The LMMS platform is a specialised logistics application which supports the targeted, trackable distribution of disaster relief from warehouse to recipient in environments with challenging infrastructure challenges.
LMMS customers increasingly need better visibility into their data to spot trends and govern the allocation of their resources.
LMMS engaged Suru Partners to develop a strategy, architecture and technology suite to add business intelligence and data visualisation to their platform. They knew they needed a new approach for their next era of product development but were unsure about how to go about it. Suru helped them critically assess their customers’ needs and product strategy, analysed the options available to them and guided them through the project.
Designing data visualisations
Designing project dashboards
It would be natural to tackle this project with traditional business intelligence (BI) tools products like PowerBI, Tableau or Chartio which is what LMMS expected we'd recommend.
However, as with all our projects, Suru conducted a customer-oriented research and planning phase which challenged LMMS's assumptions and highlighted the needs of the users, not the features of the technology. This process uncovered two critical insights:
First, the level of customisation and integration required to make the user experience good enough for a commercial product simply wasn't possible with traditional BI platforms in today’s SaaS landscape. Second, an off-the-shelf BI platform wasn't going to provide the long-term modularity that LMMS' product roadmap required.
So, Suru recommended an alternative approach using open source technologies, containerisation for robust deployments and a modular architecture. This gave LMMS the flexibility to fine-tune how the system works for end users and created a best-in-class experience.
LMMS had some initial design ideas for what visualisations they thought would be useful to their customers. Suru critiqued and improved those designs by validating them against real customer data. In many places we developed better ways to visually communicate so the data told the right story and helped end users mostly executive leadership understand the complexity faced by front-line staff and glean insights from their data.
Suru also worked with LMMS to approach these new data visualisation capabilities in an agile way, breaking up their long term vision into smaller pieces which could be independently released. This approach, along with Suru's modular system architecture, allowed LMMS to release some features to key customers early on and then test demand and usage patterns before progressing to the other areas, guiding their investment in the software by real customer feedback in an ongoing fashion.
Designing & coding distribution visualisations
With numerous on-premise instances of the LMMS product deployed in countries around the world, rapid iteration is a core challenge. Suru's proposed architecture involved cloud-deployed components tightly integrated into the LMMS experience to allow them to update to the platform in days rather than months, but to do that the application’s infrastructure needed to be centralised.
LMMS couldn't simply read customer’s data from the in-country on-premise instances doing so would break data privacy laws, not to mention adding security and operational uptime risks so Suru designed the system to have in-country components that strip PII and other information, pre-aggregate at a low level and then transfer entirely anonymous data to the central data visualisation system.
This gave LMMS the best of both worlds: Suru’s architecture meant they could rapidly deploy updates to the platform without asking customers to make a change and never had to navigate the legal and ethical issues associated with personal data. Updates to the platform can now be deployed to 100% of LMMS customers in a matter of minutes rather than months.
Suru's architecture also increased the velocity of future development. The system's various logical components are cleanly separated so they can be developed, swapped out and scaled up or down entirely independently from each other.
The build, test & deployment pipeline
Average time to deploy features to all customers
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