What is a Data Layer? All You Need to Know About What It Is, How It Works & Its Key Benefits

The data layer is a framework within a digital platform, such as a website or mobile application that organizes data flow between the business user and the underlying data. The data layer is a reliable place to collect data as it is generated, particularly data about user interactions with your platform. This layer is essential to the data infrastructure of modern organizations because it forms the foundation for collecting, storing, processing, and managing data across various systems.

What is a data layer?

The data layer serves as a bridge between the raw data collected by applications, websites, and other tools and the business intelligence platforms that rely on this information. It is often implemented as a JavaScript object for websites or mobile applications. When data is “pushed” to the data layer, it becomes readable by a tag manager and then passed on to the analytics tools and marketing tools of your choice. By implementing a data layer, companies can ensure that all systems access the same data in a structured and consistent format.

Data layer, data virtualization layer, and semantic layer

The data layer provides centralized data storage, management, and transformation, ensuring data consistency for analytics tools. By contrast, the data virtualization layer offers a unified, virtual view of data from multiple sources, such as logical data warehouses, enabling real-time access to all your data. Together, the data layer and the data virtualization layer enhance an organization’s ability to store, manage, and access data. (For a general introduction to data virtualization, refer to our blog, which provides a complete overview.)

The semantic layer sits on top of both the data layer and the data virtualization layer, offering business-friendly definitions and models for data. Business users can interact with the data solely through the semantic layer.

Key components of a data layer

Data access abstractions: This is a design approach that separates the application logic from the underlying data sources. This way, developers can access and manipulate the data without knowing the database details. For example, the data layer defines CRUD (create, read, update, and delete) operations, APIs for searching data, and a data mapper to translate between raw and business objects. The data can then evolve independently of the application logic.

Data management mechanisms: These are the tools organizations use to handle data through its lifecycle, such as storage, retrieval, security, and removal. Data management mechanisms include relational and NoSQL databases, ETL (extract, transform, load) tools, data governance tools, and data quality tools. If the data layer handles these functions, there is no need to build this functionality for each application.

Data access implementations: These are the secure and scalable ways systems and platforms interact with the data. Data access objects (DAOs) are abstract interfaces for accessing data that work directly with data sources. Data access implementations include direct querying, APIs, SDKs, and machine learning models. By using data access objects, business logic is separate from data logic, making code easier to maintain.

How does a data layer work?

As mentioned earlier, the data layer serves as an interface between your website and your data sources. The data layer captures data collected from user interactions, transactions, and other activities and makes the data accessible to various systems, such as analytics, marketing, and business intelligence tools. The data layer accomplishes this through three key processes: tagging, tracking, and data sharing. The existence of the data layer ensures that each process receives the same reliable data.

Tagging: Tagging involves adding tags or other pieces of code to a website or mobile application to capture specific user interactions or other events. This ensures that valuable information flows to the data layer, where it can be analyzed. Google Tag Manager (GTM) is a popular tag management system.

Tracking: Tracking is the process of capturing detailed data about user behavior or application events, such as page views and form submissions. This way, businesses can see details about how users interact with the application.

Data sharing: Data sharing enables the data layer to make standardized data available to internal and external systems, such as analytics platforms and marketing tools. Google Analytics and Facebook Ads Manager are popular tools that analyze user interactions with your website.

Data layer example

The following example shows part of a JavaScript dataLayer object for a simple website. The code snippet below shows capturing page views from user interactions.

window.dataLayer = window.dataLayer || [];
 
// Capturing a page view
 
dataLayer.push({
  event: "page_view", 
  page: {
    url: window.location.href, 
    title: document.title, 
    category: "Home Page", 
  }, 
  user: { 
    userId: "12345", 
    isLoggedIn: true, 
  }, 
  timestamp: new Date().toISOString(),
});

First, the data layer is initialized with an empty array. Then each dataLayer.push call is an event or interaction logged in the data layer. In the example above, every page view by a user is logged in the data layer. You can “push” other information to the data layer, such as products viewed and purchase details.

7 key benefits of implementing a data layer

Improved data consistency: The data layer supports the transformation and enrichment of raw data from multiple sources and adds metadata. By centralizing and standardizing data, this process creates consistent data that is ready for analysis.

Enhanced data quality and reliability: Along with data consistency comes better data quality and reliability. The data layer creates a single source for the most up-to-date data that is validated, formatted, and free of duplication. (For a deep dive into data quality, see our blog about data architecture.)

Increased flexibility and customization: The data layer provides a centralized, adaptable foundation for managing and accessing data. It allows organizations to tailor data to meet unique business needs and adapt quickly to changes since the application logic and data handling are separate.

Improved performance: By optimizing data storage, access, and data retrieval across the organization, the data layer improves overall performance. The data layer can provide such performance-improvement services as data compression, load balancing, data partitioning, and in-memory processing.

Easier integration with third-party tools: Data virtualization within the data layer allows third-party tools to access a unified view of data across multiple sources without needing the details of each source.

Enhanced testing capabilities: The data layer establishes a centralized, consistent, and controlled environment for data. This allows developers and analysts to verify data integrity and security at various stages.

Increased data compliance and governance: Centralized control over data allows for better data governance, including who has access to the data, and ensures compliance with applicable privacy regulations, such as GDPR.

How to implement a data layer

Implementing a data layer involves careful planning. Keep the following in mind when implementing a data layer for your business:

Planning: Planning an effective data layer involves understanding the business goals for the data collection. Do you want to track user behavior? Do you need to determine the effectiveness of marketing campaigns? Make sure you collaborate among stakeholders in marketing, analytics, and engineering to ensure that the data layer supports your business goals and that it works effectively across platforms.

Focusing on abstraction: As mentioned earlier, abstraction separates data interaction from the data sources. Focus on what data you need for your business, not how to access various tools. For example, you know you need to follow events such as “page views” or “user signups.” Avoid using tool-specific language.

Incremental adoption: Incremental implementation is something agile developers are already familiar with, as it enables a smooth transition and reduces risk. By building the data layer one step at a time, organizations can create a scalable, flexible structure that supports their business needs.

Simulation testing: Simulation testing provides a safe environment for testing potentially risky scenarios, such as high user loads or unusual edge cases.

Fine-tuning: Once you have the basic event tracking in place, you can fine-tune the data layer with more advanced features, such as custom event tracking and automated testing.

Simplify data management with CData Virtuality

An effective data layer that provides valuable insights about your customers requires a robust overall data strategy. CData Virtuality provides data virtualization and replication in a single platform, allowing for seamless integration of your data. With over 200 ready-to-use connectors, CData Virtuality allows you to easily manage your data across your data sources, data storage, and analysis tools. Book a live demo to learn more.