In today’s enterprise environments, Large Data Volumes (LDVs) are both an asset and a threat. On one hand, historical records provide valuable insights; on the other, they can cripple system performance, slow down user experiences, and create compliance risks if not properly managed.
For businesses operating on Salesforce, especially in industries like financial services, maintaining the right balance between performance, scalability, and data intelligence is critical.
That’s why integrating solutions like Own Company’s Data Archive and Backup — alongside Salesforce Data Cloud — represents a game-changing strategy.
The Challenge with Large Data Volumes in Sales Cloud
Salesforce Sales Cloud was built for action — not as a long-term warehouse for millions of historical transactions.
Financial services firms, for example, often accumulate decades of client transaction records, loan applications, payment histories, and communication logs. Over time, this data slows down search, impacts reporting, bloats storage costs, and increases API query times.
In critical sectors like short-term lending, these records aren’t just clutter — they represent active risk exposure.
Imagine a lender that experiences a 6% default rate across hundreds of thousands of small business loans. Even a small spike in default rates could equate to millions of dollars lost.
The problem? Without full historical data visibility, traditional CRM reporting alone could miss early warning signs hidden in patterns across both new and archived data.
The Solution: Own + Data Cloud
By archiving stale or seldom-used records out of Sales Cloud with Own Company’s solutions, businesses offload the strain from operational CRM environments — without losing access to critical information.
When integrated with Salesforce Data Cloud, that archived data becomes instantly actionable again:
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Archived records remain fully queryable in Salesforce reports and dashboards.
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Data Cloud harmonizes live and archived datasets, enabling holistic AI-driven analytics.
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Applications like AgentForce (built on Salesforce Data Cloud) can scan across the entire history of an account — not just what’s still stored in the core CRM.
With this architecture, a lender can now detect common denominators among defaulting accounts — before loans are funded. Patterns like:
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Certain geographies or industries underperforming
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Specific transaction anomalies or payment delays
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Historical customer engagement drop-offs
Likewise, the firm can double down on high-performing accounts, identifying the behaviors and characteristics that correlate to success, loyalty, and repayment.
Result:
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Millions saved in potential losses
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Better underwriting decisions
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Faster operational reporting
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Reduced manual analysis work across teams
Why This Matters Now
The financial services sector — and really, any data-heavy industry — can no longer afford to treat historical data as an afterthought. It’s not enough to just store information; companies need to activate it, analyze it, and act on it — all without burdening core systems.
Salesforce Data Cloud paired with Own Company’s data archive enables organizations to:
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Scale CRM performance without losing reporting fidelity
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Improve decision-making by analyzing complete datasets
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Lower compliance and security risks through smarter data governance
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Empower AI and automation initiatives by feeding richer, more complete data models
Conclusion
Data without action is just noise.
By combining Own’s Data Archive with Salesforce Data Cloud, organizations can ensure that even their oldest records remain a source of insight, opportunity, and risk mitigation — not technical debt.
It’s a true future-proof model for enterprises that recognize the value of their full history — and want to build smarter businesses because of it.
