What does an analytics department look like at an online casino? Part 1.

What does an analytics department look like at an online casino? Part 1.

Nikolai Golovatsky, Co-Founder of ANALYTIX, explained what the structure of an analytics department looks like at an online casino. Passing the word ↓

A standard mistake I encounter in almost all young projects is not understanding how analytics should look and work.

If your friends« analytics department looks like »one person who analyses something" - forward this post to them. If your department looks like that too, this post is for you.

Behind the beautiful dashboards, graphs and reports, a large, complex infrastructure is at work. To develop it, you need specialists of different profiles with clear roles.

• The foundation is data engineers

Their main task is to develop analytical databases (DWH). In simple words, they are huge data warehouses with a bunch of spreadsheets that collect all the data useful to the business:

• registrations;
• payments;
• stakes;
• sent pooches;
• bonuses given, etc.

Analytical DWH = the foundation on which all analytics work.

Data engineers connect various data sources and design tables in the database so that all data is easily accessible for further analysis. They also develop automated processes that monitor the quality of the data - checking it for errors and inaccuracies.

The data engineer is also responsible for making sure that data is not lost anywhere, that it is updated regularly.

As a result, always up-to-date and verified figures.

Data collection - web analytics

These specialists collect events from the frontend part - that is, information about what the user does on the product interface. For example, button clicks, form fills, page transitions, etc. They study the product in detail and describe what user actions should be recorded for analysis, and together with developers add «triggers» to collect this data.

Special tools are usually used to work with such analytics: PostHog, Amplitude, Google Analytics and others.

Visualisation - BI specialists

This role is not emphasised by everyone, but in large companies it often exists separately. BI specialists work professionally with BI systems and data visualisations.

Their task is to build convenient and useful dashboards. There is a huge amount of data in DHW → BI-analysts convert it into correct tables and dashboards so that everything is clear.

Predictions and Models - Data Science and ML Engineers

A completely separate speciality in the data science field. They design and train machine learning (ML) models and neural networks from scratch to solve specific business problems. In simple words, they create and deploy ML solutions to businesses that:

• are predicting user churn;
• predict the return on traffic;
• create systems of recommendations and personalised suggestions;
• segment VIP players, etc.

Important to note: Data analysts can also work with AI, but they usually use off-the-shelf models or tools. While Data Science and ML engineers are the ones who develop these solutions from scratch and implement them into business processes.