Your Salesforce org started simple. Clean data, straightforward workflows, everything under control. Fast-forward two years: your development team is spending more time fighting data issues than building features. Storage costs keep climbing. Reports that used to load instantly now timeout. Your latest release got delayed because someone discovered data quality issues during testing—again.
Meanwhile, business stakeholders keep asking for faster delivery while your compliance team adds new requirements every quarter. The platform that was supposed to accelerate development has become a bottleneck.
Salesforce Data Cloud now processes over 250 trillion transactions weekly across thousands of organizations, and every one of them faces the same scaling challenges. The teams that overcome these hurdles architect smart solutions that keep development velocity high while controlling costs and risks.
Here are the five data challenges that consistently slow down Salesforce development teams, and the high-level approaches that successful organizations use to solve them.
1. Uncontrolled Data Growth Drives Up Costs and Degrades Performance
Your org has grown from thousands to millions of records. Storage costs are climbing faster than your project budgets, and what used to be lightning-fast queries now crawl during business hours. Nightly batch jobs are spilling into the workday, delaying analytics and downstream automation.
Meanwhile, every new field, relationship, and custom object adds metadata complexity that makes deployments slower and more fragile. Your development team is spending release cycles optimizing queries instead of shipping features.
The Business Impact
Slower reporting frustrates executives who need real-time insights. Longer deployment windows delay new product launches. Escalating storage costs eat into innovation budgets. Your competitive advantage erodes as technical debt accumulates.
The Solution
Implement intelligent data tiering that automatically moves inactive records to lower-cost storage while keeping frequently accessed data on high-performance systems. Hot data stays readily available for daily operations, warm data moves to mid-tier storage for periodic access, and cold archives shift to low-cost object storage. The key is maintaining seamless user experience; archived data should look and behave exactly like active records through external objects and Salesforce Connect.
2. Disparate Data Formats Create Integration Bottlenecks
Your Salesforce org no longer just handles tidy CRM records. Customer emails, web logs, sensor data, and partner feeds arrive constantly, each in different formats. PDFs pile up in attachments, JSON payloads flood your APIs, and audio transcripts from service calls sit isolated from customer profiles.
Your integration team spends most of its time writing custom parsers and transformation logic instead of building business features. Every new data source requires weeks of mapping work, and schema changes upstream break downstream processes without warning.
The Business Impact
Customer 360 initiatives stall because critical data remains siloed. AI and analytics projects fail because training data is incomplete. New partnership integrations take months instead of weeks, slowing business expansion.
The Solution
Establish a universal data catalog that creates a single source of truth for all data assets, regardless of format or location. This catalog standardizes metadata across systems, automatically maps field relationships, and enables seamless integration without complex custom code. When done right, new data sources integrate in days rather than months.
3. High-Velocity Data Streams Overwhelm Traditional Batch Processing
Modern business moves too fast for overnight ETL jobs. Marketing automation needs customer reactions reflected in seconds, not hours. IoT sensors generate thousands of events per minute. Social media monitoring creates continuous streams that traditional batch processing can't handle.
Your current architecture forces you to choose between real-time responsiveness and platform stability. Streaming approaches hit API limits quickly, while batch jobs create unacceptable delays for time-sensitive business processes.
The Business Impact
Marketing campaigns miss optimal timing windows. Customer service agents lack real-time context during interactions. Inventory management falls behind demand fluctuations. Revenue opportunities slip away while data sits in processing queues.
The Solution
Implement micro-batching that processes small groups of records every few seconds, delivering near-real-time performance without overwhelming platform resources. By buffering events for 2-10 seconds and processing them in optimized chunks, you get the responsiveness of streaming with the reliability of batch operations.
4. Data Quality Failures Cascade Across System Architecture
Poor data quality multiplies faster than your team can fix it. A single formatting error in an upstream system creates thousands of corrupted records downstream. By the time someone notices the problem, it has contaminated reports, AI models, and compliance audits.
Manual data cleanup consumes entire release cycles. Your team fixes symptoms instead of root causes because there's no time for comprehensive solutions. Users lose trust in system accuracy, leading to shadow IT and manual workarounds that create even more problems.
The Business Impact
Executive dashboards show conflicting numbers depending on who generates them. Sales forecasts become unreliable. Compliance audits fail because data integrity can't be verified. Customer experience suffers when service agents work with incomplete or incorrect information.
The Solution
Implement automated validation rules that block corrupted data before it enters your system. These rules should check field formats, enforce completeness requirements, and validate conditional logic in real-time. Establish clear data stewardship with assigned owners for each major data domain, and create governance workflows that catch issues during development rather than production.
How Flosum Helps
Flosum's deployment pipelines include embedded quality checks that scan for validation errors, metadata conflicts, and data integrity issues before code reaches production. This catches quality problems early in the development cycle, reducing validation errors by up to 40% in production releases.
5. Security and Compliance Requirements Outpace Development Capabilities
Every new data source expands your attack surface and regulatory exposure. Processing 250 trillion transactions weekly across cloud environments creates massive compliance challenges. GDPR, HIPAA, SOX, and industry-specific regulations each demand different controls, audit trails, and retention policies.
Your security team struggles to keep up with new threats while maintaining development velocity. Compliance officers ask questions about data lineage that nobody can answer confidently. Manual audit preparation consumes weeks of effort every quarter.
The Business Impact
Security breaches risk customer trust and regulatory penalties. Compliance failures block new market expansion. Audit preparation delays feature releases. Manual governance processes become bottlenecks that slow innovation.
The Solution
Establish comprehensive governance frameworks that automate compliance scanning, enforce end-to-end encryption, and maintain detailed audit trails. Implement role-based access controls with least-privilege principles, and create automated workflows for policy exceptions. The key is shifting security left—catching issues during development rather than post-incident audits.
Solving Data Challenges with Unified Architecture
These five challenges compound each other—poor data quality complicates governance, mixed formats increase security risks, high-velocity streams overwhelm quality controls, and explosive growth amplifies every problem.
Successful organizations address these systematically with tools that solve multiple problems simultaneously. Flosum's platform tackles all five challenges through metadata-aware deployments, automated archiving, embedded quality checks, and enterprise-grade security.
DMI Finance, a leading financial services company, exemplifies how addressing these challenges transforms operations. Before implementing Flosum, it relied on manual Change Sets with no centralized control, creating security risks where anyone with access could move code to production without oversight. The company lacked proper version control for tracking changes and faced constant deployment bottlenecks.
After implementing Flosum, the company achieved a 133% increase in release frequency (from 15 to 35 production deployments per month), enhanced security through granular access controls, improved compliance readiness with built-in change tracking, and reduced deployment risks through rollback capabilities and impact analysis.
Transform your approach with Flosum's automated deployment pipelines: reduce data management overhead while improving release quality and compliance readiness.
