Introduction
A leading provider in the talent acquisition industry sought to enhance its reporting capabilities to empower users with self-service analytics and prepare for future AI-driven advancements. To achieve this, the client partnered with Data Driven to implement Looker for advanced analytics and reporting, delivering actionable insights directly within their platform.
This case study showcases how a collaborative approach and strategic use of their data transformed the client’s analytics into a scalable ecosystem, enabling self-service insights and paving the way for future growth.
Objectives
The primary goals of the project were:
Embedding dashboards to provide customers with self-service access and monetize data effectively.
Building a scalable infrastructure capable of supporting advanced analytics and future growth.
Applying query optimization best practices to significantly improve performance and speed.
Challenges
Our client faced several challenges in its analytics ecosystem:
Redundant data models created inefficiencies and made ongoing maintenance more complex.
Users lacked the ability to independently access or analyze data.
Inefficient table structures and indexing caused slow query performance.
Complex scenarios required ensuring accurate reporting without errors or inconsistencies.
Approach
Our work to transform the client’s analytics ecosystem started with a clear, phased plan tailored to tackle their specific challenges head-on. From the outset, we focused on understanding the client’s needs and aligning them with actionable, scalable solutions.
The project began with planning and strategy, during which we held detailed requirements-gathering sessions with stakeholders to identify critical KPIs and use cases. These sessions not only clarified priorities but also highlighted areas for performance improvement. A key focus was on optimizing query performance, particularly for commonly queried columns like candidate statuses. By revisiting clustering keys and evaluating table structures, we laid the groundwork for a faster, more efficient analytics experience. Additionally, we provided expert guidance on technical setup, including permissions and security configurations, ensuring the infrastructure was both robust and secure. This phase culminated in the delivery of a comprehensive requirements workbook, mapping out data flows, technical specifications, and the project’s overall scope.
Next, we moved into embed training and development, where our team worked closely with the client’s developers to embed Looker dashboards seamlessly within their application. Using the Embed SDK, we hosted co-development sessions that empowered the client’s technical team to generate embed URLs and integrate dashboards via iframes. This collaborative approach ensured that the embedded analytics were not just functional but also intuitive for end users. The result was a fully operational set of dashboards that transformed the user experience by providing easy, self-service access to actionable insights.
With embedding underway, we turned our attention to LookML modeling and optimization. The existing data models were rife with redundancies, so we consolidated overlapping Explores into a unified, DRY (Don’t Repeat Yourself) LookML model. By designing a new Explore tailored to key insights, we created comprehensive views, dimensions, and measures that streamlined reporting. Clustering keys played a pivotal role in this optimization process, reducing query runtime by a staggering 83%. For instance, queries related to candidate statuses that previously caused delays now refreshed almost instantly, significantly enhancing performance and user satisfaction.
One of the most challenging aspects of the project involved accurately reporting metrics for candidate transfers between jobs. These transfers introduced complexities that could distort key metrics if not handled properly. To address this, we created derived tables that ensured data integrity and maintained accurate relationships between metrics. Additionally, we developed custom measures and filters to eliminate anomalies, such as negative time-to-review values caused by misaligned timestamps. This meticulous approach delivered a reliable, scalable solution for tracking candidate transfers without compromising the accuracy of other reports.
Finally, we prioritized training and knowledge transfer to equip the client’s team for long-term success. Through comprehensive training sessions, we covered LookML modeling, dashboard development, and best practices for maintaining and optimizing reports. By the end of the engagement, the client’s developers and business users were empowered to independently manage their analytics, ensuring scalability and sustainability for the future.
Through this phased, collaborative approach, we not only addressed the client’s immediate needs but also laid a strong foundation for future growth and innovation.
Our Solution
The partnership between Data Driven and our client delivered the following outcomes:
Optimized queries reduced runtime by over 80%, enhancing overall system responsiveness.
Configured DEV, STAGING, and PRODUCTION environments to enable smooth migrations and reliable operations.
Embedded dashboards provided customers with direct access to actionable insights.
Optimized data models and infrastructure were designed to support AI-driven analytics.
This enhanced analytics ecosystem empowers the client to streamline reporting, deliver real-time insights, and prepare for advanced AI capabilities, paving the way for sustained growth and innovation.
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