Posts Tagged ‘DataStrategy’

Stop Manually Uploading Spreadsheets: 5 High-Impact Use Cases (and How Resident Advisor Fixed It)

If your week still includes exporting CSVs and uploading them into dashboards, you’re paying a “data tax” in delays, context switching, and stale numbers. Here are the top five use cases where teams should replace manual uploads with a real Google Sheets → warehouse pipeline plus a quick look at how Resident Advisor (RA) made this work in production.

Build Strategy First. Choose Technology That Deserves It

Introduction Every organisation has lived this story. A new data platform or AI tool promises to fix everything. Budgets are approved, contracts are signed — and a few months later, the same problems remain. Dashboards don’t align, pipelines still break, and confidence in the numbers keeps slipping. For Dylan Anderson, Head of Data Strategy at Perfusion, the real issue isn’t the technology itself — it’s where teams choose to begin. “Vendors are great at selling the idea that their product will solve all your data needs,” he says. “But strategy isn’t about buying more tools. It’s about helping the business reach its goals using data, technology, and AI in the right way.” In this episode of Data Matas, Dylan joins host Aaron Phethean to explore why strong data strategies always start with purpose, and how honest, outcome-driven technology earns its place in the stack.

How high-performing data teams align business goals, data strategy, and technology to build trust that lasts

How to Scale Mixpanel Data Efficiently Without Spiralling Costs

Your product's growing. More users, more events, more insights flowing through Mixpanel. That's exactly what you want. The problem? As your Mixpanel event volume increases, your data infrastructure costs often grow faster than your revenue. High-volume, append-only event data breaks traditional ETL pricing models. Every duplicate event costs money. Every unchanged property gets billed. Growth becomes a financial penalty. There's a smarter way to handle event data at scale.

Three Things Every Data Leader Should Kill Before Building Another Dashboard

Most organisations are drowning in dashboards that no one trusts. In this Data Matas episode, former Worldpay and FIS data leader Phil Thirlwell explains why the key to better decisions isn’t building more it’s stopping first. He breaks down how dashboard sprawl, KPI overload, and service-desk habits create chaos, and how treating dashboards like products can rebuild trust. Phil shares practical ways to simplify metrics, prioritise outcomes, and run data teams with purpose. The takeaway: fewer dashboards, clearer decisions, stronger alignment between data teams and the business.

 How to kill dashboard sprawl, service-desk habits, and KPI overload to rebuild trust in data.

How Hypebeast Reached 97% AI Adoption Without Fear or Layoffs

At Hypebeast, 97% of staff now use AI daily not out of fear, but choice. Director of Data & AI, Sami Rahman, reframed AI as a creative ally, not a threat. By focusing on practical wins, like speeding up research and cutting drudgery, he built trust and curiosity. Instead of pushing tools, he created demand through scarcity, measured impact rigorously, and deleted underused agents without sentiment. The result: adoption that stuck, creativity that flourished, and teams that saw AI as empowerment, not replacement. A playbook for leaders who want AI adoption to last built on trust, not hype.

AI adoption is at the top of every data leader’s agenda. Yet most attempts stall. Leaders flood their teams with new tools, staff get overwhelmed, and adoption drops. In some cases, AI is seen as a threat rather than an enabler.

At Hypebeast, it’s different. 97% of staff now use AI agents in their daily work. Not because they were forced, but because they wanted to.

In this episode of Data Matas, I spoke with Sami Rahman, Director of Data & AI at Hypebeast, about how he made AI adoption stick. His story offers grounded lessons for any data leader trying to balance hype with reality.

Meet Sami Rahman

Sami leads Data & AI at Hypebeast, the global fashion and lifestyle brand. His career spans psychology, counter-terrorism, and data science — giving him a rare perspective on how people, systems, and trust interact.

That unusual career path means he doesn’t just see AI as technology. He sees it as part of a wider human system — where behaviour, incentives, and culture matter just as much as code.

“We’re a creative company. We don’t want to replace journalists or designers. But we can use AI as a force multiplier — speeding up research, consolidating information, and helping people make decisions faster.”

That human-first but pragmatic outlook shaped every decision in Hypebeast’s adoption journey.

Why Most AI Adoption Efforts Fail

AI is no longer optional. Boards and executives expect adoption. But many teams fail to deliver value. Why?

Sami points to fear — not of the technology, but of being abandoned.

“The reason people are fearful around AI isn’t the tech. It’s because they don’t trust governments or institutions to look after them if jobs disappear.”

This distinction matters. If AI is framed as replacement, staff feel threatened. If it’s framed as empowerment, they engage.

Psychology backs this up. People resist change when they fear loss of control or status. The solution isn’t just better tech — it’s better framing. Data leaders need to talk about AI as a tool that supports their teams’ value, not one that makes them redundant.

Lessons from Hypebeast’s Adoption Journey

1. Frame AI as a Force Multiplier

At Hypebeast, AI is not a substitute for creativity. It’s an assistant. Tasks like research, summarisation, and trend monitoring were made faster and easier, while final judgement stayed human.

“We’re not trying to replace jobs. We might automate manual tasks, but we won’t remove the human side.”

This framing reassured staff that their value remained central — and made AI a welcome tool, not a competitor.

Implementation guidance:

  • What to do first: Communicate clearly that AI enhances, not replaces.
  • Tools: Introduce AI where speed and consolidation matter (e.g. research, summarisation).
  • Watch-outs: Don’t oversell — focus on real, modest gains.
  • Benefit: Higher trust and willingness to experiment.

2. Focus on “Unsexy” Use Cases

Flashy AI demos rarely translate into real value. Sami leaned into the unglamorous but high-impact tasks: scanning social feeds, packaging intelligence, automating logistics and finance.

“We leaned into use cases that aren’t super sexy but free up time.”

By cutting drudgery, staff had more time for meaningful creative work.

Implementation guidance:

  • What to do first: Audit manual processes that drain time.
  • Tools: Simple AI agents for monitoring, reporting, logistics.
  • Watch-outs: Avoid over-investing in “showcase” projects.
  • Benefit: Faster results, more trust in AI.

3. Create a Curiosity Gap

Perhaps the boldest move was delaying access. For 10 weeks, Sami drip-fed teasers: short demos showing what AI could do.

“It was 10 weeks before we gave anyone access — on purpose… By the launch, adoption went from 3% to 95% in three weeks.”

Scarcity created FOMO. Instead of pushing adoption, Hypebeast created pull.

This contrasts with most change management programmes, which over-prepare staff with slide decks, training, and handholding. Sami flipped the playbook — and in his industry, it worked.

He’s quick to note it wouldn’t fit everywhere. In banking or pharma, where regulation and compliance demand rigour, leaders may need a heavier hand. But in fast-moving creative industries, curiosity was the lever.

Implementation guidance:

  • What to do first: Hold back, and drip-feed examples.
  • Tools: Short demo videos or snippets to spark curiosity.
  • Watch-outs: Don’t launch too early before demand builds.
  • Benefit: Rapid, voluntary adoption.

4. Kill Zombie Agents Without Sentimentality

Hypebeast set strict benchmarks: daily or weekly agents had to hit 80% usage. If they weren’t used, they were deleted.

“If an agent isn’t used, we delete it. No sentimentality. It’s not failure — it’s iteration.”

That unsentimental approach kept adoption high and avoided wasted energy.

This is another overlooked lesson. Too many teams keep “zombie tools” alive because someone invested time or money. Sami’s product mindset — measure, test, delete — freed his team to focus only on what added value.

Implementation guidance:

  • What to do first: Define usage thresholds per agent type.
  • Tools: Usage dashboards, adoption metrics.
  • Watch-outs: Don’t cling to underused tools.
  • Benefit: Consistently high adoption, leaner portfolio.

Measuring Adoption: Beyond Usage

Usage was Sami’s primary metric, but adoption measurement can and should go deeper. Data leaders can track:

  • Time saved on manual tasks
  • User satisfaction with AI support
  • Error reduction in workflows
  • Frequency of repeat use across teams

By triangulating usage with impact, leaders can see not just whether AI is being used — but whether it’s making a difference.

This level of measurement is critical for building trust with executives and avoiding the “we bought AI, but what did it achieve?” backlash.

Real-World Impact

Hypebeast reached 97% adoption within three weeks of launch. Staff now use AI agents daily across journalism, retail, logistics, and design.

The before state was one of downsizing, high pressure, and manual workloads. The after state is a team with more time for creativity, backed by systems that take care of drudgery.

Instead of creating fear or resistance, the approach built curiosity and trust. AI is now embedded in workflows, freeing staff to focus on creative and strategic tasks.

Putting It All Together

Hypebeast’s success was not built on hype or heavy-handed change management. It came from reframing AI as support, solving practical problems, creating demand through scarcity, and cutting what didn’t work.

The results speak for themselves: adoption consistently above 90%, and a team that sees AI as an enabler, not a threat.

For data leaders, the playbook is clear:

  • Start with framing — AI enhances, it doesn’t replace
  • Automate boring tasks first
  • Create demand with curiosity, not forced adoption
  • Be ruthless with underperforming tools

Your Next Move: A Leader’s Checklist

Before your next AI rollout, ask yourself:

  1. Have I made it clear AI is here to support, not replace?
  2. Am I solving everyday pain points first, not chasing flashy demos?
  3. Can I create demand by showing value before rolling out access?
  4. Do I have clear benchmarks for adoption and usage?
  5. Am I willing to cut what doesn’t deliver?

Tick those five boxes, and you’ll be far closer to adoption that actually sticks.

Dig Deeper

This article is based on Data Matas Episode [X] with Sami Rahman, Director of Data & AI at Hypebeast.

📺 Watch the full conversation: https://www.youtube.com/@matatika
🎙️ Listen to the podcast: https://www.matatika.com/podcasts/

 

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 blog explores how MoonPay's Emily Loh uses a disciplined data strategy to drive innovation, improve focus, and future-proof infrastructure. Her 20/40/40 model and AI-first framework offer a replicable path for mid-level data leaders.

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

The term “zero-risk ETL transformation” may sound ambitious, but it’s real, proven, and achievable. With Matatika’s phased rollout, rigorous testing, transparent pricing, and post-deployment efficiency, it’s no longer a buzzword, it’s best practice. ETL doesn’t have to be hard. It just has to be done right.

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.