The Impact of Data Quality on Operational Risk Management

Data is driving everything your business does. From tracking the number of hits to your website to making correlations based on historical silos to predict future growth, the world revolves around data. The problem is a single typo in this information, or a supplier with outdated contact details can cause a ripple of errors across your systems – often leading to compliance issues or lost revenues. 

There is a direct link with data quality on operational risk. The lower the data quality, the higher the risk of fees, noncompliance, errors, PR disasters, and so much more. To remain competitive, your business or organization must understand how to craft resilient, clean data stores for gathering, analyzing, and acting on the information you need.

What Is Data Quality?

The point of good data quality rests in its accuracy, completeness, consistency, reliability, and timeliness. You must have complete faith that your data is correct, complete, and can be trusted whenever a new challenge crops up. 

Data is collected from a variety of sources. This information has to be carefully scrubbed and separated for proper usage. Most businesses record data from: 

• Customer Transactions & Accounts
• Sales Records
• Financial Records
• Website Logs & CTR (click-through rates)
• Social Media Interactions
• B2B Sales & Communication
• Entered Software Information

Think about a boutique hotel. They will collect customer data for payment and future marketing, residency reports to judge if they need more employees or marketing, and expense reports for profit gauging. However, there is also information about what is served in the hotel bar and snow reports so guests can go skiing nearby. If you run a business, you need quality data. 

Flawed analytics skew forecasting and misaligning your information with your goals. That increases the chances of operational risk. If a bank looks at a borrower’s profile incorrectly due to an error in a dataset, it can be out millions when that borrower doesn’t fulfill an obligation.

Understanding Operational Risk in the Context of Data

Data quality on operational risk comes down to failing internal processes, systems, people, or external events. Data is often the invisible line connecting these categories. 

IT ecosystems are growing more and more complex. New AI, automation, and SaaS tools force businesses to update quickly or risk losing market share. If the data quality is sacrificed in this process, risk management alarm bells should go off immediately. 

Imagine not having good data for inventory management or regulatory reporting. The legal, financial, reputational, and operational damages could sink a business – all because of some missing or erroneous pieces of information.

How Poor Data Quality on Operational Risk Exacerbates Exposure

Context is a good aid in understanding operational risk management. Data will contribute to systemic failures without the right sets of quality assurance measures in place. Some common risks include: 

• Compliance & Audit Readiness: Roughly 70% of organizations need to demonstrate compliance with more than six categories of regulatory bodies. Everything from financial compliance to environmental and operational are included. Without this compliance, the organization may face significant fines, long-lasting sanctions, and even criminal penalties. 
• Fraud Detection & Mitigation: Fraud costs businesses across the globe roughly 5% of their annual revenue in losses. Incomplete or corrupt datasets compromise your daily systems. Suspicious activity can then sneak in and be written off as “par the course” instead of something preventable. 
• Business Continuity Planning: Poor data quality on operational risk will delay response times. It slows your ability to remain agile and harms resource allocation because the crucial warning signs you need are not triggering due to insufficient data integrity. 
• Internal Controls & Reporting: The most critical operational risk of poor data is reporting. You cannot make better decisions without knowing the information you need. Faulty assumptions lead to “gut instincts,” which have no basis in factual or rational growth. You often won’t know if an error occurred in your reporting until it is too late. 

Poor data quality costs organizations $12.9 million per year, on average. Your company needs methods like those from Pirani to ensure data quality won’t be sacrificed as you navigate toward business goals.

The Role of Risk Management in Mitigating Data-Related Risks

The good news is your business or organization doesn’t have to be another statistic for poor-quality data management. When implementing robust risk management principles, you cultivate system-wide data strategies that reduce exposure to information errors. There are many ways to integrate such stopgaps, including: 

• Data Profiling & Cleaning: Assessing current and legacy datasets for any unwanted anomalies, duplicates, or gaps. 
• Metadata Management: Creating context and lineage for all data to prevent misuse and improve consistency for all interested parties and departments. 
• Anomaly Detection with AI/ML: Artificial intelligence and machine learning are powerful allies in data quality. They can flag inconsistencies or outliers in real-time, drastically lowering your risk of significant errors. 
• Centralized Governance Frameworks: A modern business must assign ownership around data usage while also implementing strict rules for better transparency. 

Having, at a minimum, these tools in place lowers operational risk, boosts decision-making, and enhances data quality on a systemic level.

Quantifying the Cost of Poor Data

It helps to understand data quality on operational risk from the standpoint of quantifiable numbers. Knowing your data makes a difference in decision-making is one thing. Seeing how those decisions directly impact your bottom line is another. Most of the costs you’ll experience from data quality errors could be: 

• Financial Damage: Lost revenue, increasing costs (including redundant work in manual labor), more risk of fraud, and a greater chance of regulatory fines or penalties. 
• Operational Damage: Processes begin to break down leading to poor resource allocation and system failures that disrupt your business from the top down and bottom up. 
• Reputation Damage: Failure to comply with local rules and regulations lowers consumer trust in your business. It increases customer churn, directly impacting customer support and service. 
• Strategic Damage: Your ability to make decisions goes down, meaning your team misses lucrative opportunities and functions at a disadvantage compared to other niche competitors. 

While these operational risks are great, the flipside is when an organization avoids them, the potential for ROI skyrockets. With mature data governance frameworks, you can expect faster company growth, lowered risk exposure, and customer trust that ensures competitive resilience.

Best Practices for Managing Data Quality to Reduce Operational Risk

Modern tools lead to better results. To mitigate the operational risk from poor data quality, you must shift how your company culture views data stores and practices. That could mean integrating new systems that focus on: 

• Cross-functional ownership of data so departments and teams “co-own” information. 
• Continuous monitoring where workflows and detection occur in real-time instead of end-of-month reporting. 
• Education throughout the organization and accountability with KPIs directly tied to quality metrics. 
• Upgrading system-wide technologies and automating data cleaning, integration, and validations – especially for legacy systems with hidden risks. 
• Matching data policies with external regulatory standards and business objectives. 

You have to get your company culture to “buy into” the change in how data is managed, used, and collected. It may take some time, but the cost benefits are too great to ignore.

How Technology Like Pirani Can Help

The intersection of data quality and operational risk cannot be managed without advanced tools, especially those with automation and AI integration. Pirani offers robust risk management platform capabilities that empower your organization. When introduced to your system, operational risks are lower as they are identified and assessed in real-time. 

Pirani allows your team to implement and monitor controls for better consistency and compliance. You get centralized incident reporting that directly links to underlying risk factors, promoting data governance with greater interdepartmental visibility. 

Everything from security flaws to normative compliance is possible with Pirani by your side. No company is risk-free, but with this software, you can turn those risks into yet untapped opportunities. Contact the team today to learn how Pirani can help your organization improve data quality and lower operational risk.

Final Thoughts

Every decision a business makes is shaped by data. When you ignore how data quality on operational risk occurs, you open your organization up to all kinds of financial and operational damage. 

To remain competitive and agile, you cannot only reduce errors. You must have tools in place to learn from any previous data oversights so you can become more agile, resilient, and put a stop to operational risk before it escalates.