metrics

4 posts

kakao

How the POPM Course Became a (opens in new tab)

Kakao developed its internal POPM (Product Owner/Product Manager) training program by treating the curriculum itself as an evolving product rather than a static lecture series. By applying agile methodologies such as data-driven prioritization and iterative versioning, the program successfully moved from a generic pilot to a structured framework that aligns teams through a shared language of problem-solving. This approach demonstrates that internal capability building is most effective when managed with the same rigor and experimentation used in software development. ## Strategic Motivation for POPM Training * Addressed the inherent ambiguity of the PO/PM role, where non-visible tasks often make it difficult for practitioners to define their own growth or impact. * Sought to resolve the disconnect between strategic problem definition (PO) and tactical execution (PM) within Kakao’s teams. * Prioritized the creation of a "common language" to allow cross-functional team members to define problems, analyze metrics, and design experiments under a unified structure. ## Iterative Design and Versioning * The program transitioned through multiple "versions," starting with an 8-session pilot that covered the entire lifecycle from bottleneck exploration to execution review. * Based on participant feedback regarding high fatigue and low efficiency in long presentations, the curriculum was condensed into 5 core modules: Strategy, Metrics, Experiment, Design, and Execution. * The instructional design shifted from "delivering information" to "designing a rhythm," utilizing a "one slide, one question, one example" rule to maintain engagement. ## Data-Driven Program Refinement * Applied a "Product Metaphor" to education by calculating "Opportunity Scores" using a matrix of Importance vs. Satisfaction for each session. * Identified "Data/Metrics" as the highest priority for redesign because it scored high in importance but low in satisfaction, indicating a structural gap in the teaching method. * Refined the "features" of the training by redesigning worksheets to focus on execution routines and converting mandatory practice tasks into selective, flexible modules. ## Structural Insights for Organizational Growth * Focused on accumulating "structure" rather than just training individuals, ensuring that even as participants change, the framework for defining problems remains consistent within the organization. * Designed practice sessions to function as "thinking structures" rather than "answer-seeking" exercises, encouraging teams to bring their training insights directly into actual team meetings. * Prioritized scalability and simplicity in the curriculum to ensure the structure can be adopted across different departments with varying product needs. To build effective internal capabilities, organizations should treat training as a product that requires constant maintenance and versioning. Instead of focusing on one-off lectures, leaders should design structural "rhythms" and feedback loops that allow the curriculum to evolve based on the actual pain points of the practitioners.

datadog

Cheering on coworkers: Building culture with Datadog dashboards | Datadog (opens in new tab)

Datadog engineers developed a real-time tracking dashboard to monitor a colleague’s progress during an 850km, six-day ultra-marathon challenge. By scraping public race statistics and piping the data into their monitoring platform, the team created a centralized visualization tool to provide remote support and office-wide engagement. ### Data Extraction and Parsing The team needed to harvest race data that was only available as plain HTML on the event’s official website. * A crawler was built using the Python `Requests` library to automate the retrieval of the webpage's source code. * The team utilized `BeautifulSoup` to parse the HTML and isolate specific data points, such as the runner's current ranking and total distance covered. ### Ingesting Metrics with StatsD Once the data was structured, it was converted into telemetry using the Datadog agent and the `statsd` Python library. * The script utilized `dog.gauge` to emit three primary metrics: `runner.distance`, `runner.ranking`, and `runner.elapsed_time`. * Each metric was assigned a "name" tag corresponding to the runner, allowing the team to filter data and compare participants within the Datadog interface. * The data was updated periodically to ensure the dashboard reflected the most current race standings. ### Dashboard Visualization and Results The final phase involved synthesizing the metrics into a high-visibility dashboard displayed in the company’s New York and Paris offices. * The dashboard combined technical performance graphs with multimedia elements, including live video feeds and GIFs, to create an interactive cheering station. * The system successfully tracked the athlete's 47km lead in real-time, providing the team with immediate updates on his physical progress and elapsed time over the 144-hour event. This project demonstrates how standard observability tools can be repurposed for creative "life-graphing" applications. By combining simple web scraping with metric ingestion, engineers can quickly build custom monitoring solutions for any public data source.