Weighing in with over seven million global subscribers in more than 200 countries and territories, DAZN have emerged as the undisputed heavy weight in the sports streaming space since its launch in July 2015. At first glance, the success of DAZN may be attributed wholly to their exclusive boxing rights with the likes of Matchroom and Golden Boy, or their domestic rights for La Liga, Serie A, and the Bundesliga in their respective nations. It could also be down to DAZN’s over-the-top (OTT) media service, which allows them to subvert the controls of more traditional television platforms, like satellite or cable. However, it was another major ingredient to DAZN’s continued ascendency that was the subject of enquiry at Burns Sheehan’s recent Data Event - Analytics Engineering: The Life Cycle.

In many ways, Analytics Engineering is to data analysis what DAZN has been to sports broadcasting, what rugby was to football, what Fabio Capello was to Ketchup. It has fundamentally rewritten the rules of engagement by negating the process of compiling insight reports and trend analysis, and by-extension, the utility of the likes of Excel and Looker. Analytics Engineering departs from the messy days of the maintenance of SQL files to now provide clean data sets to end users; these data sets are complete and ready for analysis. Through best practice Software Engineering, analytics codes are continually enhanced to better the information that is available to the business. But how does this relate to DAZN?

As Andrea Salvati of DAZN explained, DAZN deal with a lot of data, demanding transformation through pipelines that are built to scale and capable of dealing with different data sources. These disparate data sources present significant challenges towards achieving consistency. For the non-technologically inclined like me, without the work of Analytics Engineers, the achievement of consistency from different data sources is to DAZN what trophies are to Tottenham- historically elusive.

To navigate this impasse, Analytics Engineers ensure that data follows three steps before reaching the end user. Scheduled through Airflow, Data is extracted and loaded through Snowflake, transformed via DBT, and then either delivered visually through Tableau, or presented as a self-service analytics tool. This is done in accordance with the needs of business stakeholders and technical teams, meaning transformations are defined to not only ensure that more valuable conclusions can be drawn from this data, but also their business impacts can be gauged and measured.

To ensure that the end insights are readily available for both groups, DAZN construct a front-end enabling stakeholder to interact and test the logic through Streamlit and abstract the logic of transformations away from the main script to eradicate challenges in finding the logic for specific transformations.

In short, through Analytics Engineering, DAZN have been able to provide much needed clarity to the process of analysis, by cleansing what was once a messy assemblage of data sources to create a vital interactive business analytics tool. Evidently, DAZN’s success in recent times has been down to this innovative solution to catalyse the speed at which it is possible to draw valuable conclusions and inform their data-driven decisions.

Whilst I would certainly be categorised within the lowest percentile of technological literacy in the room, it is to the credit of Andrea that even I was able to understand how analytics engineering fits into DAZN’s M.O. I thoroughly enjoyed the event and hope everyone from DAZN did also. I now look forward to seeing what new areas emerge in the coming months and seeing just how far DAZN can go.

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