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Preserve Data Quality – Prevent Errors with Technology and Build a Culture of Data Integrity

Data errors cost you more than you realize. Make better decisions and more money.

Key Takeaways:

Quality data improves efficiency throughout your firm.
With data integrity, fraud is easy to detect and prevent.
Technology, including automation, reduces the need for human intervention.
The first step is identifying the sources of errors.
Then, you can establish data quality protocols and enforce them.

Data accuracy is essential in insurance adjusting firms – it directly impacts your ability to make informed decisions and assess claims. It informs decision-making, improves efficiency, enhances customer service, and mitigates fraud.

Data errors have many consequences, including incorrect assessments, claim processing delays, regulatory non-compliance, and financial loss. None of these risks are worth taking.

In this article, we’ll offer insights that allow you to establish a robust culture focused on data quality and error prevention. You’ll be able to increase data accuracy to improve operational efficiency and compliance and gain confidence in your decisions. You’ll understand the source of data errors, how to establish quality protocols, the important role of technology in data error prevention, and how to instill a data quality mindset throughout your organization.

First, you have to understand the source of data errors

Mistakes will be made. Where they are made can be harder to figure out, but these are the most common sources of data errors in insurance adjusting firms:

  • Manual entry errors. Human error is a top cause of data errors that stem from illegible handwriting, typos, and transposition of numbers and letters.
  • Inaccurate documentation. If a policyholder or an adjuster doesn’t provide all the required information or provide incorrect information, some information may be missing.
  • Lack of standardized practices. Inconsistencies in data entry practices among different adjusters or teams are a huge source of data errors. Without a standard or protocol for entering data, information accuracy is anyone’s guess among discrepancies and inaccuracies.
  • System compatibility issues can arise when different systems don’t work well together. Data can be lost, duplicated, or incorrectly imported and exported.
  • Lack of effective quality control measures. When there’s no standard set, it’s like the Wild West. Without a systematic review or verification process, errors can go unnoticed until they create significant issues. 

Take a deep dive into your systems, processes, and practices. Identifying the source of errors is the first step in preventing them and upping your error prevention game.

Establish data quality protocols

Data quality protocols are rules and guidelines that ensure accuracy, completeness, and consistency throughout its lifecycle. They help maintain data integrity and improve reliability for analysis, decision-making, and reporting. Data quality protocols include:

  1. Data validation helps ensure that data entered or imported into a system meets the specified criteria or format. Validation checks include range, format, consistency, and cross-field comparison.
  2. Data cleansing means identifying and correcting errors, inconsistencies, or discrepancies within data. Techniques like outlier detection, duplicate identification, and standardized formatting can be used.
  3. Data standardization. Standardization protocols ensure data is uniformly formatted and recorded according to your guidelines. This includes consistent naming conventions, data units, date formats, and other standardization rules.
  4. Data profiling. Analyzing data patterns, relationships, and statistical properties helps identify anomalies such as missing values, outliers, or data conflict. It provides insights into data quality issues that need to be addressed.
  5. Data governance protocols establish the guidelines, policies, and roles for managing data quality. This includes defining responsibilities, access controls, and procedures for data handling, validation, and maintenance.
  6. Data integration policies. Protocols ensure consistency, accuracy, and compatibility when integrating data from multiple sources. These policies define how data should be mapped, transformed, and synchronized to maintain quality across different systems.
  7. Perform routine audits. Audits are an essential part of maintaining data quality and integrity. Establish your criteria, plan and schedule, select a representative sample, conduct your audit, and document your findings. A root cause analysis is next so you can take corrective action. 

Implementation of these protocols is an ongoing process. Data quality needs to be monitored, maintained and improved. Regular data quality assessments and continuous improvement efforts ensure data reliability and maximize its value.

How to leverage technology for data error prevention

Technology plays a significant role in preventing data errors. Here’s how.

By implementing automation, you can perform real-time data validation checks to catch errors, including missing data, incorrect data, and duplicate entries. Document scanning also reduces (and sometimes eliminates) the need for manual intervention to resolve errors and anomalies, which means no manual entry. This means productivity by reducing the time needed to complete tasks. 

Automation also enforces data access and governance policies to increase security, and artificial intelligence (AI)- driven data analysis further reduces errors, improves accuracy and removes the need for manual spreadsheet analysis so you can focus on more strategic tasks. 

Integrating technology into existing systems and workflows improves efficiency and productivity by performing tasks that were done manually, improving accuracy, and enhancing the customer experience. Because technology means better data collection and storage, analysis contributes valuable insights for data-driven decisions. Through the use of a branch of machine learning(ML), it can identify trends and patterns that can help reduce fraud. 

Foster a data quality mindset among staff

Ensuring data quality in your insurance adjusting firm takes vigilance and the right mindset. Creating a data quality mindset among staff is essential. Take these steps:

  • Educate and train your staff on how inaccurate data impacts your company, including the bottom line.
  • Communicate regularly. Talk about data quality and emphasize the importance of accuracy, consistency, completeness, and timeliness in data management.
  • Provide clear guidelines and processes. Set expectations by educating staff on guidelines and processes for data entry, validation, and management. Help them by creating templates, checklists, and documented SOPs.
  • Lead by example. Don’t expect others to clean up your data mess. Maintain established standards to demonstrate your commitment to high-quality data.
  • Foster a collaborative culture and feedback loop. Employees should feel comfortable sharing feedback and reporting data quality issues. Be sure to establish formal channels for team members to communicate and collaborate.
  • Recognize and reward. Consider creating incentives to ensure a data-quality-driven mindset and recognize and reward staff who consistently adhere to data quality standards.

It’s important to foster a mindset of continuous improvement. Encourage learning more about data quality practices. Ensure they can access the tools, technology, and resources required for quality data management.

Embrace your future of enhanced data integrity

Inaccurate, messy data serves no one. It creates unnecessary problems, clouds real issues in business performance, and hurts the bottom line. Data is fuel for your business just like gasoline is fuel for a car – low-quality fuel leads to breakdowns. 

Data integrity is important, and trends that will shape its future include advanced analytics and the further use of AI and ML. These will play a further role in data cleansing, validation, and normalization and will help reduce and prevent fraud. Data integrity also leads to an enhanced customer experience, a competitive edge, and growth.

At Susco, we build technology that unleashes human potential

Technology makes businesses more efficient. We can help you close operational gaps that reduce data integrity so you can make the data-driven decisions that lead to business success. 

For more than a decade, our dedicated team of web and application developers has built custom solutions that perfectly align with business goals. Discover what we can do for you. Contact us today to schedule your free one-hour assessment.

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