For Data & Analytics Teams
You cannot hire your way out of a context problem.
Your warehouse is solid. Your metrics are defined. Your team is still buried in ad-hoc requests because stakeholders have data access without understanding what drives what. The instinct is to hire another analyst. But the bottleneck is not capacity. It is that nobody outside your team can see how the business works as a system. KPI Tree sits on top of your existing stack and gives them the causal model they are missing. For a fraction of what one analyst costs, you get back the strategic bandwidth you have been trying to recruit your way out of.
Three problems that hiring another analyst will not solve.
Data leaders know these frustrations intimately. They are not tool problems and they are not staffing problems. They are architecture problems. Your stack has a missing layer between "metric is defined" and "stakeholder understands what it means."
Your backlog grows faster than your headcount.
Every analyst you hire creates capacity that gets consumed within weeks. The requests keep coming because the underlying problem never changed: stakeholders cannot see how metrics connect, so they ask your team for context that no dashboard was designed to provide. You are not short-staffed. You are absorbing a structural gap in your stack.
Your best analysts are doing your most repetitive work.
You hired senior data professionals to build data products, improve data quality, and do the analytical work that moves the business. Instead, they spend most of their week pulling numbers and adding context that should be self-evident. That is not a team problem. It is an architecture problem. There is a missing layer between "metric is defined" and "stakeholder understands what it means."
Your stakeholders have access. They still ask you.
Self-service was supposed to fix this. You invested in the BI tool, the documentation, the training. Adoption plateaued. The reason is not laziness. It is that a metric without context is a number without meaning. People ask your team because they trust your interpretation, not their own. What they need is not more access. It is a mental model of how the business works.
Not a dashboard. A causal model of your business.
KPI Tree does not visualise your data in a new way. It models how your metrics drive each other. Revenue connects to conversion rate, which connects to page load speed, which connects to engineering velocity. When someone asks "why did revenue drop?", they trace the answer themselves. Over time, they stop asking because they have built the intuition that dashboards were never designed to create.
- Metric trees show cause and effect, not just numbers in a grid
- Business users navigate the model without writing a single query
- Every metric links back to its source definition in your semantic layer
- Stakeholders build real intuition instead of depending on your team for every question
Your stack stays. We add the missing layer.
KPI Tree does not replace your warehouse, your dbt models, or your semantic layer. It reads them. Your metric definitions stay where they are. We add the one thing your stack was never designed to hold: the causal relationships between metrics and the humans responsible for them. Think of it as the context layer that makes your entire existing investment finally land with stakeholders.
- Native integrations with Snowflake, BigQuery, dbt, and major semantic layers
- Metric definitions sync automatically from your existing sources
- No data duplication, no new ETL pipelines, no new data models to maintain
- Your governance and access controls carry through unchanged
From service desk to strategic function.
When stakeholders can see the full metric picture and trace relationships themselves, the nature of their requests changes. They stop asking "what is this number?" and start asking "what should we do about it?" That shift changes your team's role permanently. You stop being the people who respond to ad-hoc requests. You become the people who built the system everyone relies on to think clearly about the business.
- Reduce ad-hoc requests by making the full metric picture visible and navigable
- Metric ownership routes questions to the right person, not always to your team
- Self-service that actually works because users have context, not just access
- Free your team to focus on quality, modelling, and the analytical work you hired them for
Start alongside your BI tool. Let the results speak.
KPI Tree is not a replacement for your BI tool. Your BI tool handles exploration, ad-hoc queries, and the data that has not yet been defined as a metric. KPI Tree handles the structured layer: the defined metrics, the causal relationships between them, the ownership, and the actions that close the loop. Start with one team. Expand as the results compound.
Your data stack has every layer except the one that creates understanding.
Your warehouse stores data reliably. Your semantic layer defines metrics consistently. Your BI tool visualises them clearly. None of these tools were designed to show how metrics drive each other, who is responsible for them, or whether the right actions are happening. KPI Tree adds the missing layer: causal structure, named ownership, and a closed loop from metric movement to verified human action. For less than the cost of one analyst.
Stack-native, not stack-replacing
We sit on top of your warehouse and semantic layer. Your metric definitions, your governance, your access controls stay exactly where they are. We add the causal relationships and human context your stack was never designed to hold. Nothing moves. Everything connects.
Causal structure, not just clean definitions
Your semantic layer defines metrics consistently. Your BI tool visualises them clearly. Neither shows how metrics drive each other. That structural understanding is the difference between a stakeholder who asks "what happened?" and one who can trace the answer to "why?"
Ownership that closes the loop
Every metric has an owner. When something moves, the right person is notified with context. Actions are tracked against the metric they were meant to move. Impact is verified. Your data team stops being the middleman between a metric and the person responsible for it.
Common questions
- Your BI tool handles exploration, ad-hoc queries, and the data that has not yet been formalised as a metric. KPI Tree handles the structured layer: defined metrics, their causal relationships, ownership, and action tracking. Both connect to the same warehouses and semantic layers. Most teams run both permanently. KPI Tree reduces the volume of ad-hoc requests by giving stakeholders self-sufficient access to the metrics that are already defined. Your BI tool stays for the work that requires genuine analytical skill.
- That is exactly what your BI tool is for. KPI Tree is not designed to replace exploratory analysis. It handles the structured metric layer: the KPIs your team has already defined, how they connect to each other, who owns them, and what actions are being taken. By covering the structured layer, KPI Tree frees your analysts to spend more time on the exploratory work that only they can do.
- KPI Tree starts at £1,000 per month. A mid-level data analyst costs £45,000 to £65,000 per year in salary alone, before tools, management overhead, and recruiting costs. Most data leaders tell us KPI Tree eliminates enough ad-hoc reporting to recover the equivalent of one to two full-time analysts in capacity. The maths is straightforward.
- Your semantic layer defines metrics consistently. That is essential infrastructure. But consistent definitions do not show cause and effect. They do not tell a stakeholder what drives conversion rate or why churn matters to revenue. KPI Tree inherits your semantic layer definitions and adds the causal relationships and ownership that turn clean definitions into genuine understanding.
- Most data teams have their first metric tree live within a day. Connect your data source, map your key metrics, define the relationships between them. The initial tree does not need to be perfect. Start with your core revenue model and expand as stakeholders engage with it.
- This is the right question. Dashboards go unused because they show numbers without context. That is a behavioural science problem, not a technology problem. People engage with information when it is personally relevant and structurally clear. A metric tree gives both: you see your metrics, you see how they connect to the whole business, and you see what you are supposed to do about them. That is why adoption sticks where dashboards fail.
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Give your team back the week they lost to ad-hoc requests.
Your stack is solid. Your team is talented. The missing piece is a shared model that turns all of that investment into understanding. See how KPI Tree works with your existing data stack.
