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🚨 The Brutal Truth About Data Teams: Fixing Broken Analytics in 12 Months 🚨

For most companies, data is an afterthought, until it isn’t.

When you secure funding, scale, or face investor scrutiny, the big questions start rolling in:

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📊 What’s driving engagement?
📈 Which features improve retention?
⚙️ Are we delivering measurable outcomes?

Then reality hits: Your data infrastructure is broken.

When we raised funding, we thought we were ready to scale. Instead, we found a mess:

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⚠️ Postgres couldn’t handle the load. Queries crawled.
⏳ Dashboards were unreliable. No one trusted Tableau.
🤯 Analysts drowned in endless Slack DMs.
❌ Teams were flying blind. No clear KPIs, no product insights.

We were stuck in chaos, reacting instead of solving. It was clear: Fix this, or fail to scale.

This case study breaks down how we rebuilt our analytics infrastructure, eliminated data chaos, and scaled self-serve insights in 12 months with a team of 3 Analysts + 2 BI Engineers + 1 Data PM.

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🚀 4 Core High-Impact Outcomes: Before vs. After

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🚀 How We Fixed Broken Analytics in 12 Months: A Phased Approach

Phase 1: Stop the Chaos – Declaring “Data Bankruptcy” (Month 1)

The first step was hitting reset and giving the team some breathing room. We froze non-critical requests for a month and created processes to stop the endless ad-hoc chaos.

🔧 What We Did:

1. Froze all non-critical requests.
   • Only critical business metrics were approved.
   • Hired an intern to pull routine reports like last month’s revenue.
2. Built a Data Request Portal.
   • All requests had to go through a portal with clear guidelines:
     • A business impact score (1-10).
     • VP approval for anything flagged as urgent.
3. Gave analysts breathing room.
   • Analysts shifted their focus to high-impact strategic projects instead of drowning in low-value requests.
4. Established a Plan.
   • Stakeholder interviews were identified as the next step to uncover real data needs.

🎯 Impact:

• 📉 40% drop in data requests within a week.
• ⏳ Analysts regained 15+ hours per week for strategic work.
• 💡 The team finally had time to start planning long-term fixes.

Key Takeaway: Declaring “data bankruptcy” gave us the breathing room to reset. Without this step, we would have stayed stuck in the hamster wheel of reactive requests.
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Phase 2: Stakeholder Alignment – Uncovering the Real Problems (Month 2)

With breathing room secured, we moved on to stakeholder interviews and alignment to identify the biggest gaps and pain points.

🔧 What We Did:

1. Conducted stakeholder interviews.
   • Key questions:
     • What metrics keep you awake at night?
     • What’s your biggest time-waster?
     • Do you trust your data? Why or why not?
2. Standardized KPIs.
   • Defined core metrics like activation, retention, and revenue.
   • Consolidated conflicting definitions (e.g., “active user” was defined 5 different ways across teams).
3. Created the BI and Product Analytics Roadmap:
   • Mapped out what we need to do set up our product analytics tech stack.
   • Identified core BI models to migrate first.
4. Started hiring.
   • Hired BI Engineers and Analysts in parallel to support the roadmap.

🎯 Impact:

• 🛠 Teams aligned on one source of truth for core KPIs.
• 🚀 Faster queries in Looker improved initial adoption.
• 🔍 A clear roadmap emerged for fixing user behavior tracking and analytics.

Key Takeaway: Stakeholder alignment is critical. Without understanding the real pain points, you’ll waste time solving the wrong problems.

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Phase 3: Vendor Evaluation & Core Table Migration (Month 3)

We evaluated vendors and began migrating core tables from Postgres to BigQuery.

🔧 What We Did:

1. Evaluated vendors for product analytics.
   • Chose Segment + Amplitude for event tracking and behavioral analytics.
2. Migrated Postgres tables to BigQuery.
   • Focused on 20% of critical tables (e.g., users, active users, revenue) to speed up queries and build trust.

🎯 Impact:

• ⚡️ Query speeds improved 10x in BigQuery & Looker.
• 📊 Teams started seeing reliable metrics in Looker dashboards.
• 📊 Interim dashboards set up so stakeholders could get the core KPis they need.

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Phase 4: Event Design Framework (Month 4)

We designed a scalable event tracking framework to capture user behavior while avoiding data bloat.

🔧 What We Did:

1. Defined 100+ core events using a lean taxonomy.
2. Standardized event properties like product_area, screen_ name
3. Scrapped old Google Analytics tracking and started fresh.

🎯 Impact:

• 🧩 A clean, scalable framework that captured rich behavioral data.
• 🎯 Stayed under Amplitude’s 2,000-event limit.

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Phase 5: Event Implementation (Months 4-10)

We rolled out the event framework across 10 product areas over 6 months.

🔧 What We Did:

1. Worked with PMs and Engineers to embed tracking into development cycles.
2. Validated events across platforms (Android, iOS, Web) to ensure accuracy.

🎯 Impact:

• ✅ 100+ events implemented and validated.
• 🔍 Teams gained visibility into user behavior for the first time.

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Phase 6: BI Model Migration & Looker Rollout (Months 4-10)

In parallel with event tracking, we migrated BI models and built core dashboards in Looker.

🔧 What We Did:

1. Migrated 20-30 BI models to LookML.
2. Created Core KPI Dashboards for leadership and product teams.

🎯 Impact:

• 📈 Looker adoption hit 40% within 6 weeks.
• 🗑 Shut down 60+ outdated dashboards—no one missed them.

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Phase 7: Training & Documentation (Months 10-12)

The final phase focused on driving adoption through training and clear documentation.

🔧 What We Did:

1. Built a Metric Catalog documenting every key metric with definitions, owners, and calculations.
2. Conducted training for PMs, Engineers, and Analysts.
3. Held bi-weekly office hours and created a Slack channel for Q&A.

🎯 Impact:

• 🔥 Amplitude adoption increased within 2 months.
• 🎓 PMs began using data in product reviews, reducing dependency on analysts

Conclusion: The Real Fix - People, Process & Technology

Fixing analytics isn’t just about deploying tools—it’s about aligning people, process, and technology to create a system that actually works.

🚀 Process → Establishing data contracts to ensure:

• Event instrumentation happens early in development
• Backend teams inform data teams before making changes
• Engineers test tracking before release
• PMs own instrumentation, not just analysts

👥 People → Empowering teams by:

• Eliminating ad-hoc SQL requests that waste analyst time
• Saying no to low-impact data asks
• Training teams to self-serve insights instead of relying on data teams

🛠️ Technology → Using the right tools, not the most:

• Fewer, well-designed data models instead of 100 redundant ones
• Tools that enable adoption, not just complexity
• A system that scales without constant fixes

Most companies fail because they tackle technology first, but without process and people, the best tools won’t matter.

📩 Get a Free Analytics Audit → I’ll assess your setup and show you 3 quick wins to clean up your data, reduce wasted time, and unlock real insights.

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