Simplifying the Complex: The Secret to a Small/Mid-Size Startup Data Warehouse
Choosing the Right Data Foundation
👋 Ciao, Alex here. Welcome to a new free edition of Not Just Bits, and thank you to all the readers and those who support my work. Every week, my goal is to share lightweight and informative resources for CTOs.
You realize it's time to adopt a more analytical approach to decision-making but the question remains: which data sources to use and how to begin? In the following article, I will share some of my key learnings and a solid serverless setup for a data warehouse.
Nowadays, there is an overload of data and many times, less is better.
My first lesson learned in building a data warehouse was to initially reduce the data sources and focus on key business metrics.
Choosing the Right Data Sources
For the majority of organizations I've worked with, the main data sources were payments, customer success tickets, and NPS. These sources are not just streams of data; they are the veins through which the lifeblood of the business flows, offering a transparent view into its health and customer perceptions.
Once the data sources were finalized, it was time to decide how to connect to them or get notified in real-time. For this, one of the most effective and cost-efficient solutions was leveraging AWS Lambda or, more generally, serverless functions.
Simplifying Data Collection
Transforming how we collected data was a key turning point. By integrating webhooks and employing AWS Lambda for our ETL (Extract, Transform, Load) processes, we significantly streamlined data collection. Webhooks, alerting us in real-time to events like new Stripe transactions or Zendesk tickets, and AWS Lambda, automating the journey of data from source to warehouse, made the process efficient and nearly hands-off.
Going with Redshift or even Google BigQuery? Well, do you have a very large large data sets that could make running queries super slow? If not, consider starting with a simpler solution, such as AWS S3 or PostgreSQL, and later move to a more performant service like Redshift. Once the data structure is defined, migrating it to a more performant service shouldn't be a problem.
What if someone on your team pushes for a data mesh? Small businesses often start with a more centralized data management approach, which is simpler and more cost-effective to implement and maintain. Do not try starting with it.
Once our data was securely stored, the focus shifted to translating this vast information into actionable insights.
Reporting and Insights with AWS QuickSight
AWS QuickSight became the tool of choice, enabling us to create dashboards that were not just visually engaging but also rich with intelligence. It allowed us to effortlessly monitor key business metrics, from monthly revenue streams to customer satisfaction indices, illuminating the path to data-driven decision-making.
Defining and focusing on key business metrics was central to our strategy. This step was about distilling the vast ocean of data into the quintessential elements that would inform our strategic and product development decisions. Moving away from reliance on instinct to a more data-oriented approach marked a pivotal shift in our decision-making processes
The Outcome
This straightforward yet strategic approach to data warehousing has been transformative. It has brought hidden trends to light, identified growth opportunities, and preemptively addressed potential challenges. More than just optimizing operations or refining decision-making, it has fundamentally altered how startups engage with their data, leading to more informed strategies and happier customers.
In Conclusion:
Simplifying the Complex
Building a data warehouse for a mid-sized startup doesn't have to be an overwhelming challenge. By focusing on essential data sources, leveraging automation for ETL processes, and utilizing tools like AWS QuickSight for analytics, startups can unlock the full potential of their data. This approach ensures that data warehousing efforts are not only aligned with business goals but also pave the way for insightful, data-driven growth.
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See you next week! Best, Alex Di Mango