Data quality depends not only on having clean and tidy records, but also on knowing how to interpret them, and this is where KPIs come into play.
Building effective KPIs is similar to climbing a mountain. It’s not just about getting to the top, but doing it step by step, building on each step and making sure that every decision we make is aligned with the overall business strategy. Just as a climber does not ignore warnings of bad weather or unstable terrain, a data analyst cannot ignore the early warnings that shape key performance indicators.
Data Quality: Beyond the Technical
One of the biggest challenges in working with data is ensuring that it is not only correct on a technical level (sums that add up, clean formats), but that it makes sense and provides real value to the business. The real quality of the data comes not only from the tool, but from the understanding of the business and the indicators that are built.
That’s why I want to share a simple and effective methodology that will allow you to create solid KPIs based on quality data. As in climbing, it is key to divide the path into phases and establish checkpoints to ensure that you are moving safely towards the goal.
5 steps to building effective KPIs
1. Define the initial prompts
The first step is to identify which events you need to constantly monitor. These warnings represent specific actions or situations that, if they occur, need to be taken into account. It is important to think about those critical points that may affect overall performance or business goals. For example, a customer cancelling a subscription or a user abandoning a shopping cart. It is these simple events that lay the foundation for building more complex KPIs later on.
2. Set alarms
Once you have defined alerts, it is essential to set up alarms that trigger when an alert is not addressed in a reasonable amount of time. Alarms not only indicate that something has gone wrong, but also allow you to identify patterns of risk and take action before the problem escalates. For example, if a customer cancels his subscription and no action has been taken within 48 hours, an alarm is triggered indicating the risk of churn. Similarly, a customer inactive for 30 days may be a red flag that needs to be addressed.
3. Create operational KPIs
The next step is to consolidate the alarms and transform them into operational KPIs. These KPIs reflect the day-to-day state of the business and allow you to measure performance on an ongoing basis. For example, the percentage of cancellations not attended within the defined timeframe or the number of leads that have not been followed up within 24 hours. Operational KPIs help to have constant control over the workflow, making it easier to make quick decisions and correct deviations.
4. Elevate KPIs to a strategic level
As operational KPIs are consolidated, it is time to take them to a strategic level. Strategic KPIs analyse the impact of daily indicators on overall business performance. For example, an increase in unattended cancellations can lead to a decrease in monthly net growth. These indicators help to understand how operational issues affect long-term objectives and facilitate high-level decision making.
5. Build a data pyramid
Finally, it is critical to structure KPIs in the form of a pyramid, with each level aligned to the overall business objectives. From initial warnings to strategic KPIs, each piece of data should have a specific role in the decision-making process. This pyramid ensures that efforts are directed at generating long-term value and that the business is prepared to face any challenge based on accurate and well-structured information.
The KPI pyramid: A map to the top
Each step in the creation of KPIs is designed to build on the previous one. In this way, a structure is generated that, like a pyramid, is strengthened at each level. From initial warnings to strategic KPIs, each piece of data has a role in decision making.
Conclusion
Scaling with KPIs is not only a measurement tool, but a guide to transform data into concrete decisions. By building a pyramid of indicators, we not only look at the surface of the data, but we dig deeper into its meaning, making sure that each warning, alarm and KPI leads to decisions aligned with business objectives.
Because in the end, it’s not about having more data, it’s about having better data and, above all, knowing how to use it to get to the bottom line.
