Common Data Capture Errors and How to Avoid Them

Data capture is the linchpin of an organization’s information management lifecycle.  

An organization’s effectiveness in collecting, storing, and accessing documents and data from various sources has a big impact on its ability to make informed decisions, formulate and execute strategic plans, optimize processes, gain insights into customer behavior, and comply with regulations.  

But capturing data from large volumes of documents and documents in different formats is not easy.  And common data capture mistakes can impede an organization’s information management cycle.  

This article reveals common data capture errors and how to avoid them. 

What is data capture and how does it work?

Data capture is the process of capturing information from physical or digital sources and converting it into a structured format that can be stored, processed, and accessed efficiently and effectively.  

Here’s how the data capture process typically works: 

• Identification.  The first step in the data capture process is to identify the paper documents, electronic forms, databases, websites, and other sources from which data is to be collected. 
• Selection.  Once the data sources are identified, an appropriate data capture method is selected.  Scanning with high-speed production scanners, optical character recognition (OCR), barcode recognition, artificial intelligence (AI) with machine learning, and Natural Language Processing (NLP), are some of the options for capturing mission-critical data.  
• Processing.  Extracted data is then converted into a standardized format suitable for storage and analysis by legacy systems such as an enterprise content management (ECM) platform. 
• Validation.  Once data is captured and converted, it undergoes validation and quality control checks to ensure accuracy, completeness, and consistency.  This may include identifying and correcting any errors and validating data against pre-defined criteria or business rules.  
• Storage.  Captured data is securely stored in a centralized cloud-based repository, database, or other legacy system, or integrated with downstream systems to create a unified dataset. 
• Analysis.  Captured data can be used by data analytics and business intelligence tools to identify patterns, trends, or correlations in the data, and provide insights to stakeholders. 

These steps improve the collection, storage, and analysis of mission-critical information. 

Most common data capture mistakes

While data capture can help organizations better manage their content, common data capture mistakes can result in inaccurate or incomplete data that impedes the decision-making process. 

Here are some of the most common data capture mistakes:

• Keying errors.  Typographical errors, transposed numbers, and misspelled names are inevitable wherever manual data entry is required.  Resolving these issues downstream can be a major headache.  Left unchecked, inaccurate data can impede meaningful analysis.  
• Delays.  Keying lots of data can take lots of time.  Delays in the data entry process can lead to outdated information that skews decision-making and negatively impacts customers.  
• Incomplete data.  It not uncommon for crucial information to go uncaptured in a manual data capture environment.  Incomplete datasets can make analyzing data hard or misleading. 
• Lack of validation.  Errors such as text that’s been entered into a numerical field are more likely to slip through the cracks when organizations don’t validate data at the point of entry. 
• Duplicate data.  Poor integration between data capture systems and upstream and downstream data sources opens the door to redundant or conflicting information.  
• Data entry bias.  The biases of people keying data can lead to skewed or inaccurate results. 
• Inconsistent formats.  Integrating data into customer relationship management (CRM) applications and other legacy systems is complex and error-prone when organizations have inconsistent data formats across different data sources and multiple data entry points.  
• Unsecure data.  From ever-increasing compliance regulations to the heightened risk of fraud, the stakes have never been higher for organizations to safeguard privacy and integrity.  Inadequate security measures can increase the risk of unauthorized access or data breaches.   
• No metadata.  It’s tempting to overlook the need for metadata during data capture.  But it’s hard to manage and interpret data without the context and structure that metadata provides. 

These common mistakes can undermine an organization’s data capture initiatives. 

Data capture best practices

Organizations cannot afford any missteps in their data capture processes.  Here are some best practices to ensure that your organization’s data is captured accurately, efficiently, and securely. 

• Assess.  Understand what information needs to be collected, and it’s intended use. 
• Standardize.  Establish standardized formats and conventions for data entry to ensure consistency and accuracy across different data source and business users. 
• Automate.  Use AI and other technologies to eliminate manual effort, wherever possible.
• Validate.  Implement validation checks at the point of entry to reduce downstream errors. 
• Verify.  Employ data quality tools to identify and correct errors in captured data. 
• Control.  Implement user permissions, audit logging, and other data security measures to protect sensitive data from unauthorized access, breaches, or theft during capture. 
• Tag.  Capture and store metadata along with captured data to provide context, improve data governance, speed information retrieval, and streamline data management and analysis. 
• Train.  Train operators to ensure they understand data capture procedures and standards. 
• Monitor.  Regularly monitor data capture processes to identify issues, track performance, and ensure compliance with established standards and procedures. 
• Document.  Document data capture procedures, standards, and best practices to provide guidance to data capture operators and facilitate consistency and compliance.
• Govern.  Integrate data capture processes and procedures with broader data governance initiatives to ensure alignment with organizational policies, regulations, and standards.
• Adapt.  Periodically review and update data capture practices to incorporate new technologies, address emerging challenges, and achieve continuous process improvements.

These best practices help organizations derive maximum value from their data capture initiatives. 

Conclusion

Data capture is a cornerstone of effective information management.  By following the best practices in this article and avoiding common mistakes, organizations can unlock the full potential of their data, drive innovation, and achieve continuous process improvements in today’s digital environment.