Post by account_disabled on Dec 24, 2023 0:28:08 GMT -5
Aup a glossary on the Confluence shared workspace that allows us to identify and present each of our flows by indicating the data schema the service provider involved the time of reception the creation or not of associated intermediate tables and the definition of the fields of all our data sets etc. It is a titanic task. We are still very far from having finished it but we are spending a lot of time working on it.
How do you ensure the quality of your data on a daily basis On the site side as I explained earlier we have a person who ensures the quality of the implementation of our tagging plan. We also have people who take care of the reliability of the input data flows on the big query side. Data cleaning Phone Number List is carried out by our DPO in consultation with the data marketing and technical teams and includes a regulatory data purge. In addition we also have monitoring metrics to identify possible bugs or system crashes. For this task we use New Relic on the web and Crashlytics on mobile apps but these tools are more for the technical teams. In this environment what are the objectives of the data team Our teams goals are simply aligned with the publishers business model i.e.
Generate more page views thus more display advertising and more subscriptions. All our data projects must be aligned with this goal. Today data is used to deliver the right article to the right user but it will not specifically influence production. We can indeed occasionally identify a users interest in a subject for example more recently MMA or Formula and share this trend directly with editorial teams in the form of recommendations. But data does not drive the editorial line of the newsroom. What are the upcoming data governance projects At Lquipe I feel that we are mature.
How do you ensure the quality of your data on a daily basis On the site side as I explained earlier we have a person who ensures the quality of the implementation of our tagging plan. We also have people who take care of the reliability of the input data flows on the big query side. Data cleaning Phone Number List is carried out by our DPO in consultation with the data marketing and technical teams and includes a regulatory data purge. In addition we also have monitoring metrics to identify possible bugs or system crashes. For this task we use New Relic on the web and Crashlytics on mobile apps but these tools are more for the technical teams. In this environment what are the objectives of the data team Our teams goals are simply aligned with the publishers business model i.e.
Generate more page views thus more display advertising and more subscriptions. All our data projects must be aligned with this goal. Today data is used to deliver the right article to the right user but it will not specifically influence production. We can indeed occasionally identify a users interest in a subject for example more recently MMA or Formula and share this trend directly with editorial teams in the form of recommendations. But data does not drive the editorial line of the newsroom. What are the upcoming data governance projects At Lquipe I feel that we are mature.