You may have already heard about “the modern data stack” if you work in data. However, the term can be confusing for practitioners due to the varied definitions and architectural diagrams associated with it. Additionally, the modern data stack is often positioned as a single big step for a data team, hence making practical implementation difficult.
While data team practitioners know that the reality on the ground is different and that data stacks evolve along with the needs of the business, the conversation around the modern data stack often overlooks this fact. The goal for data teams is to progressively modernize the data stack in practical ways that impact the business’s ability to make better decisions, rather than blindly adopting a particular architecture. Modernizing a stack is a process that takes time and thought at each juncture, involving many steps as the business grows and changes.
The good news is that you don’t have to take a single, overwhelming step. You can begin making small changes at your starting point to better serve the data needs of your business. This is the first post in a series aiming to break down each phase of the Data Maturity Journey, a framework built to aid data teams in navigating the sea of tools and architectures of the modern data stack. It is designed to help companies build the most practical, helpful stacks for every stage of their journey. Today, we will introduce the “Starter Stack,” the first phase in modernizing the data stack.
It’s important to note that the “starter” designation isn’t limited to company stage. Whether you’re a startup or a large corporation, the Starter Stack is designed for companies of any size facing data integration and data consistency problems due to the lack of a unified data layer.
The Starter Stack introduces a “unified data layer” to address fundamental data challenges and enable more powerful data use cases. It aims to solve data consistency and integration complexity. These problems can manifest as constant pain related to data and slower velocity due to data consistency and integration issues, such as point-to-point integrations between SaaS tools requiring constant maintenance, different teams having different versions of the truth, and the inability to answer simple complex questions about customers and their journey, among others.
The Starter Stack architecture involves leveraging a single system for creating and updating both customer behavioral data and customer traits, and unifying all integration needs into a single, automated integration layer.
Knowing when to implement the Starter Stack is crucial to successfully navigating your data maturity journey. Symptoms that indicate you need the Starter Stack include constant pain related to data, decreases in velocity due to data consistency and integration problems, and the need to export data from multiple systems in order to answer basic questions.
Whether your company is small or large, the Starter Stack is the first step in modernizing your data stack, and it’s critical to get this first step right because it sets the foundation for later phases of the journey, such as the Growth Stack and ML Stack.
To illustrate this, let’s look at an example company that’s ready for the Starter Stack. You’re an eCommerce company focusing on driving digital purchases through your website and app. Your website and mobile teams run scripts for various marketing and analytics tools, and your sales team relies on a CRM connected to an email marketing tool. This company would benefit from implementing the unified data layer provided by the Starter Stack.