Why Your Startup Needs a DBT Data App *NOW*
I see a lot of startups under-investing in their analytics and business intelligence, so I decided I’d put some thoughts together to outline how vital it is to invest in a robust data warehouse before you think you need it.
As a startup leader, you’re constantly bombarded with data from all directions – whether it’s customer behavior, marketing performance, or financial metrics. But amidst the data deluge, it’s so easy to doubt your reporting or get bogged down by having a low signal-to-noise ratio and little insight. Making data-driven decisions can save you months of development time or many thousands of dollars and can even be the difference between success and failure. This is where a DBT (Data Build Tool) application becomes invaluable, offering a powerful solution to transform your raw, siloed data into reliable, fast, and valuable business intelligence.
Understanding DBT
DBT (Data Build Tool) is an open-source technology that developers use to turn data into stories. DBT transforms raw data into reliable, flexible, and fast business intelligence tables, allowing you to focus on growing your business without getting bogged down in data complexities or worrying about inconsistencies.
Reliability
DBT ensures high data quality through automated testing and documentation. This means you can trust the data you’re working with, leading to confident decision-making. No more second-guessing the numbers or dealing with inconsistent reports.
Flexibility
DBT’s compatibility with various data warehouses like Snowflake, BigQuery, and Redshift means it adapts to your existing data infrastructure. Whether you’re scaling up or pivoting directions, DBT provides the flexibility needed to keep your data operations smooth and efficient.
Speed
With DBT, you can quickly write, test, and deploy data transformation code. This accelerates your development cycles and reduces maintenance efforts. Modular SQL code allows for rapid iteration and easy adjustments, ensuring your data pipelines evolve with your business needs.
Empowerment through Open-Source
Being open-source, DBT allows developers to create custom, scalable data transformation models that integrate seamlessly with your business analytics tools. This means you can have bespoke data solutions tailored specifically to your startup’s requirements, enhancing your ability to derive actionable insights from your data.
How DBT Works
DBT functions by leveraging SQL to transform raw data directly within your data warehouse. Here’s how developers use it:
1. Writing Models
Developers write modular SQL queries, known as models, that define how raw data should be transformed into clean, analysis-ready tables. These models are structured in a way that they can be reused and easily modified.
2. Testing and Documentation
DBT includes built-in testing and documentation features. Developers can write tests to ensure data integrity and create documentation to keep track of data transformations. This makes it easier to maintain high data quality and transparency.
3. Version Control
DBT integrates seamlessly with version control systems like Git. This allows teams to collaborate on data models, track changes, and ensure consistency across the data pipeline.
4. Dependency Management
DBT manages dependencies between models automatically. When changes are made to an upstream model, these changes cascade through all dependent models, ensuring consistent and accurate data across the entire pipeline.
5. Deployment
Once models are written, tested, and documented, they can be deployed to the data warehouse. DBT handles the orchestration of these transformations, ensuring that the data pipeline runs smoothly and efficiently.
Analogy Time!: Car parts vs. Cars
Using DBT is like having a custom-built car assembly line. Imagine that production applications and third-party tools store data like car parts in a factory. The wheels are in one section, the seatbelts are in another, and the windshields are in yet another location. Business stakeholders, however, need fully-assembled cars to get meaningful insights. DBT allows developers to take these scattered parts and assemble them into a fully-functional vehicle, tailored to the specific needs of the business. This way, stakeholders can ask questions and get answers without needing to understand the intricacies of assembling a car. Unlike off-the-shelf tools, DBT provides a customized solution, ensuring that the data is transformed and presented exactly as the business requires.
Imagine having a custom-built, streamlined, scalable business intelligence tool that grows with your startup, providing you with actionable insights at your fingertips. This is not just about improving data workflows; it's about giving you a competitive edge and peace of mind.
As a freelance data engineering consultant, I specialize in building robust DBT applications tailored to your startup’s needs. Let me help you unlock the full potential of your data, so you can make informed decisions faster and drive your business forward.
4 Problems DBT Solves for Your Company
1. Achieving Full Data Transparency
Problem: Disorganized Data Across Multiple Platforms
Startups often have data scattered across various platforms like Shopify, Google Analytics, Segment, and production databases. This disorganization makes it challenging to get a unified view of your business.
Solution: Unified Data Models
A DBT app helps you create unified data models by integrating disparate data sources. This enables you to connect dots that were previously isolated, giving you a holistic view of your business operations.
Example: At MUDWTR, we faced challenges in calculating customer retention and lifetime value due to frequent platform changes. By building a custom ETL application in Ruby on Rails and integrating it with DBT, we created a unified customer model that linked products, orders, and subscriptions. This allowed us to identify valuable customer segments and measure LTV accurately.
2. Enhanced Decision-Making with Actionable Insights
Problem: Inability to Perform Ad Hoc Data Queries
Startups need the flexibility to ask and answer ad hoc questions about their data without relying on pre-defined reports.
Solution: User-Friendly Dashboards
DBT, combined with tools like Metabase or Looker, enables you to create user-friendly dashboards that provide real-time insights. These dashboards are designed to answer future questions even before they are asked, making data exploration intuitive and accessible.
Example: At Canal, we built a custom DBT data application with 150 unique models, creating comprehensive dashboards in Metabase. This allowed the team to measure product-market fit progress and make data-driven decisions, impressing both executives and investors.
3. Ensuring Consistent and Reliable Data Reporting
Problem: Inconsistent Results from Slightly Different SQL Queries
Bespoke SQL queries written at different times for different purposes can lead to gross inconsistencies and unreliable results, causing you to doubt your reporting and preventing you from confidently making decisions.
Solution: Efficient Data Transformation with DBT
DBT simplifies data transformation by allowing you to write modular, reusable SQL code components. Its model lineage feature ensures that changes in one part of the data pipeline cascade consistently throughout the full data warehouse, reducing maintenance overhead and keeping methodologies of calculations consistent throughout.
Example: At MESA, I used DBT to integrate data from various sources like GA4, a production database, Google Search Console, Shopify, and Customer.io into a single interconnected picture of every customer. No more joining disparate tables or writing calculation logic on the fly. This approach streamlined maintenance and ensured data accuracy, enabling informed business decisions on marketing channels, product development, and pricing models.
4. Eliminating the Hassle of Nested Views
Problem: Painful Maintenance of Nested Views
In traditional SQL environments, maintaining nested Views can be a significant headache. Each change in an upstream View often necessitates deleting all dependent Views before making edits, and then recreating them afterward. This cumbersome process is not only time-consuming but also prone to errors, leading to inconsistent data and delayed insights.
Solution: Simplified Data Modeling with DBT
DBT allows you to build a more efficient and maintainable data transformation pipeline without the need for nested Views. With DBT, you write modular SQL code, creating reusable models that can be easily maintained and updated. When changes are made to an upstream model, DBT’s dependency management ensures that these changes automatically cascade through all dependent models, eliminating the need for manual deletions and recreations.
Example: At Grüvi, we initially relied heavily on nested Views to transform our data from multiple sources like Google Analytics, Shopify, and HubSpot. Each modification to an upstream View required a tedious process of deleting and recreating dependent Views, which significantly slowed down our workflow. By switching to DBT, we streamlined our data transformation process. DBT’s model lineage and dependency management features allowed us to update upstream models seamlessly, ensuring consistent and accurate data across all our analytics platforms without the hassle of nested Views.
5. Building a Data-Driven Culture
Problem: Disjointed Reporting and Poor Data Accessibility
Startups often struggle with disjointed reporting and poor data accessibility, making it difficult for teams to leverage data effectively.
Solution: Centralized and Accessible Data
DBT allows you to centralize your data and make it accessible to all stakeholders. By creating flat, column-rich tables, DBT enables business users to access and analyze data without needing deep technical knowledge.
Example: At SmartProperty, we created a comprehensive DBT application that integrated data from multiple sources. This centralized data access empowered teams to make data-driven decisions and fostered a data-centric culture within the organization.
When is the right time to build a DBT app at a startup?
If you’ve recently secured funding or you’re riding high on the wave of early traction, it might be tempting to put off building sophisticated data & BI infrastructure. After all, everything’s going great, right? But as my mom always said, “tough times are right around the corner.” There are many ups & downs on the startup journey, and eventually you’ll wish you had started building a robust, flexible business insights tool six months ago when you had the time and resources.
Think about it like buying insurance or a security system for your house. You don’t wait until after the break-in to install an alarm, and you don’t purchase insurance post-accident. You prepare in advance to safeguard your future. In the same way, investing in building a DBT app before you desperately need it can save you from scrambling to make sense of your data when the stakes are high.
Here’s why it’s crucial to start now:
Early Detection of Issues
With a solid data infrastructure, you can catch potential problems before they escalate. Whether it’s identifying a drop in user engagement, spotting inefficiencies in your marketing funnel, or recognizing a decline in product performance, a DBT app can help you stay ahead of the curve.
Scalability
As your startup grows, so does your data. A DBT app ensures your data management scales with your business. By implementing it early, you avoid the technical debt and chaos that comes with trying to retrofit a solution when your data is already out of control.
Investor Confidence
Investors love data-driven startups. Demonstrating that you have a robust data infrastructure in place not only shows that you’re serious about growth but also instills confidence that you’re prepared to handle future challenges.
Time Efficiency
Building and optimizing your data infrastructure while you have the luxury of time means you won’t be scrambling to fix issues when you’re in the thick of scaling operations. This proactive approach saves valuable time and resources in the long run.
Competitive Edge
Startups that leverage their data effectively are often more agile and capable of making informed decisions quickly. This gives you a significant competitive advantage, enabling you to pivot or double down on strategies based on solid data insights.
Remember, building a robust data infrastructure with a DBT app is not just about handling current data needs; it’s about future-proofing your startup. Just as you wouldn’t drive a car without a seatbelt or go without health insurance, don’t wait until you’re in a tight spot to realize the value of a solid data foundation. Invest in your data now, and your future self will thank you.
Conclusion
In the fast-paced world of startups, having the right data at your fingertips can be a game-changer. A DBT app not only enhances data transparency and decision-making but also streamlines data transformation and fosters a data-driven culture. If you’re looking to unlock the full potential of your startup’s data, implementing a DBT app is the way to go. And if you’re looking for someone to outline and build it for your company, I am your guy.