This article aims to provide a deeper understanding of the typical steps involved in advanced analysis.

As visualised in the snail-shaped process below, advanced analysis begins with a research question or a hypothesis that drives the analysis. From there, the relevant available data sources and the most effective approach for answering the question are determined.

Then we come to the most challenging part of the cycle, the data preparation. In most cases, the data required to answer a research question comes from multiple sources, such as GA4 and DV360. Integrating these data sources in BigQuery can be challenging, requiring a deep understanding of the data to ensure that the data is accurate and complete. Another challenge of data preparation is cleaning the data itself. Data is often imperfect, with missing values, errors, and inconsistencies that can compromise the quality of the outcome of the analysis. Remember that the more effort and care you put into preparing and cleaning your data, the more accurate and robust your final results will be.

With the clean data in hand, the analysis itself can begin. This step can vary depending on the question in the first step but typically it involves slicing and dicing the data and applying statistical techniques to identify patterns and trends in the data. These findings are evaluated and interpreted to gain insights into the research question or hypothesis. The interpretation of results is a crucial aspect of advanced analysis, as it requires a deep business understanding and the used methodology itself.

Finally, the analysis result must be activated, usually either by creating a dashboard for questions that need to be answered on a regular basis or by deploying a model that will be updated regularly. It is very common that after the last step, follow-up questions are raised and the snail cycle could potentially start again.

At Digitl we are facing the exciting challenge of executing each step of this snail-shaped process in the most optimal way on a daily basis. We have successfully executed numerous advanced analysis projects. As a result, we have been able to enhance the capacity for data-driven decision-making for our clients, which you can learn more about in the following sections. Below, you will find a selection of some of these projects that resulted in significant improvements for our clients.