Make data work for you: collection, storage, and manipulation
Published: September 13, 2016
Author: Eric Swanson
Lately, the 3Q Decision Sciences team has tried to break down some of the fast-climbing industry buzzwords surrounding data. Today, we’ll take on data warehouses and data visualization.
Like many companies who provide value to their clients through advanced reporting and analytics, 3Q Digital gains efficiencies by leveraging a centralized data warehouse and data visualization platform. Our reporting system has 3 main components: data collection, data storage, and data manipulation.
Before I get in too deeply here, let me address Excel, which a lot of you are probably using to manipulate data. Excel is great – and cheap! – but it has limitations. Here’s a quick snapshot of Excel vs. Tableau capability:
Convinced that there are other options worth looking into? All righty.
Starting with data collection, we can choose from a robust amount of options to fully automate our ETL processes. We work with multiple technologies including SQL Server BI stack, an FTP server, and API calls, giving our account teams flexibility to interact with an increasing number of publishers that are relevant to their client’s business needs. In addition to standardizing processes for large vendors such as Google, Bing, and Facebook, we also develop custom solution for smaller partners.
Once the standard performance data is gathered, we can connect information from web analytics services like Google Analytics, attribution platforms such as Convertro, and internal client data to add context to marketing initiatives.
We combine the relevant sources in our central data repository to provide one source of truth that is updated nightly, and addresses 30 days of latency to ensure our numbers are up to date and accurate. From here, we develop business logic in our data warehouse to build custom metrics and dimension.
Tableau then visualizes the data, providing our clients with an interface that is delivered securely to users online. Account teams benefit from automated, multi-channel reporting, and can dedicate time savings to analysis, account optimizations, and strategy. Another great feature is that our decision scientists can explore this same data set to take us from reporting on what happened, to explaining why and advising on what we should do next.
The upshot is this: companies who put more resources into getting smarter with their data will collect, store, and visualize it more effectively, which will free up account teams to do what they do best. Add some true specialists to apply advanced insights, and your data will be mapping out a path to bigger and better marketing performance.