The data management services we’ve developed have come as a result of the fast pace of change we’ve seen over the last decade.
Tagging websites used to be a process which involved defining KPIs and then writing up documents before sending them to developers (often on the other side of the world) to deploy on websites. This was slow and laborious and Tag management services have largely removed that problem.
We often have to move data around, merge it, extract it, add other big data sources to it and we do that in data warehouses. We use BigQuery or Amazon Web services typically if we’re deploying things from scratch or running it ourselves but we’re also used to using a variety of customer tools.
Once we have numerous datasets in a data warehouse we can apply machine learning algorithms to the data to determine patterns. For instance we might add a lot of social media data and match it with web or offline data for a customer and run an analysis to see if any patterns emerge. Once the patterns do emerge then our analysts will go and find out if there are any insights we can draw from the findings.