It's essential for modern data teams to bring software development lifecycle (SDLC) practices into BI. As companies grow, so do their dashboards, data models, and reporting needs — introducing complexity, risk, and the constant challenge of keeping everything running smoothly. Unlike software engineering, where structured development workflows ensure reliability, BI has historically lacked the same rigor.
This is why our team has prioritized building robust SDLC controls directly into Omni: to give data engineers and analysts the tools they need to manage their BI deployments effectively.
And here’s the thing: Not every change in BI needs a heavy SDLC process. Some analyses need to move fast, without multiple review steps or complex workflows. In Omni, SDLC practices can be as lightweight as our built-in draft/publish workflow and model history, or as rigorous as our git integration. The key is being able to apply SDLC controls where it makes sense.
In this blog, I’ll share why SDLC is crucial to operating BI at scale. In a follow-up blog, one of Omni’s solutions engineers, Conor, will dive into Omni’s SDLC controls in more detail.
Why data teams need SDLC #
SDLC is a well-established framework in software engineering. Developers don’t push code changes directly to production — they work in branches, test their updates, and roll them out in controlled deployments. In contrast, BI tools have traditionally allowed users to edit reports, models, and metrics live, with little structure around how those changes are made. Make one small tweak to a key metric, and suddenly, reports across the company could break, leading to confusion, bad decisions, and a frantic scramble to fix things. This impact can be even greater if the reports in your customer-facing analytics break.
The reality is that data teams already function as software engineers in many ways. They write code, build pipelines, and deploy changes that impact an entire organization. But without proper SDLC controls, they have to manage change through manual workarounds, fragmented processes, and reactive firefighting.
The costs of BI without SDLC #
Other BI tools may make it easy to run analyses and create dashboards, but they often don’t make it easy to manage that content at scale. Without proper SDLC practices in the BI layer, data teams face several major challenges:
1. Lack of safe development environments #
In software engineering, every change starts in a development environment before being merged into production. This ensures that new features or updates don’t break existing functionality.
Most BI tools are just one big development and production environment combined. Changes are often made directly to live reports – sometimes, even customer-facing ones 😅. Or, at best, tests are done in copies of dashboards that need to be maintained and quickly become outdated. This leads to:
Accidental disruptions, where updates break existing reports.
Fear of making changes, since one small tweak could cascade into larger issues.
Longer turnaround times, as teams create and maintain multiple versions of reports instead of safely iterating in one place.
2. No version control or auditability #
A software engineer can roll back a bad release with a single command. Data teams, on the other hand, often have no way of tracking or undoing changes to a dashboard or data model.
Without version control, teams struggle with:
Tracking who changed what and when — creating confusion when reports suddenly look different.
Reverting mistakes — if a key metric is modified incorrectly, there’s no easy way to restore it.
Lack of accountability — since it’s difficult to pinpoint the source of errors.
This is especially painful in high-stakes reporting environments like finance or operations, where even minor discrepancies can have major business implications.
3. Siloed change management across the data stack #
Modern data stacks are fragmented. A data pipeline might be managed in an ETL tool, transformations happen in dbt or the warehouse, and reports exist in a BI tool. But these systems don’t always communicate with each other.
This disconnect creates problems like:
Changes in one system breaking another (e.g., a dbt model update breaking a dashboard).
Time-consuming coordination between teams, since there’s no single source of truth for how changes affect downstream analytics.
Delayed insights, as data teams spend time debugging instead of delivering value.
In software engineering, continuous integration and deployment (CI/CD) ensures that changes are tested and deployed holistically, across the stack. BI needs the same kind of system-wide coordination to prevent changes from introducing unexpected issues.
4. The pressure of 100% uptime #
Data teams operate in a high-visibility environment. A broken dashboard, inaccurate metrics, or delayed response times can immediately impact decision-making across the company. In the case of customer-facing reports, downtime erodes customer trust.
This leads to:
Constant firefighting, as teams scramble to fix broken reports.
Frustration among leadership, when key metrics appear inconsistent.
Stress and burnout, as analysts and engineers feel pressure to maintain perfect uptime.
Today, data-driven decision-making is the norm. Data teams need more than just reporting tools — they need robust development workflows that ensure reliability, scalability, and peace of mind.
BI development as software development #
The software development lifecycle is more than just a set of technical processes—it’s a way to build with confidence and control. In BI, adopting SDLC principles means:
Using branches and staging environments to safely test updates.
Implementing version control to track and roll back changes.
Integrating BI with the broader data stack for seamless change management.
Reducing fire drills by proactively preventing errors before they reach end users.
Data teams need the same structured workflows that software engineers rely on. By bringing SDLC into BI, data engineers and analysts can move beyond reactive firefighting and into scalable and reliable reporting, analysis, and decision-making for the business.
Soon, we’ll dive into how you can use Omni’s SDLC controls to manage your data effectively. I’d love to hear your thoughts on the intersection of SDLC, software engineering, and BI – send me a note at chris [at] omni [dot] co.