Understanding the Root Causes of Data Quality Problems

In today’s data-driven world, maintaining high-quality data is crucial for effective decision-making and operational efficiency. However, organisations often encounter significant challenges in ensuring the integrity and accuracy of their data. These challenges can be traced back to various root causes, which can be categorised into four primary areas: Manual Data Entry, Data Conversion & Consolidation, Data Integration & Data Processing, and Data Decay. Each of these areas contributes to the deterioration of data quality, impacting business processes and outcomes.

1. Manual Data Entry: The Human Contribution

One of the most common sources of data quality problems is manual data entry. Despite advances in automation, many organisations still rely heavily on manual input, which inherently introduces the potential for errors. The issues stemming from manual data entry include:

Data Entry Mistakes: Human errors, such as typos, incorrect data input, and misinterpretation, are frequent and can severely compromise data quality.
Inconvenient and Poorly Designed Data Entry Forms: When data entry forms are not user-friendly or are poorly structured, they can lead to incomplete or inaccurate data input.
Flawed Data Entry Processes: Inefficient or outdated processes for entering data can exacerbate the likelihood of mistakes and inconsistencies.
Deliberate Errors: In some cases, intentional falsification of data due to lack of oversight or fraudulent behaviour can also occur, further compromising data integrity.

These issues highlight the significant role human factors play in data quality. While completely eliminating human error may be impossible, organisations can minimise risks by improving data entry processes and implementing better-designed systems.

2. Data Conversion & Consolidation: One-Time Data Movement and Restructuring

Another critical factor in data quality degradation is the process of data conversion and consolidation. This typically occurs during system upgrades, migrations, or when integrating data from various sources. Key challenges in this area include:

Incomplete or Outdated Metadata: Metadata, which describes the data’s attributes, can often be incomplete or outdated, leading to misinterpretations and errors during data conversion.
Insufficient Understanding of Source and Target Databases: Without a comprehensive understanding of both source and target databases, data can be incorrectly mapped, leading to inconsistencies and inaccuracies.
Data Conversion from Legacy Systems: Converting data from older, legacy systems to new platforms can be complex, often resulting in data loss or corruption.
System Consolidation: Merging data from multiple systems into a single database can introduce errors if the data is not properly standardised or validated beforehand.

These challenges underscore the importance of meticulous planning and execution during data conversion and consolidation processes. Ensuring accurate and consistent data movement is essential to maintaining high data quality.

3. Data Integration & Data Processing: Processes That Should Work Like Clockwork

Data integration and processing are supposed to be seamless operations that contribute to maintaining data quality. However, these processes can often become sources of data quality issues due to:

Source System Data Changes: Changes in the underlying source systems, such as schema updates or modifications, can lead to data mismatches and errors.
Real-Time and Near Real-Time Interfaces: The pressure to process data in real or near real-time can introduce errors if systems are not adequately synchronised or if data validation steps are skipped.
Data Processing: Complex data processing operations, especially those involving large datasets, can result in data being improperly transformed or processed.
System Upgrades: During system upgrades, data processing routines may be altered, potentially leading to data inconsistencies or loss.
Data Cleansing and Purging: Inadequate or improper data cleansing and purging practices can leave behind errors and irrelevant data, reducing the overall quality.

Effective data integration and processing require robust systems and processes that can handle the intricacies of data management without introducing new errors. Regular monitoring and validation are key to ensuring these processes contribute positively to data quality.

4. Data Decay: The Inevitable Deterioration

Lastly, data decay represents the inevitable deterioration of data quality over time. Unlike the other categories, data decay is a gradual process influenced by several factors:

Decay: Data naturally becomes less accurate or relevant over time as it ages, especially if not regularly updated or maintained.
Data Usage: The way data is used can also contribute to its decay. Frequent access, modifications, or transfers can degrade data quality.
Process Automation: Automation can help maintain data quality, but if not managed correctly, it can also lead to the propagation of errors or outdated information.
Loss of Expertise: Over time, the expertise needed to manage and interpret certain data may diminish, leading to misinterpretations or improper handling of data.

Data decay is an unavoidable aspect of data management, but its effects can be mitigated through proactive data maintenance, regular updates, and ensuring that data management expertise is retained within the organisation.

AICA’s Role in Ensuring Clean and Enriched Data

AICA leverages advanced AI and ML algorithms to address the challenges associated with data quality, particularly in product and service data. Our cutting-edge technology is designed to detect and rectify errors at every stage of the data lifecycle, from manual entry to integration and processing.

By automating the identification and correction of data entry mistakes, inconsistencies from legacy system conversions, and issues arising from real-time data processing, AICA ensures that your product data remains accurate, consistent, and reliable. Our solutions are not only highly effective, boasting over 80% accuracy with specialised LLMs, but they also integrate seamlessly with existing PIM, MDM, EAM, and ERP systems, enabling businesses to maintain high-quality data with minimal manual intervention.

This ultimately leads to enhanced decision-making, operational efficiency, and a significant reduction in costs associated with poor data quality.

Conclusion

Whether it’s the human factor in manual data entry, the challenges of data conversion and consolidation, the complexities of data integration and processing, or the inevitability of data decay, understanding these root causes is the first step toward mitigating their impact. By addressing these issues head-on, organisations can ensure higher data quality, leading to more accurate insights, better decision-making, and ultimately, greater business success.

To learn more about our services and take control of your product data management today, visit our website here.