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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.

 Managing Growth Without Budget Overruns

A scalable data pipeline architecture is essential for modern analytics and AI-driven operations. However, as businesses expand their data capabilities, cloud costs can escalate rapidly—often without clear visibility. Many organisations unknowingly overspend on inefficient data pipelines, redundant processing tasks, and restrictive vendor agreements.

In the Data Matas Season 2 premiere, Aaron Phethean engages AWS expert Jon Hammant to explore how organisations can scale data pipeline architecture while maintaining cost control. Their discussion outlines practical strategies for optimising infrastructure, enhancing scalability, and avoiding vendor lock-in.

This article distills those insights to help you improve your data pipeline architecture and reduce unnecessary expenses—ensuring growth does not come at the cost of inefficiency.

What You’ll Learn

• Why many businesses overspend on cloud-based data pipeline architecture
• How to optimise data transfer and ETL processing without compromising performance
• Strategies to reduce costs while scaling infrastructure effectively
• Actionable steps for avoiding vendor lock-in and leveraging AI for efficiency gains

“AI is scaling faster than governance mechanisms,” notes Jon Hammant, AWS UK & Ireland Lead. “Without proactive cost control, businesses lose agility as infrastructure costs surge.”

Meet the Expert: Jon Hammant

Jon Hammant leads the UK & Ireland AWS Specialist Team, driving data pipeline architecture, AI, compute, and cloud infrastructure strategies. He has extensive experience in cloud optimisation, helping enterprises scale without uncontrolled cost growth.

“Cloud infrastructure is reshaping how businesses operate,” Jon explains. “Cost optimisation must be integrated into data architecture from the outset.”

The Hidden Cost of Inefficient Data Pipeline Architecture

Cloud infrastructure costs can grow unnoticed, especially when data pipelines are built without consideration for optimisation. Many businesses provision excess compute resources, rely on always-on synchronisation, and maintain outdated processing schedules.

“Real-time processing has become default, but that doesn’t mean it’s always necessary,” Jon warns. “Costs rise when data pipeline architecture isn’t right-sized.”

1. Audit Your Data Pipeline Architecture and Cloud Spend

From reactive budgeting to proactive visibility.

A comprehensive audit reveals inefficiencies in existing data pipeline architecture. Many organisations underestimate the impact of idle compute resources, unused storage, and unnecessary data transfers.

Implementation Guidelines:

• Use AWS Cost Explorer or similar tools to analyse usage patterns.
• Identify data pipelines with low utilisation and redundant sync operations.
• Evaluate data transfer charges and archival storage usage.
• Audit workloads for underutilised ETL tasks.

A regular audit can reduce cloud costs by 20–30%—savings that directly support business growth.

2. Shift from Always-On Syncing to Smarter Scheduling

From default 24/7 data syncing to context-driven scheduling.

Not all data requires real-time processing. Businesses often maintain continuous synchronisation pipelines for workloads that could be run periodically. This significantly inflates infrastructure costs.

Implementation Guidelines:

• Classify data pipelines based on criticality: real-time, near real-time, or batch.
• Use scheduled tasks via AWS EventBridge for non-critical pipelines.
• Implement auto-scaling for fluctuating workloads.
• Use serverless data processing to minimise idle costs.

This approach can reduce data pipeline architecture costs by 40–60%, without impacting business performance.

3. Build Flexibility into Vendor Agreements

From rigid contracts to adaptable cost structures.

Multi-year contracts often lock organisations into pricing models that fail to reflect evolving needs. Flexible, usage-based pricing allows businesses to adjust infrastructure spend dynamically.

Implementation Guidelines:

• Reassess cloud vendor agreements before automatic renewal.
• Prioritise usage-based billing with tiered volume discounts.
• Tag data pipeline architecture components by project or team for better cost tracking.
• Combine reserved instances with on-demand for balanced spending.

A flexible pricing model can reduce cloud expenditure by 15–25% and improve budgeting accuracy.

4. Apply AI to Streamline Data Pipeline Management

From AI as a cost burden to AI as a cost optimiser.

AI-powered automation can significantly reduce the manual effort required to manage data pipeline architecture. By applying AI to capacity planning and anomaly detection, businesses enhance pipeline efficiency.

Implementation Guidelines:

• Identify high-effort tasks in data pipeline workflows.
• Automate routine ETL tasks with AI-based tools.
• Use predictive analytics to forecast infrastructure demand.
• Apply anomaly detection to catch unusual cost spikes early.

AI-driven automation typically results in a 30–50% reduction in data management costs while improving time-to-insight.

Your Optimisation Roadmap

A structured approach is essential to optimise data pipeline architecture effectively:

Phase 1: Conduct a full audit to identify cost-saving opportunities.

Phase 2: Implement smart scheduling to reduce processing inefficiencies.

Phase 3: Review and renegotiate vendor contracts for flexible pricing.

Phase 4: Deploy AI-based tools to automate and streamline operations.

“Sequencing matters,” Jon advises. “Visibility, then optimisation, followed by automation, it’s a continuous improvement cycle.”

Final Thoughts: Scaling Responsibly with a Smarter Data Pipeline Architecture

Scalable data pipeline architecture is essential to long-term growth. Yet, without cost optimisation, infrastructure becomes a liability rather than an asset. Begin by auditing your environment, adopt scheduling strategies, embrace flexible contracts, and invest in AI-led automation.

Cloud cost optimisation isn’t just an IT priority—it’s a business strategy that ensures sustainable, agile growth.

Resources to Get Started

• AWS Cost Explorer: Gain insights into your infrastructure spend.
• Matatika Cost Comparison Tool: Evaluate cost differences across usage models.
• Data Efficiency Blueprint: Access an 8-point framework for reducing data pipeline architecture costs and improving operational ROI.