Having worked at several fast-growing tech companies, I've seen firsthand how the complexity of data systems, reporting, and tooling grows exponentially. What starts as a clean stack soon becomes a web of dashboards, overlapping tools, and manual processes. I've even found myself dusting off old SQL skills to create my own reports—often one of the very few business users able to do so. Ironically, that's usually when the data team gives you access: when you're the least maintenance.
But I've also witnessed a more troubling pattern emerge time and again: revenue and growth teams get excited about running new experiments or conducting deep analysis. The data lead nods along, eager to collaborate—until they realize there are simply no resources to support it. These projects inevitably get shelved as "nice-to-haves," and teams revert back to BAU, often relying on Excel exports and stitched-together data to get by.
These aren't isolated anecdotes. They point to a systemic issue plaguing data teams in mid-size technology companies (200–1,000 employees). While these teams are responsible for building and maintaining critical data infrastructure—ETL pipelines, data warehousing, semantic modeling, BI dashboards, data quality, and governance—they're drowning in non-value activities. Industry studies suggest that manual chores like fixing broken pipelines, patching databases, manually updating dashboards, and chasing down errors consume roughly 30–40% of a mid-market data team's time. In essence, up to a third of your data staff's effort becomes "busy work" rather than driving the strategic insights that fuel growth.
The result? Data teams are stretched thin, strategic projects remain perpetually backlogged, and business teams resort to makeshift solutions that undermine the very data consistency and governance these teams work so hard to maintain.
Strong data management and orchestration is vital. Without centralized control and a unified semantic layer, different teams define metrics in their own way, leading to inconsistency and misaligned decisions. This is why tools like dbt, Airflow, and Looker’s semantic models have become so widely adopted. But despite these tools, a huge amount of work remains manual.
Let’s talk numbers!
Take a hypothetical tech company with 500 employees. Data teams typically represent around 3% of headcount in tech companies, so let’s assume a 15-person data team composed of data engineers, analytics engineers, and data scientists.
Assuming a conservative average fully-loaded salary of $120K per person, the team costs $1.8M per year. Now consider that 40% of their time goes to repetitive tasks: fixing broken pipelines, maintaining legacy dashboards, and fulfilling basic reporting requests.
So, just in this single example, a company might spend $2M+ annually on data team salaries and tools, with up to $700K of that lost to routine work.
Modern platforms using agentic AI are changing this. AI-first data tools can now automate up to 70% of those low-value tasks: error resolution, report generation, data lineage tracing, and even natural language querying.
Enterprise studies show that:
The rapid advancements in AI models—from conversational querying to workflow orchestration—are no longer theoretical. With OpenAI, Anthropic, Gemini and Grok pushing boundaries in reasoning, context retention, and automation, the gap between manual and AI-augmented data work is widening at unprecedented speed.
Instead of juggling five tools and maintaining brittle dashboards, teams are moving toward platforms that unify the entire data flow.
This is where solutions like Oraion come in, designed specifically to help mid-sized tech companies maximize the ROI of their data investments.
Oraion helps companies go from raw data to intelligent action by consolidating your entire data workflow in one platform:
In fast-growing companies, there's often a rush to hire more people. But with the AI shift we’re all experiencing, the better question is: how do we give our existing team the bandwidth to deliver real insights?
Our Head of Product wrote a more general piece on how to overcome AI adoption at a company, but if you’re a CFO or CTO at a mid-sized tech company, consider this: automating 70% of your data team's "busy work" could free up $400K–$600K/year of capacity—without hiring anyone new.
That’s the real opportunity. One that I’ve seen go untapped too many times.
It’s time to let your data team do what they were hired to do: help the company grow, not just keep the dashboards alive.