Bridging OKRs & Execution: A Data Framework Approach from Trendyol Tech

# Bridging OKRs & Execution: A Data Framework Approach from Trendyol Tech Ideas are easy, but execution is everything. According to Harvard Business Review research and the findings of Kaplan and Norton, 90% of companies worldwide fail to execute their strategies. **Why?** Because while strategy is often soaring in the clouds, engineering or sales teams are down on the ground, dealing with the mud. The bridge between them is usually missing or broken. If OKRs are nothing more than static figures on a spreadsheet, they cannot influence daily decisions teams make. ## Turning Strategy into Fuel for Operations: Data Frameworks Companies often fall into three fundamental traps while striving to become data-driven: * **Metric Overload**: Measuring everything to measure nothing. An abundance of data obscures focus and creates paralysis by analysis. * **Erosion of Trust**: If data exists but no one trusts it, that data is nothing more than a liability. When teams encounter conflicting data points, they inevitably revert to following their gut instincts. * **Walking in the Dark (Missing Data)**: When critical information is missing at the moment of decision-making, you are condemned to rely solely on intuition. ## Hero of the Solution: Product Operations (Product Ops) Who will resolve this chaos? Product Ops is uniquely positioned to tackle this challenge because: * They possess a broad perspective, observing how data flows across the entire company and how different teams interact with one another. * They have the power of influence, able to bridge the gap between leadership and execution teams. ## Building a Robust Data Framework in 4 Steps 1. **Define What to Measure**: Instead of asking which metric should we look at?, ask which business question are we trying to answer? 2. **Design the Data Collection and Processing Layer**: Treat this system like a product, starting with an MVP. 3. **Establish Data Quality and Trust**: Create joint QA processes with data teams and transparent dashboards. 4. **Operationalize Insights**: Turn data review into a routine habit, course-correcting plans based on insights. ## Case Study: How Trendyol’s Tech Resolution Team Conquered Recurring Password Issues To bridge this gap, we rely on a practical 4-step framework — Define, Design, Control, and Operationalize — and let's break down exactly how it works in action through a real-world case study from Trendyol's Tech Resolution team. ### Mapping the Case Study to the 4-Step Model: 1. **Define What to Measure**: We measured password reset tickets, system issues, and Mean Time to Resolve. 2. **Design the Data Collection and Processing Layer**: We leveraged internal engineering power and automated ticket routing using Raven AI. 3. **Establish Data Quality and Trust**: We performed weekly audits to verify AI categorizations against our Slack Issue Dataset. 4. **Operationalize Insights**: We set an ambitious target: Reduce manual password ticket volume by 60% in Q2. ### The Result (Actual Metrics): By combining Raven’s AI automation with root-cause UX improvements, we achieved a 65% actual reduction in manual password tickets within the first quarter. **Conclusion**: Bridging the gap between strategy and operations is not merely a technical challenge; it is a cultural transformation. Data does not exist simply for reporting; it exists to eliminate repetitive work, build better products, and create a real, tangible impact on your organization.