The Pros and Cons of Analytics as Code

"Analytics as Code" is an approach that applies software development principles to analytics. It treats analytics processes, such as building reports, dashboards, and data models, as code rather than static artifacts manually created using GUI-based tools.

This method promotes consistency, efficiency, and automation in analytics workflows by leveraging practices typically found in software engineering.

This approach allows data practitioners to have greater control and flexibility over their data solutions, enabling them to customize workflows to meet specific business needs and objectives. The approach is based on the principles of DevOps and agile software development, where continuous integration, testing, and deployment are emphasized.

The article discusses the benefits of this approach, such as greater flexibility and control, but also delves into the downsides, such as the need for highly skilled practitioners and the potential for increased complexity and costs.

Key Concepts

The Key Concepts of "Analytics as Code" revolve around applying software development practices to analytics workflows. These principles ensure efficiency, collaboration, and reliability in building and maintaining analytics solutions:

1. Version Control:Just like in software development, analytics assets (e.g., SQL scripts, data models, transformations) are stored in version control systems like Git. This allows teams to track changes, collaborate effectively, and roll back to previous versions if needed.

2. Modularity and Reusability: Analytics processes are broken into reusable components, making it easier to maintain and scale. For example, SQL queries, transformation logic, or dashboard elements can be modularized and reused across projects.

3. CI/CD: Continuous Integration (CI) and Continuous Deployment (CD) practices are applied to analytics pipelines. Automated testing and deployment ensure that changes to analytics processes are validated before being deployed to production.

4. Code-Driven Infrastructure: Analytics tasks (e.g., data transformations, model creation, or visualizations) are defined in code rather than GUI-based tools. This can include languages like Python, R, SQL, or specialized configuration files.

5. Testing and Quality Assurance: Automated tests (e.g., data quality checks, consistency tests) ensure that analytics processes produce accurate and reliable results.

6. Collaboration: Teams can work together on analytics projects using collaborative tools and workflows, much like software developers working on a shared codebase.

Examples of Tools for Analytics as Code

1. SQL-Based Tools: dbt (data build tool) allows analytics workflows to be defined in SQL and version-controlled.
2. Python Libraries: Pandas, Apache Spark, or PyTest for data transformation and testing.
3. Workflow Orchestration: Apache Airflow, Dagster, or Prefect for orchestrating analytics pipelines.
4. Version Control: Git for managing changes to analytics code.
5. BI Tools: Some BI tools, like Looker, support code-driven modeling through LookML.

Pros of Analytics as Code

Analytics as code offers several advantages, including:

• Customizability and Flexibility: Writing analytics workflows as code allows for tailored solutions that can adapt to specific needs. Code-based approaches are inherently more flexible than GUI-based tools since they are not restricted by predefined interfaces.

• Reproducibility: Because all analytics processes are defined in code and stored in version control, it's easy to reproduce results consistently across different environments. This is critical for audits, compliance, and collaboration.

• Better Control Over Data Workflows: ith Analytics as Code, every step of the process — from data ingestion, transformation, testing, and deployment — is defined explicitly in code. This provides detailed oversight, making workflows easier to manage, audit, and troubleshoot.

• Greater Access to Code Libraries and Third-Party Packages: Using programming languages (e.g., Python, SQL) for analytics grants access to a rich ecosystem of libraries (e.g., pandas, numpy, dbt, etc.) and third-party tools that can accelerate development and add advanced functionality.

• Improved Scalability: Analytics as Code integrates well with cloud computing and DevOps practices (e.g., CI/CD pipelines). This allows organizations to scale analytics workloads up or down efficiently using automated workflows and infrastructure-as-code principles.

• Cost-Effectiveness (in some cases): Building custom solutions using Analytics as Code can be cost-effective in some cases, especially if a pre-built tool does not exist for a particular use case, or if an organization has highly specific requirements that cannot be met with pre-built tools.

Cons of Analytics as Code

While analytics as code offers several benefits, it also comes with some significant drawbacks that should be taken into consideration:

• Need for Highly-Skilled Data Practitioners: The Analytics as Code approach requires data practitioners who are proficient in coding languages such as Python, R, and SQL. This means that organizations need to invest in hiring or upskilling data practitioners so they have the necessary skills to use this approach effectively.

• High Cost of Ownership: Managing multiple tools for version control, CI/CD pipelines, testing, and orchestration can lead to increased costs, including licensing fees, infrastructure expenses, and the time required to maintain and support these tools.
• Integration Challenges and Technical Debt: Setting up version control, CI/CD pipelines, and other infrastructure for Analytics as Code requires significant effort, particularly in integrating multiple tools and ensuring they work seamlessly together. Poorly integrated systems can lead to technical debt, making future updates, troubleshooting, and maintenance more complex and time-consuming.
• Time-Consuming Manual Processes: The Analytics as Code approach can be highly time-consuming, particularly during the initial setup. Data practitioners must write, test, and debug code for each stage of the pipeline, which can significantly delay the delivery of insights. This added complexity and development time can be a disadvantage when quick, ad-hoc analyses or rapid solutions are needed.

• Complexity in Management and Scaling: Managing codebases, dependencies, and deployment pipelines introduces significant complexity. As projects grow, keeping everything organized and up-to-date becomes increasingly challenging. Without disciplined practices, this can lead to inefficiencies, delays, and difficulty in maintaining smooth operations.

• Potential for Errors and Bugs: Writing code manually introduces the risk of errors and bugs. Without rigorous testing, these errors can lead to inaccurate insights. Automated testing and validation processes are essential to mitigate this risk, but require additional tools which drives up costs.

The Role of Analytics as Code in Widening the Data Divide

The Data Divide refers to the gap between those who can effectively leverage new data and analytics technologies, and those who cannot. This divide has become increasingly pronounced in recent years, as the volume, variety, and velocity of data continue to grow.

Organizations with substantial resources, skilled personnel, and robust infrastructure can leverage Analytics as Code to streamline workflows and drive innovation.

However, organizations already struggling with data management challenges — such as handling large data volumes, complex infrastructures, or a shortage of technical talent — may find Analytics as Code amplifies their difficulties. The need for coding skills, version control, CI/CD pipelines, and additional tooling adds layers of complexity that can become overwhelming.

For these organizations, adopting Analytics as Code can result in slower development, increased costs, and limited accessibility. This adds another barrier to entry, preventing less-resourced teams from fully leveraging their data and deepening the Data Divide.

What Are You Optimizing For?

"What are you optimizing for?" is a crucial question that organizations must ask themselves when considering which data and analytics approach to take.

The stakes are extremely high, as the wrong approach can result in wasted time and resources, missed opportunities for innovation and growth, and being left on the wrong side of the Data Divide.

So, are you optimizing for...

• New technology trends?

• A fragmented tool stack?

• Highly-customized code?

• DevOps frameworks?

• Ingrained habits?

• Organizational momentum?

• Gaining marketable skills?

You can’t optimize for everything, all at once.

If you choose to optimize for fragmentation or customizability, you will be forced to make big sacrifices in speed, agility, and efficiency.

At the end of the day, it’s not the organizations with the most over-engineered data stacks or the most superhuman coding abilities that will succeed.

While having an optimized tool stack and skilled coders are certainly beneficial, it is important to remember that the ultimate goal of data and analytics is simply to drive business value.

“The Modern Data Stack ended up solving mostly engineering challenges related to cost and performance, generating more problems when it comes to how the data is used to solve business problems.

The primary objective of leveraging data was and will be to enrich the business experience and returns, so that’s where our focus should be.”

– Diogo Silva Santos, Senior Data Leader

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