In today’s fast-moving and highly regulated insurance landscape, data monitoring for insurance is no longer a nice-to-have, it’s mission critical.

Whether you’re trying to reduce operational drag, stay compliant with evolving regulations or enable trusted AI models, one thing is clear: if your data is wrong at source, everything downstream suffers.

Yet despite this, many specialty insurers still rely on outdated or incomplete methods to manage data quality and it’s costing them more than they think.

So, are you a classic non-DQPro-er?

Let’s find out!

Why Data at the Source Matters

In specialty insurance, front-line data is where it all begins and where it often starts to go wrong.

When errors go undetected at the point of entry, they ripple through pricing, compliance, claims, reinsurance and reporting. Small gaps in critical data elements such as policy details, premium fields and key industry risk or event codes can result in:

• Weeks of rework for Ops
• Missed reporting deadlines
• Regulatory breaches
• Errors in capital modelling
• Reduced reinsurance recoveries
• Delayed AI adoption

With so much at stake, it’s no surprise that daily data monitoring for insurance is now a top priority for high-performing carriers.

The Three Types of Non-DQPro-ers

If you’re not using a purpose-built front-line data quality solution like DQPro, chances are you fall into one of these three camps.

1. The Spreadsheet Juggler

Also known as “Exception Report Enthusiasts”

You know the drill: IT runs a batch job overnight. A list of data anomalies is exported, packaged into an Excel file and emailed to Ops the next day or week.

What could go wrong? Quite a bit, actually.

• The format is inconsistent
• There’s no context or workflow
• Fixes depend on whoever has time
• The process is slow and reactive
• Ops teams burn out after 20–30 reports
• No audit trail or visibility for compliance teams

This is human-powered data correction – heroic, but inefficient. And it was never built to scale.

2. The Overengineered Analyst

“We’ve got tools, but no one uses them”

This camp usually has a data engineering team with access to heavy-duty platforms. Dashboards are generated, anomalies are flagged and reports exist somewhere in a portal.

But when it comes to frontline Ops?

• No real-time alerts.
• No embedded workflows.
• No visibility into regulatory fields.
• No feedback loop.

The tools may be powerful but if they aren’t embedded in your operational process, they’re not driving daily decisions.

Data monitoring for insurance isn’t just about surfacing errors – it’s about making sure the right people fix the right issues at the right time.

3. The Free Spirit

“We’ll fix it later, if it breaks”

Yes, this group still exists. Some carriers are knowingly flying blind – collecting front-end data with little to no validation, hoping any errors will be caught before serious damage is done.

This approach is:

• Temporarily easy
• Immediately cheap
• Long-term, dangerous

You might dodge a few bullets… until you don’t.

Like the £9.6M PRA fine one top-10 carrier faced in 2022 after governance and reporting failures made headlines. Or the remediation costs quietly swallowing up BAU budgets behind the scenes.

For modern insurers, doing nothing is no longer viable. The cost of reputational damage and regulatory failure is simply too high.

So What Does “Good” Look Like?

For insurers leading the pack, data quality isn’t an afterthought. It’s an embedded process. And it starts right at the front line.

These carriers understand that data monitoring for insurance must:

• Be real-time, not monthly
• Be Ops-facing, not buried in IT or behind a technical UI
• Flag priority fields, not generic data points
• Enable workflow, audit, and clear ownership, not confusion
• Meet market-specific regulations (e.g., Lloyd’s reporting, DA compliance)

That’s why nearly half the Lloyd’s market and a growing list of global carriers now use DQPro – a solution built for insurance Ops teams, not just data professionals.

The Shift Is Already Happening. The question isn’t whether the market is changing, it’s whether you’re changing with it.

So ask yourself:

Are we still juggling spreadsheets?
Are our Ops teams flying blind?
Are we relying on tools foisted on us by a technical team that no one actually uses?

Or are we finally treating data quality as the operational priority it really is?

If you’re ready to stop winging it and start scaling data confidence across your business, it’s time to explore a platform designed for specialty insurance – from front line to boardroom.

Learn how DQPro makes data monitoring for insurers fast, scalable and future-proof – book a demo today!