Realtime KPIs: Building Pacelab’s Analytics Layer

Key performance indicators (KPIs) are the compass of any organisation. They reveal whether a company is on track to meet its goals, highlight areas of concern and enable proactive management. Traditionally, KPIs are calculated periodically— monthly or quarterly—leaving executives to make decisions based on outdated information. As data volumes grow and competition intensifies, the need for realtime KPIs becomes pressing. Pacelab, a fictitious but illustrative software company, embarked on a journey to build an analytics layer that delivers realtime KPIs across its operations. This article recounts how Pacelab implemented its analytics layer, the challenges encountered and the impact on decision making.

Pacelab provides software solutions for manufacturing businesses, including scheduling, inventory management and production analytics. Its clients expect efficiency and reliability. Pacelab’s internal teams—engineering, sales, customer success, finance—operate in a dynamic environment. Prior to the analytics project, teams relied on spreadsheets and monthly reports compiled from disparate systems. This lag hindered agility. For instance, if engineering fell behind schedule, it wasn’t detected until a weekly meeting. Customer success managers discovered churn risk only after a client cancelled subscriptions. Pacelab’s leadership sought a unified, realtime view of key metrics to improve responsiveness and foster data‑driven culture.

Requirements and Design

The first step was defining requirements. Stakeholder interviews identified the KPIs that mattered most: system uptime, deployment frequency, support ticket resolution time, trial conversion rates, monthly recurring revenue (MRR), churn, customer satisfaction and feature adoption. Each KPI needed to be updated as close to realtime as possible—ranging from every minute for uptime to hourly for support metrics. The analytics layer had to integrate data from multiple sources: application logs, version control, ticketing systems, CRM, billing platforms and product telemetry. It also had to provide role‑based dashboards—executives needed a high‑level overview, while team leads required granular details. Security and compliance were paramount, given the sensitivity of customer data.

The design approach centred on a modern data stack. Data ingestion would occur through event streaming and batch processes, feeding a central data warehouse. A realtime processing layer would compute KPIs and deliver them to a visualisation tool. The stack needed to scale elastically, handle schema changes gracefully and support complex transformations. Pacelab chose open source technologies complemented by commercial tools for reliability. The overall architecture consisted of four layers: ingestion, storage, processing and presentation.

Ingestion and Storage

For data ingestion, Pacelab deployed Apache Kafka to handle realtime streams. Application services published events such as errors, deployments and user actions to Kafka topics. Connectors ingested data from external systems—Jira (tickets), Salesforce (CRM), Stripe (billing), GitHub (commits) and Intercom (customer chats). Batch ingestion for historical data used scheduled Extract‑Transform‑Load (ETL) jobs. The data warehouse selected was Snowflake, chosen for its separation of compute and storage, ability to handle semi‑structured data and support for time travel queries. Kafka streams were mirrored into Snowflake using a connector, ensuring that the warehouse had both realtime and historical data.

Data governance was a key consideration. Pacelab implemented a schema registry for Kafka topics, ensuring that event structures were documented and versioned. This prevented downstream processing failures when schemas evolved. Role‑based access control on Snowflake restricted sensitive data to authorised personnel. Data was encrypted at rest and in transit. Logging and auditing tracked who accessed which data sets. These measures built trust in the analytics platform and ensured compliance with data protection regulations.

Processing and KPIs

With data flowing into the warehouse, the next step was computing KPIs. For realtime metrics, Pacelab used Apache Flink—a streaming data processing framework. Flink jobs consumed events from Kafka and performed aggregations within sliding windows. For example, system uptime was calculated by monitoring heartbeat events from microservices. If a service failed to send a heartbeat within a threshold, an alert triggered. Deployment frequency was derived from continuous integration events. These metrics were computed every minute and stored in a dedicated table. Other KPIs, like churn or trial conversions, were updated hourly via batch transformations executed by dbt (Data Build Tool). dbt models defined how raw data was transformed into metrics, using version control for reproducibility.

Pacelab emphasised consistency in metric definitions. A metrics catalog documented each KPI—its calculation, data sources and update frequency. This avoided discrepancies between teams. To manage computational costs, the company used materialised views for frequently accessed metrics, updating them incrementally. Alerts were configured in the processing layer; thresholds triggered notifications via Slack and email when values exceeded limits. For example, if churn exceeded a target, the customer success team was alerted immediately.

Presentation and Visualisation

On the presentation layer, Pacelab selected a business intelligence (BI) tool—Looker—to build dashboards. Looker connected to Snowflake and Flink’s output tables, providing realtime access to computed KPIs. Dashboards were customised for each role. Executives saw high‑level views of revenue, churn and uptime, with drill‑downs available. Engineering dashboards highlighted error rates, deployment success and feature usage. Customer success dashboards displayed active trials, support ticket response times and satisfaction scores. The dashboards included filters, time selectors and annotations. Colour‑coded indicators signalled performance against targets. Users could subscribe to email digests with key metrics and receive Slack alerts for critical thresholds.

Pacelab also integrated KPIs into operational workflows. For instance, service uptime metrics fed into a SRE (Site Reliability Engineering) runbook, triggering incident response. Sales dashboards linked to CRM workflows, allowing representatives to follow up with high‑value prospects immediately. Customer success dashboards integrated with support ticketing, enabling agents to prioritise at‑risk clients. By embedding analytics into everyday tools, the company ensured that data informed action rather than sitting idle.

Challenges and Lessons Learned

Building a realtime analytics layer presented challenges. Data silos were the first hurdle; legacy systems lacked APIs or standard formats. Extracting and normalising data required significant engineering effort. Data quality issues emerged, such as missing timestamps or inconsistent identifiers. Pacelab established a data quality team responsible for monitoring ingestion pipelines and raising issues. Another challenge was latency. Streaming jobs needed to process events quickly without overwhelming resources. Tuning Flink parallelism and controlling message sizes in Kafka were crucial. Ensuring that dashboards remained responsive despite frequent updates required careful caching and load management.

Cultural change was equally important. Teams accustomed to monthly reports had to adjust to realtime feedback. At first, some employees felt overwhelmed by constant notifications. Pacelab responded by setting sensible alert thresholds and encouraging teams to focus on trends rather than every fluctuation. Training sessions helped employees interpret dashboards and integrate them into planning. Cross‑departmental workshops fostered a data‑driven mindset. The result was greater collaboration and accountability. Teams saw how their actions affected KPIs and worked together to improve outcomes.

Impact on Decision Making

The realtime analytics layer transformed Pacelab’s decision‑making. Engineering teams detected performance degradations within minutes, fixing issues before customers noticed. Deployment frequency increased as confidence grew in monitoring and rollback capabilities. Customer success managers intervened proactively when usage metrics signalled a drop in engagement, reducing churn. Sales leaders adjusted strategies based on realtime trial conversions, reallocating resources to high‑performing channels. Executives made strategic decisions with up‑to‑date revenue and margin data, enabling timely investments. The company also discovered new insights, such as correlations between product usage and support tickets, leading to targeted feature improvements.

Beyond internal benefits, Pacelab’s clients benefited from enhanced reliability and faster response times. The company shared certain KPIs with clients, such as system uptime and ticket resolution times, building trust. The realtime analytics capability became a selling point, demonstrating Pacelab’s commitment to transparency and performance.

Conclusion

Building a realtime analytics layer allowed Pacelab to transform how it measures and manages performance. By integrating data from diverse sources, processing it in realtime and presenting it in role‑based dashboards, the company enabled proactive decision‑making across departments. The journey involved overcoming technical and cultural challenges, but the payoff was substantial—improved responsiveness, higher customer satisfaction, and more informed strategic planning. As businesses increasingly compete on data, realtime KPIs are becoming essential. The Pacelab case illustrates that with the right architecture, governance and mindset, organisations can harness the power of realtime analytics to achieve operational excellence and customer delight.