Turning Data into Decisions (Not Just Reports)
A report is data organised for reading. A decision is data organised for action. Most businesses produce the former and wonder why the latter does not happen more often or more quickly.
Manthan Sharma
Author
The difference between a data-driven business and a data-aware business is not the volume of data collected or the sophistication of the analytics tools used. It is whether the data reaches the decision-maker at the moment of decision, in the format that enables the decision, with the context that makes the correct action obvious. Most businesses are data-aware: they collect the data, produce the reports, and review the summaries. The data exists. The reports are read. And then the meeting ends and nobody is sure what to do differently based on what was presented. The transition from data-aware to data-driven requires redesigning the data delivery system around decisions rather than around information asking not 'what data do we have?' but 'what data does this specific decision-maker need, in what format, at what time, to make this specific decision reliably?'
The Decision-First Data Design Principle
Decision-first data design starts with the decision and works backward to the data. The marketing spend decision requires: current CAC by channel versus the profitable threshold (the gap between them), the 7-day trend in each channel's CAC (the direction), and the current cash position relative to the next week's planned spend (the constraint). These three data points, delivered together at the time the decision is being made, produce a decision in under two minutes. The same marketing spend decision made from a comprehensive campaign performance report showing 40 metrics requires the decision-maker to identify the relevant metrics from among the irrelevant ones, calculate the CAC from the raw data, assess the trend from a series of daily figures, and retrieve the cash position from a separate financial report. The comprehensive report contains the same information but buried in the format for information consumption rather than the format for decision making.The practical design principle: for each of the five to seven decisions that the founder makes most frequently and with the most consequence, design a specific data view that contains exactly the information required for that decision, in the format that makes the correct action obvious, updated at the frequency the decision needs to be made. This is not a comprehensive dashboard. It is a decision-specific view potentially one page, potentially three numbers and a threshold comparison that enables the decision without requiring interpretation.
The Four Data Formats That Enable Decisions
The threshold comparison: showing the current value of a metric against the threshold at which action is required CAC of ₹840 versus profitable threshold of ₹780, shown as a bar that is 7.7% above threshold in red. The action is obvious: pause or restructure. The trend line: showing the direction of a metric over a relevant window NDR rate in Maharashtra, 7-day rolling average, shown as a line that has been rising for four days. The action question is 'is this structural?' and the data shows the relevant context. The gap analysis: showing the difference between the current state and the target state inventory cover of 9 days versus reorder threshold of 14 days. The action is obvious: initiate reorder today. The scenario comparison: showing two options with their projected outcomes 'scale campaign at current CAC (₹840) produces estimated 380 new customers next week at a ₹319,200 spend; hold campaign at reduced spend until CAC returns to threshold produces 220 new customers at a ₹171,600 spend.' The decision-maker chooses; the data has framed the choice.Each of these formats is designed around a specific decision type. None of them requires the recipient to process information before understanding the decision they face. The transition from producing reports to producing decision-enablers is the most impactful data infrastructure improvement most growing businesses can make and it requires very little technology change. It requires a redesign of how the existing data is formatted and delivered.
Related articles
View all →
Contribution MarginUnderstanding Contribution Margin (Not Just Profit)
Profit is what remains after all costs. Contribution margin is what remains after variable costs and it is the metric that determines whether adding one more order, one more channel, or one more marketing rupee makes the business better or worse. Most founders optimise for profit. The best founders optimise for contribution margin first.
Seasonal InventoryInventory Planning for Seasonal Demand
Seasonal demand is predictable. Seasonal inventory problems are not inevitable they are the result of predictable demand being met with reactive planning. The brand that plans its Diwali inventory in October is already too late. The brand that planned it in August has the right stock, in the right channels, at the right cost.
Systems DesignBuilding Systems That Scale With You
Most founders build systems for the current problem. The right systems are built for three problems ahead designed to handle 5x the current volume with configuration changes rather than architectural rebuilds. The difference between a system that scales and one that requires replacement is in the design decisions made when the current volume is modest.