Where Does Your Cloud Data Spend Go to Waste? (and How to Fix It)
Michael Jimmink, MBA
Senior Executive Leader in Life Sciences & Healthcare | Expert in Sales Strategy & Digital Transformation | Driving Innovation with Data and AI
The rise of cloud computing has transformed how organizations store, access, and analyze data, but cloud spend waste is silently draining budgets. Bloated storage tiers, idle analytics clusters, and unmonitored AI pipelines cost companies millions.
The Scale of Cloud Waste in Healthcare
According to Gartner, organizations will spend over $600 billion on public cloud services in 2025, yet?30% of that spend is wasted?on inefficiencies.
The global healthcare cloud computing market is expected to reach $107.25 billion by 2032, growing at a CAGR of 11.06%. Given the significant investments in healthcare, optimizing cloud spending has become a critical priority for organizations
The Top Drivers of Cloud Data Waste
1. Overprovisioned Data Infrastructure
Paying for idle Spark clusters, oversized data warehouses, or GPU instances reserved “just in case”? Overprovisioning is rampant in data-heavy environments.
How Infocion Helps:
Our?AI-driven Resource Rightsizing?identifies underused resources—like overbuilt data lakes or static AI training nodes—and scales them dynamically. One client slashed 40% of their monthly Snowflake spend by rightsizing virtual warehouses.
2. Orphaned Data Services
Unused Snowflake credits, dormant BI tools, or forgotten data lake instances add up fast. These “zombie services” consume budgets without delivering value.
How Infocion Helps:
Our?Cloud Usage Audit?hunts down and eliminates waste, like a healthcare client saving $250k/year by sunsetting abandoned Tableau servers and redundant S3 buckets.
3. Data Egress Surprises
Moving data across regions or clouds (e.g., AI training datasets) triggers hidden fees. One e-commerce company faced unexpected transfer costs of $80k/month.
How Infocion Helps:
We optimize data workflows with?locality strategies?(co-locating analytics engines with storage) and cloud-native caching. For example, we reduced a client’s cross-region transfers by 70% using Azure Front Door caching.
4. Storage Tier Mismanagement
Why pay premium rates for archival data? A fintech client stored 300TB of cold logs on high-performance SSDs—wasting $45k/month.
How Infocion Helps:
Our?Tiered Storage Engine?auto-classifies data (hot/cold/archive) and migrates it seamlessly. We cut another client’s storage costs by 60% using AWS Glacier for compliance data.
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5. Manual Cost Management
Without automation, teams miss savings in real-time.
How Infocion Helps: We deploy?custom automation?to auto-archive cold logs, scale real-time processing jobs, and shut down dev/test environments post-deployment.
Why Choose Infocion?
We go beyond basic cost-cutting with?data-first optimization:
Client Example: A retail giant saved?$1.2M/year?by:
1.??? Rightsizing Redshift clusters (↓42% spend).
2.??? Migrating 200TB of archival data to S3 Glacier.
3.??? Automating real-time analytics scaling.
Turn Data Waste into Fuel
Cloud data spend shouldn’t stifle innovation—it should fund it. Let Infocion help you redirect savings into AI, analytics, or customer-facing tools.
Ready to optimize? Visit?Infocion’s Cloud Data Cost Optimization Page?to see how we transform waste into ROI.
Let's architect the future of your cloud infrastructure – together.
What’s your biggest data cost headache? Share below or message me to brainstorm solutions!
About the Author:
Michael Jimmink, MBA, is a husband, father, amateur podcaster and blogger, big fan of AI, and SVP of Revenue, Healthcare & Life Sciences, working with companies by meeting them where they are and supporting their Digital Transformation Journey.
Michael received his MBA in Marketing from Colorado State University and his bachelor's in biology from UC Santa Barbara.