Analytics as Code (AaC): How GoodData is Shaping the Future of Data Analytics

The demand for streamlined, automated, and scalable analytics has never been greater. Why? As data volumes and complexity continue to skyrocket, traditional analytics processes are struggling to keep pace.

One transformative approach gaining traction in the analytics and business intelligence world is Analytics as Code (AaC). This methodology treats analytics components such as data pipelines, transformations, and visualizations, as well as platform administration tasks such as environment building, management, and deployments as code. This revolutionizes how organizations manage and leverage their data; by embedding analytics within a code-centric framework, AaC empowers teams to accelerate development cycles, ensure consistency, and enhance collaboration across the organization.

What is Analytics as Code and why does it matter?

AaC applies software engineering principles to analytics processes. By managing analytics assets and entire environments as code, organizations enhance control, collaboration, and efficiency throughout the analytics lifecycle. Here’s why AaC stands out:

• Efficiency and automation: Automating repetitive tasks and workflows minimizes manual intervention, accelerates development, and reduces errors.
• Version control: Version control and code repository systems ensure that there’s a logged history of all data definitions and changes made, when, and by whom. This enables democratization and understanding of metrics while also providing a system of data governance.
• Scalability: AaC enables rapid scaling of analytics capabilities, by mixing and matching different composable elements for new purposes.
• Accuracy and quality: Automated testing and validation along with change logs help maintain high data quality and analytics reliability.

GoodData’s role in advancing Analytics as Code

GoodData is leading the way in adopting and implementing AaC by offering a robust platform designed to transform raw data into actionable insights with greater speed and precision. Here’s how GoodData supports the Analytics as Code approach:

• Comprehensive Toolset: GoodData facilitates a comprehensive analytics lifecycle through robust integrations and partner tools, covering data integration, modeling, visualization, and embedding. This approach leverages these integrations and partnerships to provide a flexible and controlled process, managed through code.
• Streamlined processes: GoodData’s platform supports the automation of key analytics tasks, including data refreshes and user creation. This automation reduces manual effort and enables rapid scaling, ensuring that analytics keep pace with business needs.
• API-first architecture: Built with an API-first approach, GoodData ensures seamless integration with other tools and systems. This architecture supports the modularity and interoperability essential to the AaC methodology.
• Collaboration: GoodData facilitates collaboration by providing a modular data app development platform. Data teams can work together more effectively, maintaining consistency across projects and reducing the risk of errors.
• User-friendly interface: While GoodData supports advanced AaC features, it also offers an intuitive interface, making it accessible to users with varying technical expertise. This balance ensures organizations can leverage AaC without requiring extensive coding skills across the board.

A real-world case study: enhancing efficiency with AaC at GoodData

The effectiveness of AaC can be demonstrated through a real-world situation. For example, consider our work with Mews, a B2B hospitality software company, that needed a scalable solution to manage its rapid growth and deliver financial and operational analytics to its customers. With over 5,000 properties and multi-property management complexities, Mews faced challenges in ensuring data consistency, efficient pipeline management, and customizable reporting across different time zones.

Mews implemented GoodData’s AaC functionality to automate data pipelines and embed insights directly into their existing hospitality management software. This integration enabled Mews to offer consistent, customizable analytics to their customers while improving operational efficiency. The GoodData platform also supports multitenancy, enabling Mews to provision and manage data for each customer centrally, while allowing for customizations.

Mews’ results:

• Reduced manual effort: The automation of data pipelines and the adoption of AaC reduced manual intervention and the associated risks of human error, ensuring data consistency across the board.
• Effective scalability: Mews can now seamlessly roll out new products and capabilities, offering customers a composable analytics approach that scales with their business needs.
• Improved team alignment: By embracing AaC, Mews enhanced communication and collaboration across teams, enabling effective experimentation and faster time-to-market for new features.

Could it be done manually? I don't think so. At this scale, we would very much struggle to support all the requests, onboarding, and development without an Analytics as Code approach.

Vojta Kopal, Director of Engineering, Data Science at Mews

Understanding the AaC maturity model

To fully appreciate Analytics as Code, it’s useful to understand its maturity stages and how GoodData aligns with them:

• Adoption: Analytics are typically managed through basic GUI-based tools with limited development integration. GoodData enhances this stage by offering robust code-based integration, providing foundational support for scalability and flexibility.
• Exploration: At these levels, organizations adopt advanced methodologies such as API-first architectures, domain-specific languages (DSLs), and sophisticated version control. GoodData supports these needs with a comprehensive suite of tools that facilitate deeper integration and management of analytics processes.
• Full lifecycle integration: This represents the ideal state where analytics are seamlessly integrated throughout the entire development and deployment lifecycle. GoodData is paving the way toward this future by offering a fully integrated platform that supports continuous analytics delivery and adaptation.

Why now is the time to adopt AaC

The benefits of adopting Analytics as Code are substantial:

• Future-proofing your analytics: As data complexity grows, traditional methods become less effective. AaC offers a scalable, adaptable solution that evolves with your business.
• Maturity of AaC methodology: Infrastructure as Code (IaC) and Policy as Code (PaC) revolutionized IT and became the standard once the models had matured. Similarly, Analytics as Code is now hitting a point where it’s being accepted as a preferred approach by innovative analytics and product teams.
• Optimizing AI and ML integration: AaC offers a robust framework for managing the entire lifecycle and seamless integration of AI and ML models. It simplifies the integration of data pipelines and model deployments, accelerates iterative development, and supports real-time updates, ensuring your AI and ML initiatives are agile, accurate, and aligned with evolving business needs.

Try it with GoodData

Analytics as Code represents a key advancement in data analytics, delivering improvements in efficiency, accuracy, and scalability. GoodData’s platform aligns with AaC principles, offering tools that streamline analytics processes and support informed decision-making. With GoodData, you can enhance your analytics capabilities, accelerate insight generation, and stay competitive in a data-driven world. To try it for yourself, sign up for a 30-day trial.