Cloud analytics vs on-premises BI for ERP reporting: cost, flexibility, and compliance tradeoffs

Cloud Platforms Google Cloud Platform (GCP)
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1

We’re evaluating whether to migrate our ERP reporting infrastructure from on-premises BI tools (currently using a commercial BI platform) to GCP’s cloud analytics stack (BigQuery + Looker). Our current setup handles about 2TB of ERP data with 150+ reports serving 500 users across finance, operations, and supply chain.

The promise of cloud analytics scalability is appealing - no more waiting for hardware upgrades when query volumes spike during month-end close. But I’m concerned about the cost implications of running complex ERP queries in BigQuery versus our current dedicated BI server infrastructure.

There’s also the question of on-prem BI integration. We have years of custom reports, calculated fields, and user training invested in our current platform. A complete migration seems risky, but maintaining a hybrid environment adds complexity.

Finally, our compliance team has questions about data residency and audit trail requirements. Our ERP data includes personally identifiable information and financial records that need to stay in specific geographic regions.

What experiences have others had making this transition? Are there specific ERP reporting patterns that work better in cloud analytics versus traditional BI?

2

Phased migration is definitely the way. Start with your most resource-intensive reports - the ones that slow down your BI server during peak times. Those benefit most from BigQuery’s scalability and justify the migration effort. We prioritized based on: 1) query execution time, 2) user impact, 3) data volume processed.

For report conversion, there’s no magic tool, but you can use BigQuery’s SQL compatibility mode to minimize rewrites. Most standard SQL translates directly. The complex calculated fields are where you’ll spend time - convert them to BigQuery SQL functions or Looker derived tables.

3

The hybrid approach works better than you’d think. We kept our on-prem BI platform for operational reports (daily sales, inventory levels) that users access constantly, while moving analytical workloads (forecasting, trend analysis, data mining) to BigQuery. This gave us the best of both worlds - low latency for operational queries and massive scalability for analytics.

For integration, we use BigQuery Data Transfer Service to sync ERP data nightly. Looker connects to both BigQuery and our on-prem database, so users get a unified reporting experience without knowing which backend they’re hitting.

4

Thanks for the insights. The hybrid approach is sounding more practical. How do you handle the migration of existing reports? We have 150+ reports with complex logic - recreating all of them in Looker seems like a massive effort. Is there any automated conversion or do you recommend a phased migration approach?

5

We made this exact transition last year. BigQuery’s scalability is real - our month-end reporting that used to take 6-8 hours now completes in under 30 minutes. The key is optimizing your data model for BigQuery’s columnar storage. We denormalized our ERP tables and used partitioning by transaction date, which reduced query costs by 60%.

For cost management, set up slot reservations for predictable workloads rather than on-demand pricing. Our monthly BigQuery cost is actually 40% less than our old BI server maintenance and licensing.

6

Looking at the three key focus areas for this decision:

Cloud Analytics Scalability: BigQuery fundamentally changes how you think about ERP reporting performance. Unlike on-premises BI servers with fixed CPU/RAM, BigQuery automatically scales to thousands of parallel workers for complex queries. Your month-end close reports that aggregate millions of transactions across multiple fiscal years will see 10-20x performance improvements. The columnar storage format is optimized for analytical queries that scan large datasets - exactly what ERP reporting needs.

However, scalability requires rethinking your data model. Star schema designs work well. Denormalize frequently joined tables to reduce query complexity. Use partitioning by date (transaction_date, posting_date) and clustering by high-cardinality fields (customer_id, product_id). This optimization is crucial - poorly designed BigQuery tables can actually cost more than on-prem solutions.

Looker adds another scalability dimension through its caching layer and aggregate awareness. Define persistent derived tables for common aggregations (monthly sales by region, inventory turnover ratios) and Looker serves results from cache rather than re-querying BigQuery. This reduces both cost and latency for repetitive reports.

On-Premises BI Integration: The hybrid approach James mentioned is the pragmatic path. Keep your existing BI platform for:

  • Real-time operational dashboards (current inventory, today’s orders)
  • Reports with complex proprietary logic that would be expensive to recreate
  • User communities resistant to change who are highly productive with current tools

Migrate to BigQuery/Looker for:

  • Historical analysis and trend reporting (multi-year comparisons, forecasting)
  • Ad-hoc analysis where users need flexibility to explore data
  • Reports that currently cause performance bottlenecks

BigQuery Data Transfer Service handles the sync between your ERP database and BigQuery. For real-time needs, consider Change Data Capture (CDC) through Datastream. Looker’s federated query capability means one dashboard can combine BigQuery data (historical trends) with on-prem data (today’s actuals) seamlessly.

Plan for a 12-18 month hybrid period. This gives users time to adapt and lets you validate that cloud analytics meets requirements before decommissioning on-prem infrastructure.

Cost and Compliance Tradeoffs: Total cost of ownership favors cloud analytics when you factor in:

  • Eliminated hardware refresh cycles ($50K-$200K every 3-4 years)
  • Reduced DBA/infrastructure staff time (30-50% reduction in our experience)
  • No over-provisioning for peak capacity (month-end, year-end)
  • Pay-per-query model means test/dev environments cost almost nothing

For predictable workloads, BigQuery flat-rate pricing (slots) provides cost certainty. Purchase 500 slots (~$4K/month) and run unlimited queries. This works well for scheduled reports. Use on-demand pricing for ad-hoc analysis.

Compliance actually improves with GCP. BigQuery dataset locations enforce data residency (create EU datasets for GDPR data, US datasets for others). Cloud Logging provides immutable audit trails showing who accessed what data when. Column-level security and dynamic data masking address PII concerns. Cloud DLP automatically discovers sensitive data patterns.

The compliance team should review GCP’s certifications (SOC 2, ISO 27001, PCI DSS) and data processing agreements. Most find GCP’s security posture exceeds on-premises capabilities.

For your 2TB dataset and 500 users, expect monthly costs around $8K-$12K for BigQuery + Looker (assuming optimization), versus $15K-$25K fully loaded cost for on-prem BI infrastructure. The ROI comes from faster insights enabling better business decisions, not just infrastructure savings.