In daily growth work, the production database is not enough to make effective decisions. Many teams start by directly questioning the OLTP database, the one that records transactions in real time, to answer business questions. At first it seems to work, but soon problems of slowness, fragile joins and inconsistent metrics emerge.
Thesis
OLTP is like the store’s cash register: it records every transaction. OLAP is the room where you analyze shelves, margins, cohorts and seasonality to understand how business grows. It’s not about adding another tool or a dashboard in more, but about building a mechanism that makes the switch from signal to decision faster. A good growth system doesn’t promise certainties, but reduces the cost of uncertainty.
Operational schedule
The process consists of five clear steps:
- Operating sources: raw data from the production system. 2. Controlled extraction: transform and clean data for analysis. 3. Analytical model: integrate calendar data, campaigns, returns, costs, segments and historical customer. 4. Shared metrics: unique and consistent definitions for everyone. 5. Reading for dashboards, experiments and decision-making agents.
The simplicity of this scheme is desired. The complexity comes only when traffic, segments, channels and automations increase. If the basic flow is not clear in a few steps, it is probably automating a process that is not yet understood.
Practical example
An order is perfect in the operating database, but to understand the long-term value of the customer (LTV) you need data integrated and historicized in an analytical model. The true value is not in the single data, but in the connection between intervention and learning: if the result improves, you know what to scale; if it gets worse, you know which hypothesis to correct. So the system becomes more intelligent.
Metrical to watch
- Query execution time, Number of duplicated definitions, Completeness of the historian, Freshness of data
These metrics must enter a short scorecard, read regularly and associated with concrete decisions. If a metric does not affect any choice, it is only a comfort metric.
Typical error to avoid
Confuse the actual data with the data ready to decide. The operational data is true, but often not interpretable. This error is common because it seems productive: it generates activities, meetings and graphs, but the value of growth engineering is measured by the quality of the learning cycle that remains.
Checklist for the team
What kind of decision should be made more clearly?, What event or data source makes behavior observable?, What risk do we not want to make worse while optimising?, Who can really change the process after reading the result?
If at least one answer is vague, it is better to stop before implementing. The real speed is not to start immediately, but to avoid to redo the job for hypotheses, data or confused criteria.