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.

Tag management

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.

Data warehouses

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.

Machine Learning

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.

Data Integration Services capabilities

Application Programming Interfaces (or APIs) make it easier for developers to use certain technologies in building applications. In Quru for instance we use APIs to connect disparate data sources together in order to build data studio reports. We also use APIs to develop things like data management platforms and populate data warehouses.
The term "big data" tends to refer to using predictive analytics, user behaviour analytics, or certain other advanced data analytics methods to get value from the dataset rather than the size of the data. Extracting, integrating, formatting, cleaning and representing big data in a manner that is easy to understand is often challenging. However, getting value from data is our bread and butter.
Customer data platforms or data warehouses are typically used as unified customer databases that are accessible to other systems. Data is pulled from multiple sources, cleaned, and combined to create a single customer view. This structured data is then made available to other marketing systems. We help integrate data to and from DMPs or CRM systems and create audience segments from analytics to help enrich individual customer data.
Analytics tags were formerly hard-coded in the website HTML code. The main problem in this model was that you usually had to work with IT people to make the changes to the code. In today’s fast moving Internet world, campaigns are very agile. If you want your analytics to follow so you don't miss out on valuable insights you should use a Tag management tool like Google Tag manager (GTM) or Adobe Dynamic Tag Management (DTM). We're well versed with both (and other paid tools too).