Hello, I can discuss our experience at Wisemonk about how we divided analytics. We identified a distinct boundary concerning what prices customers are ready to accept. We found that customers consider "Operational Analytics" (information on their previous behavior) a fundamental right, while they see "Market Intelligence" (data concerning the external environment) as a luxury resource. At first, we thought about restricting fundamental reports such as "Time to Hire" or "Candidate Pipeline Velocity." We soon understood that restricting access to a customer's own usage data causes frustration and dissatisfaction. It seems as if we are keeping their history imprisoned. We constructed our premium tier based on decision-support information. We compiled anonymized salary benchmarks, regional talent availability heatmaps, and cost-of-living forecasts. The validation procedure was simple. We examined our manual support requests. Clients frequently reached out via email with questions like, "What budget should I allocate for a Senior Engineer in Poland?" or "Is the market in Bangalore experiencing a slowdown?" That signaled the event. We transformed those responses into a "Global Talent Insights" dashboard and secured it. The takeaway for us was straightforward. If the chart indicates what they have already accomplished, it is a feature. If the chart aids them in conserving budget for their next choice, it is a product. Best, Aditya Nagpal Founder & CEO, Wisemonk
We rebuilt our analytics after seeing teams export data just to understand how drawings moved through a project. Our multi-tenant dashboards had grown slow, and every drilldown hit the same performance wall. The surprise was that the real bottleneck wasn't the charts, it was how we cached fragmented queries across tenants. Once we shifted to a shared metrics layer and precomputed common segments, load times dropped and we stopped maintaining one-off chart code. The lesson for us was simple. Analytics only scales when the data model does.
Our custom chart code at ShipTheDeal completely broke. As more users needed drilldowns, everything got slow and fragile. We pulled out the common queries and cached the repeat segments. Suddenly, things were fast. My advice? Separate the shared metrics from the custom views early. It makes maintenance so much easier later on.
Teachers at Tutorbase wanted better reporting. They needed to see data by class, teacher, or location, not just basic stats. What surprised us was how our custom charts became a maintenance nightmare as we grew. Caching got messy and users started complaining about delays. We switched to a standard charting framework and cut back on custom tweaks. That fixed both the performance and maintenance problems.
Last year, our SaaS platform started facing difficulties with the analytics layer. Our multi-tenant dashboard depended heavily on complex drilldowns and segmentation, but managing and maintaining the custom charting code has become complicated and burdensome. During the peak hours, performance began to drop significantly, which negatively impacted our users. As a result, users started reporting about the slow dashboard. That's the moment we realized that the time had arrived when we had to redesign and rebuild our entire setup. We chose to rebuild the entire analytics layer from scratch, putting emphasis on improved and advanced smarter caching, pre-aggregated datasets, and reusable chart components. While rebuilding the analytics layer, we got the biggest surprise. Most of the logs stopped rendering, but the inconsistent queries were coming from the various tenants. Bringing all those queries into a consistent structure gave us the largest performance gains. My Advice: Always think of analytics as its own product, not just another feature. When you rebuild it with scalability at its core, it's obvious that there will be noticeable improvement to both your team and your customers.