Powering self-service analytics and end-to-end monitoring at BlueCargo
Looking forward
Besides using Rill’s power for exploratory analysis and end-to-end monitoring of their shipment lifecycles, BlueCargo has also been looking to use Rill to address other potential questions:
- Understanding how many invoices they have received from their customers
- Funnel analysis (input / output)
- Statistical modeling to quantify and determine how difficult it is to return a container
- Providing data-driven decision making when disputing fees
Additionally, there are multiple other brand new projects involving analytics that the BlueCargo team will be looking to take on in the next year. As Timothee likes to joke, his goal is to “get to 100% on all these metrics and automate his own job away using Rill”. We look forward to the continued collaboration with the BlueCargo team and can’t wait to see what they accomplish!
BlueCargo is a Detention and Demurrage mitigation platform provider that enables transportation and logistics companies to streamline and optimize their container supply chain management process. By centralizing their data on a single platform, they enable customers to track shipments (through the entire lifecycle), follow the status of individual containers, track gate schedules, secure appointments to pick up or drop off containers, manage demurrage / detention / per diem fees across ports, and much more!
BlueCargo works with over 300 customers, tracks well over 15 million container events, and has saved customers over $175 million dollars in per diem fees. To meet the growing needs of their customers while remaining an agile startup, BlueCargo needed a tool that could:
- Enable their analysts to perform interactive self-service analytics and drill into their data quickly to ensure responsiveness and an ability to rapidly turn around on end user queries
- Perform complex modeling to ensure proper coverage, completeness, and timeliness of the monitored container transactions
- Remain cost-effective while scaling with the needs of their business
Results
Technical value
Performance at scale and cost-effectiveness
Operational value
Enabling dynamic and interactive analytics at a deeper level
Customer value
End-to-end monitoring and alerting / notification
Performance at scale and cost-effectiveness
Prior to Rill, PMs within BlueCargo were leveraging a different self-service BI tool that provided a visual query builder and interactive GUI. Unfortunately, for the data analysts, queries were quite slow and insufficient for their data exploration needs. In their case, actions taken in this other BI tool, such as adding filters or changing dimensions / group-bys would trigger new queries to be generated and pushed down, which would then have to be re-executed. Having to constantly wait for queries to return, which could often take time to complete, proved inefficient and not practical given the requirements of their day-to-day responsibilities.
Besides needing to go faster, the BlueCargo team had looked into a few other solutions to allow their data analysts to slice & dice and perform interactive analysis but those other tools proved either costly or required significant set-up. Rill simplified that process in a cost-effective manner, allowing their teams to get set up and deployed quickly.
With Rill, data analysts point to existing S3 data and ingest the data into Rill, which they then use to quickly perform modeling and set up ready-to-use dashboards. Rather than regularly waiting for queries to return, these same actions were now effectively instantaneous and data analysts could now effectively perform their exploratory analysis at the speed of thought. Performance remains a key differentiator to why the BlueCargo team continues to use Rill today.
Enabling dynamic and interactive analytics at a deeper level
In the early days, BlueCargo’s data team had insufficient means to analyze and explore their data in a highly interactive manner. In particular, for data analysts, they needed an extensive grasp over the shipment tracking system, how it worked, understand various moving parts, track new features, and work with many data sources. Additionally, data analysts had to have a deep understanding of metric definitions, what they meant, why specific metrics could go up or down, and where to look when unexpected changes occurred. To provide a concrete example, a KPI going up could indicate that there was either a system error, an user error, or simply be related to a broader industry trend. It would be up to the analyst to determine the why. Before Rill, this would often require herculean effort as it can be a difficult and time-consuming process.
After adopting Rill:
- Data analysts had a centralized tool for both exploratory analysis and day-to-day reporting. Other self-service BI tools simply weren’t good enough given query execution time. With Rill, self-service analytics and weekly reports could be put together for management in under 10 minutes, saving potential hours of weekly effort.
- Data analysts were now able to effectively engage with and discover insights about their data in a real-time manner. When it comes to tracking the lifecycle of a shipment, this would often consist of 60+ data points and 70+ sources over the span of 2 months. Analyzing why the average terminal coverage drops for example would require the analyst to deep dive across a multitude of dimensions and metrics - Port, Terminals, Success Rate, Terminal Coverage, and Refresh Rate. Before Rill, this type of analysis was simply not possible, especially with the granularity and fidelity needed, but was now something that could easily be analyzed at log-level detail by analysts within seconds.
End-to-end monitoring and alerting / notification
One of the key use cases that Rill powers is enabling the BlueCargo team to study the shipment lifecycle for containers from A-Z, which can contain many events and data points throughout the entire lifecycle. This process entails working with many different data sources, which their data team has to retrieve, normalize, summarize, and explain to the end user.
For this use case, analysts have to be able to quickly and effectively answer:
- Coverage - For a given container number, how good is their coverage for this individual container?
- Completeness - For a given container or shipment lifecycle, are they getting all the data points that they need and how complete is the data?
- Timeliness - How fast are they getting data for a particular container?
- Performance - How efficient is this actual process?
For BlueCargo, accuracy and the ability to have end-to-end monitoring for shipment lifecycles are simply a must. Through Rill, this end-to-end visibility was now achievable and analysts could effectively flag or internally alert on gaps / issues as required.
Looking forward
Besides using Rill’s power for exploratory analysis and end-to-end monitoring of their shipment lifecycles, BlueCargo has also been looking to use Rill to address other potential questions:
- Understanding how many invoices they have received from their customers
- Funnel analysis (input / output)
- Statistical modeling to quantify and determine how difficult it is to return a container
- Providing data-driven decision making when disputing fees
Additionally, there are multiple other brand new projects involving analytics that the BlueCargo team will be looking to take on in the next year. As Timothee likes to joke, his goal is to “get to 100% on all these metrics and automate his own job away using Rill”. We look forward to the continued collaboration with the BlueCargo team and can’t wait to see what they accomplish!
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