Data cost traps and how to avoid them

Data is no longer just a byproduct of business operations. It’s the fuel driving digital transformation, AI initiatives, and competitive advantage. But as enterprise data strategies mature, so do their price tags. What began as a few cloud tables has evolved into complex multi-vendor architectures that are increasingly expensive to operate, govern, and scale.

Today’s data leaders are under pressure to deliver insights faster while keeping budgets under control. Yet, they overlook the silent drivers of data cost bloat: redundant tools (like maintaining both Looker, Tableau and Hex), zombie tables that haven’t been queried in months, and excessive compute cycles from unoptimized queries.

Worse, cost reduction efforts often fall short when they focus only on budget cuts rather than structural efficiency.

In this post, we’ll explore practical tactics for reducing data costs, and common mistakes to avoid along the way. But first, let’s unpack where the real costs come from in modern data stacks.

The growing cost of data in modern enterprises

Enterprises are ingesting more data than ever, from a broader array of sources and at increasing velocity. This growth brings opportunity, but also expense. The cost of managing modern data infrastructure can be traced back to three primary factors:

• Volume: More data means more storage and more frequent processing jobs
• Variety: Supporting multiple data formats, pipelines, and use cases often leads to tool sprawl and costly duplication
• Velocity: Real-time and near-real-time use cases introduce additional compute strain and complexity. Organizations need strategies to reduce data storage costs while maintaining performance.

On top of these technical challenges, a typical enterprise data warehouse incurs costs across three major components:

• Compute: Query execution, data processing, and transformation jobs
• Storage: Raw, processed, and analytical datasets stored over time
• Data Movement & Activities: Ingestion, ETL/ELT, orchestration, and integration across systems

As companies scale, they also expand their tooling, often adopting best-in-breed solutions for ingestion, transformation, observability, cataloging, lineage, and more. Each tool adds value, but also adds cost. Licensing fees, overlapping functionality, and integration overhead all eat into data budgets.
As these costs compound, data teams are facing a new reality: cost optimization is no longer optional, it’s a core part of operating a modern, trusted data function.

Why cost optimization is no longer optional

Three converging trends have made cost optimization mandatory for every data organization in 2025:

1. Executive scrutiny has reached the data stack

CFOs and boards now expect the same financial discipline from data teams that they demand from every other function. The era of “invest now, measure later” is over. When leadership asks, “What’s driving our Snowflake bill?” or “Where’s the ROI on our 15 data tools?”, you need answers. Without visibility and active cost management, data teams risk losing credibility, autonomy, and budget.

2. Data democratization without cost controls leads to runaway spend

Self-serve analytics is now gaining traction, but it comes with a price. When every analyst can launch compute-heavy queries and every team spins up their own pipelines, costs spiral quickly. Democratization without cost governance is a ticking time bomb. Controls must come before widespread access. The choice is clear: build in cost guardrails, or risk having your self-serve strategy cut off at the knees.

3. Cluttered environment 

Here’s what many miss: cost optimization isn’t just about saving money, it’s about eliminating the hidden costs of inefficiency. Zombie tables, duplicate datasets, and conflicting metric definitions don’t just waste storage: they waste time. Analysts spend hours searching for the “right” data, second-guessing definitions, and troubleshooting broken dashboards. That’s time not spent delivering insights. 

And things only get worse when LLMs enter the picture. AI agents don’t know which version of a dataset is trusted or which metric is deprecated. In messy environments, they produce incorrect outputs, amplify inconsistencies, or simply hallucinate.

So how do you actually bring costs under control?
It starts with visibility. Then comes hygiene, and finally, culture.
The best data teams build systems and habits that prevent waste in the first place. Below are the most effective tactics we’ve seen for cutting costs without cutting value.

Top 5 tactics to reduce data costs

Let’s be honest: nobody got into data to manage budgets. But smart cost management is what separates teams that scale from teams that get their budgets slashed. The good news? Most cost savings come from fixing obvious inefficiencies, not from complex optimizations. Here’s what actually works:

1. Track costs per pipeline, not per invoice

Monthly bills tell you nothing useful. You need to know which specific workflows are burning money. That ETL job that extracts 50GB hourly to power dashboards nobody checks? That dbt model computing metrics “just in case”?

Modern warehouses expose query-level costs – use them! Tools like Euno can map costs to specific pipelines. Once you see that the cost of unused materialized dbt models is over $4k a month, the fix becomes obvious.

2. Use metadata to kill zombie data

Half your tables are probably dead, you just don’t know which ones. Use your metadata catalog to track:

• # of downstream impressions for every column 
• # of queries for every tables 
• Last query date for every table
• Duplicate datasets, metrics, logic  across teams

Schedule monthly “zombie hunts.” One e-commerce company deleted 400 unused tables and cut their storage bill by 30%. Bonus: their analysts stopped getting confused by outdated data.

3. Automate the boring stuff: archival and deletion

Your warehouse is full of data nobody will ever touch again. Dev datasets from Q1, backup tables from that migration, experimental models that didn’t pan out. Reduce stored data through smart archival policies:

• Development data: auto-delete after 30 days
• Staging tables: move to cheap storage after 90 days
• Anything without queries for 6 months: archive it

Snowflake, BigQuery, and Databricks all support automated lifecycle policies. Spend an afternoon setting these up, and save thousands monthly. These automated policies help reduce data storage costs without manual intervention.

4. Make cost everyone’s problem 

Buying another FinOps tool won’t fix cultural issues. Teams that actually control costs do a few things differently:

• Every dataset has someone’s name on it who gets pinged when costs spike
• Deprecation happens on a schedule, not “someday”

5. Fix the queries that hurt your wallet

A single bad query pattern, used everywhere, can double your compute costs. The usual suspects:

Full table scans on massive Bigquerry fact tables (add date filters!) 

Repeated joins that should be materialized

SELECT * in production pipelines

Start with your most expensive queries. Fix the top 10. Repeat every 2 months. 

These aren’t revolutionary ideas. They’re basic hygiene. But most teams are too busy building new things to clean up the old. Schedule time for optimization just like you schedule time for new features. Your CFO will thank you.

Common mistakes to avoid in data cost reduction

Not all cost cutting is smart cutting. Many well-intentioned data teams try to reduce spend, only to create bigger problems down the road. Here are the most common ways cost reduction efforts backfire:

1. Obsessing over storage while ignoring compute costs

Storage feels like the obvious target. It’s easy to measure and easy to cut. But here’s the thing: compute usually costs considerably more than storage. Teams that spend weeks deleting old tables while ignoring inefficient queries are missing the point. That daily aggregation query scanning billions of rows? It probably costs more in a month than storing those old tables costs in a year.

2. Making cost decisions without talking to the people who use the data

Platform teams sometimes go rogue, cutting costs without checking with the folks who actually use the data. Bad idea. The analyst who built that “expensive” pipeline knows why it runs hourly. The data scientist understands which historical data actually matters. Leave them out, and you’ll break critical workflows while saving pennies.

3. Deleting first, asking questions later

Quick way to lose trust: delete a table that seemed unused, then discover it was critical for monthly reporting. Or worse, find out you just violated a compliance requirement. Before you delete anything, run a proper impact analysis:

• Check data lineage to see what depends on this table downstream
• Identify which dashboards, models, or reports would break
• Verify who uses it and when (sometimes critical reports only run quarterly)
• Confirm compliance and retention requirements

Without lineage visibility and impact analysis, you’re flying blind. Cost cutting without homework is just breaking things with extra steps.

4. Making data harder to use in the name of saving money

The fastest way to fail: make cost reduction painful for everyone. Common mistakes include:

• Locking down access so tightly that people can’t do their jobs
• Implementing complex approval processes for basic queries
• Blaming teams for costs without teaching them better patterns
• Turning data teams and finance into enemies instead of partners

Remember: if people can’t use data effectively, you haven’t saved money. You’ve just made expensive data useless.

The goal is simple: spend less while getting more value. That means being thoughtful about what to cut, involving the right people, and keeping the focus on sustainable efficiency, not just this quarter’s bill.

Conclusion

Data cost optimization isn’t a one-time project you can check off your list. It’s an ongoing practice that separates mature data teams from those constantly fighting fires and budget battles.

The good news? You don’t need to tackle everything at once. Start with visibility and cost reduction analysis. Pick one area (maybe those expensive queries) and clean it up. Show quick wins to build momentum and trust. Then expand from there.

Remember: the goal isn’t to spend less on data. It’s to spend smarter. Teams that master cost optimization free up budget for what matters: new initiatives, better tools, and deeper insights. They turn cost management from a painful necessity into a competitive advantage.

Your data stack should grow with your business, not despite it. With the right tactics and mindset, you can build a data operation that’s both powerful and sustainable.

Ready to start? Pick one tactic from this guide and implement it this week. The sooner you begin, the sooner you’ll see results.

FAQs 

How often should we review and optimize our data usage?

Review data usage monthly for tactical improvements (expensive queries, unused tables) and quarterly for strategic decisions (tool consolidation, pipeline deprecation). Set up automated cost alerts for immediate issue detection. Best practice: dedicate the first Monday of each month to reviewing your top 10 most expensive queries.

What tools can help monitor and forecast data spending?

Native tools: Snowflake Resource Monitors, BigQuery Cost Control, Databricks Account Console. Third-party solutions: Euno for cost vs usage mapping and impact analysis, Monte Carlo for data observability costs. Choose tools that integrate with your existing stack and provide both historical analysis and predictive forecasting.

How do I balance data accessibility with cost reduction?

Implement smart defaults instead of restrictions: auto-assign smaller warehouses for ad-hoc queries, set query timeout limits, show cost estimates before execution. Create separate dev/prod environments with different cost controls. Educate users on query costs and provide efficient query templates. The goal: make good behavior easy, not access hard.

How can we ensure data compliance while cutting storage costs?

Map actual retention requirements by regulation (GDPR, CCPA, etc.). Tag data with retention periods at creation. Automate deletion after required retention expires. Use cold storage for compliance data rarely accessed. Maintain deletion audit logs. Remember: most regulations specify maximum retention periods, not minimum. Compliance often means keeping less data, not more.