Will Benefit Most

Mid-level data leaders navigating operational and executive pressures will gain the most from these lessons. Whether in startups or large enterprises, these practices foster sustainable innovation.

 

Your Next Steps

Begin with a time audit and apply the 20/40/40 framework. Build modularity into your systems. Above all, maintain clarity on strategic priorities.

Learn More To hear the full conversation with Emily Loh and discover additional insights, listen to the complete Data Matas podcast episode.

How to Deliver a Zero-Risk ETL Transformation

Let’s be honest—ETL transformations have a bad reputation. Talk to any data leader, and they’ll tell you the same thing: it’s too disruptive, too expensive, and too risky. They worry about downtime, getting locked into another overpriced vendor contract, and the strain on internal teams. That’s exactly why at Matatika, we’ve built an approach that eliminates these risks entirely—no downtime, no wasted spend, and no surprises.

7 Data Strategies That Work – What the Best Data Teams Do Differently

Every data team wants to scale efficiently, reduce costs, and deliver real business value. But in practice, many struggle with siloed workflows, unreliable data, and costly inefficiencies. Since recording Season 1 of the Data Matas podcast, I've reflected on the key levers these great teams are using to deliver value in their businesses and pulled together the seven of the biggest lessons. These aren’t abstract theories—they are practical, tested strategies from professionals who have made data work for their organisations.