Common challenges to scale data operations

This is a guest post by Dakota Kelley, senior solutions architect at phData

Scaling data operations within an organization is no small feat. As teams grow, processes become more complex, and the volume of data will expand exponentially. This often leads to several challenges that will slow down your progress and create massive challenges.

Here are some of the most common roadblocks that companies face when trying to scale.

Lack of standardization

One of the most significant hurdles to scaling is the absence of consistent standards across modeling, development, and processes. When each team or individual operates under a different set of rules or lacks clear guidelines, workflows can vary wildly across the organization. This lack of standardization makes it difficult to collaborate, track progress, and ensure the quality of data outputs.

This results in disjointed efforts, duplicative work, and errors that could easily be avoided with a standardized framework. Often leading to fatigue and burnout in your data team.

Unclear ownership

Another common issue is the absence of a clear owner for data initiatives. In many organizations, overwhelmed data teams are tasked with handling an influx of requests and projects. However, with no designated responsibility for overseeing specific datasets or processes, the data produced may not fully meet the needs of the requesters.

This often leads to a cycle of finger-pointing between the data team and business stakeholders, as both groups avoid taking responsibility for the outcome. The lack of accountability stalls progress and can undermine ‌trust between teams.

Inefficient workflows

Without streamlined workflows, productivity can take a major hit. Inefficiencies in the way tasks are handled lead to delays in decision-making and increase the likelihood of errors. Moreover, these slowdowns reduce organizational agility, making it harder to respond to market changes or customer needs in real-time. Inefficient workflows also contribute to employee frustration, which in turn impacts team morale and performance.

Minimal operational oversight

Operational oversight is crucial for identifying issues and addressing root causes, yet many organizations struggle to implement it effectively. When metrics aren't tracked, or issues go unmonitored, it becomes nearly impossible to diagnose problems, let alone prevent them in the future.

This lack of insight hinders the ability to perform meaningful analysis, which is critical for driving continuous improvement. Without operational oversight, organizations miss out on valuable opportunities to optimize processes and enhance performance.

Embrace the ADLC

Successfully scaling data operations requires addressing these common challenges head-on. Which is done best by embracing the Analytics Development Lifecycle (ADLC).

Set and enforce standards

“You should shoot for high standards and believe they’re obtainable.” - Buster Posey

Business impact

Establishing clear standards and codifying processes can significantly improve operational efficiency and drive meaningful business impact. By creating a unified framework, organizations can reduce redundancy, streamline workflows, and eliminate unnecessary rework.

This structure not only enhances productivity but also makes onboarding and task transitions smoother for employees. This means that teams can quickly adapt and contribute to new projects.

Moreover, the collaborative nature of developing standardized solutions fosters cross-team alignment, accelerates development cycles, and often results in higher-quality outcomes. In short, standardization is a key enabler of scalability, innovation, and long-term success.

Technical best practices

Implementing standards in a repeatable way that can drive true business impact requires a variety of technologies. Tools like SqlFluff help enforce SQL style and coding standards, ensuring that queries across teams follow consistent, maintainable formats.

Meta-testing further enhances this by setting conventions for naming, testing, modeling, and documentation, making it easier to collaborate and review work across the organization.

A well-structured Git workflow, paired with pull request templates, streamlines the development process by encouraging thorough code reviews and reducing the chances of errors.

Finally, by standardizing the CI/CD pipeline, teams can automate deployment and testing, reducing manual intervention and improving overall efficiency. Together, these best practices not only improve code quality but also accelerate development and ensure scalability.

Provide clear ownership

“No one can come and claim ownership of my work. I am the creator of it, and it lives within me." - Prince

Business Impact

Clearly defining roles within data operations enhances governance by minimizing the risk of unauthorized changes and ensuring that the right people have control over critical data processes. By assigning subject matter experts (SMEs) with approval authority and ownership, organizations can foster workflow stability and robustness, ensuring that decisions are made by those with the most expertise.

Furthermore, adopting a data mesh approach empowers individual teams to own their data domains, promoting greater collaboration and efficiency. This decentralization allows teams to work autonomously, improving productivity while maintaining alignment across the organization utilizing cross-project references. Together, role clarity and a data mesh strategy drive both operational resilience and business agility.

Technical best practices

Establishing the appropriate processes, ownership boundaries, and hand-offs requires us to utilize our technology beyond the basic features. Using Codeowners within a Git repository ensures that specific teams or individuals are assigned ownership, making it clear who is responsible for approving changes. A well-defined organizational structure for Git workflows and approval processes further enhances accountability and streamlines development.

This same principle should be applied to dbt projects. Establishing governance and ownership within teams ensures that models, tests, and documentation are consistently maintained and improved in a way that makes it easy to enforce with code ownership.

By embracing data mesh—scaling horizontally with clear roles, structure, and standards—organizations can expand their data capabilities efficiently without sacrificing quality or collaboration. This creates a strong foundation for long-term scalability and success.

Establish operational excellence

“Watch the little things; a small leak will sink a great ship” - Benjamin Franklin

Business impact

Improving operational efficiency requires a proactive approach to addressing recurring issues at their source, preventing them from resurfacing and disrupting workflows.

By continuously tracking key metrics such as warehouse sizes, model run-times, and compute usage, organizations can optimize costs, ensuring resources are used efficiently without unnecessary overspend.

Additionally, identifying patterns in data operations allows teams to update processes and standards regularly, ensuring high-quality output is maintained while mitigating potential issues before they escalate.

This ongoing refinement not only drives cost savings but also boosts productivity and overall business performance.

Technical best practices

dbt artifacts, which drive the Discovery API and dbt Explorer, offer invaluable observability across all dbt projects, allowing teams to monitor performance, spot patterns, and detect problems across the organization. By leveraging these insights, teams can conduct rigorous root-cause analysis to swiftly identify and resolve recurring issues.

This level of visibility is crucial for understanding the financial impact of technical decisions, particularly when it comes to concurrency and scheduling—factors that can unknowingly drive up cloud spend.

By proactively addressing these inefficiencies, organizations can not only maintain operational stability but also optimize their cloud usage and reduce costs, ensuring smarter and more scalable data operations.

dbt has a new Advanced CI feature that lets you see how code changes affect your data better. It does this by comparing the last production state to the latest pull request commit. It allows teams to see changes in primary keys, rows, and columns, either directly in dbt Cloud or through Git comments. Helping teams avoid introducing changes that result in major breakages or loss of data. Ensuring trustworthy data products with efficient operations.

Excellence is a journey

It’s time to collaborate, empower individuals, and focus on continuous improvement through innovation.