The Pros and Cons of Analytics as Code

Analytics as code is an approach to data management and analytics that emphasizes the use of coding languages, rather than pre-built tools, to create custom data workflows and solutions.

It 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.

Pros of Analytics as Code

Analytics as code offers several advantages, including:

• Customizability and flexibility: With the ability to write code for data workflows, analytics practitioners can build highly customized solutions that fit their specific needs and specifications.

• Better control over data workflows: Analytics as code provides more control over the entire data workflow, from data ingestion to deployment, as well as increased visibility and transparency into each step of the process.

• Greater access to code libraries and third-party packages: Using code for analytics opens up access to a vast array of code libraries and third-party packages that can be used to accelerate development and enhance functionality.

• Improved scalability: Analytics as code enables organizations to scale their data analytics up or down as needed, using cloud computing resources and DevOps tools to automate the process.

• 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.

The 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 skilled data practitioners who are proficient in coding languages: 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 training data practitioners who have the necessary skills to use this approach effectively.

• Steep learning curve: Because analytics as code requires proficiency in multiple coding languages and DevOps practices, it has a steep learning curve. It can take a considerable amount of time and resources for data practitioners to become proficient in using this approach.

• Increased complexity and maintenance: Analytics as code can make data workflows more complex, as there are multiple codebases to manage, test, and deploy. This can make maintenance and troubleshooting more difficult, which can lead to delays in delivering insights and solutions.

• Lack of user-friendly interfaces: Analytics as code does not offer the same level of user-friendliness as pre-built tools with graphical user interfaces. This means that less technical users may struggle to use this approach effectively, which can lead to a lack of adoption and suboptimal results.

• Time-consuming process: The analytics as code approach can be time-consuming, as data practitioners need to write and test code for each stage of the data workflow. This can make it difficult to deliver insights and solutions quickly.

• Potential for errors and bugs: Writing code manually increases the potential for errors and bugs, which can lead to inaccurate insights and solutions. It requires a rigorous testing and validation process to ensure the code is correct.

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 technologies, and those who cannot.

While some organizations have the resources, expertise, and infrastructure needed to fully leverage the potential of data, others struggle to access, store, manage, and analyze their data, which puts them at a significant disadvantage in the marketplace.

The Data Divide has become increasingly pronounced in recent years, as the volume, variety, and velocity of data continue to grow.

If an organization is already struggling with the complexities and challenges of the modern data stack, adding the manual coding required by the "analytics as code" approach (along with additional tools for managing the development, testing, and deployment of this code), will only make things considerably worse.

The end result is even more complexity, exclusivity, and gatekeeping in the data industry, which only serves to exacerbate 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 data 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 data 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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TimeXtender is not the right tool for those who want to spend their time writing code and maintaining a complex stack of disconnected tools and systems.

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