About a year ago I started working with a successful wearable-device company. This company hired me to help them with two main challenges:
In this post I'm going to cover how we achieved these goals and how you can do the same for your own organization. The lessons I share in each section are relevant for any organization making significant changes to their BI infrastructure.
My client was kind enough to agree to me writing this case study but I decided to keep their identity private so that I can share as much as I can about the process and what we learnt.
The first step I take when helping a client become more data-driven is to conduct a discovery and data audit.
The main goals of this phase include:
I learnt the following information about my client during the discovery phase:
After the initial discovery of the business, I was able to draw up a road map for centralizing the companies data in a data warehouse.
The idea is to move all the relevant data into a single data base to make it easy to build dashboards and to conduct analyses. This would allow a quicker delivery time and to allow more individuals to get value from the company's data.
A data warehouse would also make it easy for future analysts to immediately provide value to the company.
An overview of my plan can be seen in the diagram below:
The BI stack that I recommended to my client included:
My client had their product data spread out in a number of different data silos. These included a MySQL database, Intercom and Firebase. At a later stage a MongoDB was also introduced which added complications.
Since Firebase would act as our event tracking solution, and it has its own integration with BigQuery, I decided to go with BigQuery as our data warehouse.
The challenge was to move the MySQL, MongoDB and Firebase data into BigQuery.
We managed to move these sources into BigQuery by using a combination of ETL scripts that we wrote and Stitch. Before we started moving these sources into BigQuery we made sure to set up a replication process so that the scripts and Stitch were not running on the production databases.
We were able to easily push the Firebase events into BigQuery by using the native integration between the two tools.
One major advantage of BigQuery is the ease of creating views. Once I had all the data silos in BigQuery I could build smart tables made up of data from numerous sources.
One example of such a table is the "master users" table. This table includes user properties and aggregations from all the data sources. Each row represents a user and the columns represent user-specific information. This table would become central in all the dashboards I built for the product team.
A critical piece of the BI puzzle is data visualization. A data warehouse provides a ton of value to analysts but in order to share that value, you need dashboards. Tableau is my first choice and thankfully my client trusted my decision to go with Tableau.
One major decision that needs to be taken when setting up Tableau is to go with Tableau Online or Tableau Server. There are a number of pros and cons with each which I've covered in detail in this introductory post to Tableau.
In order to speed up the process and eliminate reliance on R&D we went with Tableau Online. We started with a single license of Tableau Online and Tableau Desktop.
We introduced Tableau on day 1 because I was able to connect to replicas of the data silos and conduct analyses for my client.
I was able to answer key questions relating to retention by accessing usage data and working with it in Tableau Desktop.
Once the initial data was being pushed into BigQuery I as able to focus my energy on building robust dashboards for the product team.
Below are some screenshots of some of the dashboards I built for my client.
Notice the user-level filters like gender and age. Since all of our product data is in one database, BigQuery, it is easy to blend it with usage and retention data and then build very robust dashboards.
When my client hired me 12 months ago they needed help better understanding their product retention and usage.
Today the product team has a number of powerful dashboards that they use to track key KPIs and answer ad-hock questions on their own.
Their data warehouse continues to scale as does their user base and the foundation is in place to quickly analyze their product and users in the form of analyses or dashboards.
The head of product and his product manager are using these dashboards to frequently update the CEO and other executives in the organization. Overall everyone in the company is smarter when it comes to understanding how people are using their device and mobile apps.
Thanks for reading.