Marketing DataWarehouse: Fast and Easy ways to build

7 min readJun 27, 2019

Building a marketing data warehouse is not a simple project to do but if it’s done properly, there is a numerous business outcome.

We will go through 3 things.

  1. Why we need marketing data warehouse and what’s so important?
  2. How should we build marketing data warehouse and different approaches ?
  3. What are the tools/skills needed to build data warehouse?

1. If you have a complex marketing technology stack

if you are a pioneer, you would have faced a lot of challenges before few years when assembling your marketing technology stack. It was not easy initially as I have experienced myself that many tools features were overlapping each other and end up being a dozens of multiple tools. And the data is scattered and silo. How can be bring together to improve the performance of different layers?

2.If you have matured marketing technology stack

Some latecomers have enjoyed the maturity of the industry where they escaped from all the challenges faced by the pioneers. And 90% clear on what stack would be perfect for them. but more than the data sync, there are multiple needs for a data warehouse. We had a vision of enriching our marketing Datawarehouse and then supply this data to data scientists for further analysis, deep learning and modelling.

Companies are moving from the insights provided from Analytical tools to supply this database to the marketing systems.

3. Building the warehouse to help the business Intelligence.

Instead of trying to connect business intelligence system with every other marketing tools/channels. Marketers can get the data into Datewarehouse solutions and bring the data into business intelligence tool via warehouse. Why? Because getting the data into BI from the marketing platforms directly takes up data Load work, Latency, etc. Also bringing the data into warehouse always allow you to store the newly cleansed data into new dataset.

Case1: Company like Uber

Here is an example happening on Uber Technologies. or basically you want to store all the data safely for other use cases. Uber current big data process 100+ Petabytes data with Minute Latency. It has achieved…


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