Category: Data Pipeline

Is your marketing data currently under the control of multiple vendors and platforms? Do you have to mix together siloed data and reporting tools to answer questions about marketing ROI and your customer’s journey? It’s time to take control of your data!

Cloud databases (i.e. BigQuery) and various data management tools have made it possible for marketers to build data pipelines without a big investment in hardware, software, and custom development.

In this article, we will walk through the steps of gathering data from Google Analytics, Google Ads, Google Search Console, Facebook, CRM and several other sources into Google BigQuery data warehouse and making it the single source of truth for all marketing data.

The number of marketing tools an average business uses has grown rapidly. Besides one or two analytics platforms there’re a few ads platforms, CRM, CMS, several social media platforms, an email system and probably a few more tools and platforms.

All of those tools are supposed to make our work as marketers, business owners or data analysts easier and more effective. In reality, though, you will end up with a bunch of silos – systems that don’t really communicate well with each other and almost never agree on any of the important metrics.

Data silos create confusion and disagreement between teams, leading to a situation where, at the end of the day, no-one knows which tool or numbers to trust.

BigQuery is an extremely powerful tool for analyzing massive sets of data. It’s serverless, highly scalable and integrates seamlessly with most popular BI and data visualization tools like Data Studio, Tableau and Looker.

Working with Google Analytics data in BigQuery has mostly been a privilege of those having a 360 version of Google Analytics. Its hefty price tag, though, has made that list quite short.

Google Analytics is a really good tool for marketing-focused digital analytics. And by far the most popular one in this segment. With some custom setup, you can also use Google Analytics for tracking SaaS and other web apps & products.

Two of the most common shortcomings of Google Analytics that most of the more advanced users experience, though, are the lack of hit-level granularity and sampling. In this article, we are taking a look at some of the ways you can overcome these shortcomings without spending a fortune on Google Analytics 360.

This blog post is aimed for anyone planning to build a data pipeline or upgrade their current setup.

An end-to-end analytics data pipeline is a secure and reliable mechanism that is responsible for feeding your business with valuable data that can be used for reporting, analysis, machine learning or any other activity that requires accurate data about your business.