What is data accuracy? Definition, importance, and best practices

The value of data is intrinsically tied to its quality. Data accuracy dictates its quality level and informs its value to users. Inaccurate data not only leads to specious conclusions, but can have an adverse effect on transactions, such as incorrect addresses resulting in misdelivered orders or transposed numbers resulting in faulty calculations. 

What is data accuracy?

Data accuracy, a subset of data quality and data integrity, is a measure of how closely information represents the objects or events being recorded. The degree of correctness of information that is collected, used, and stored is measured by data accuracy. 

Data accuracy is crucial for records to be used as a reliable source of information and to power derivative insights with analysis. 

Maintaining high data accuracy ensures that records and datasets meet criteria for reliability and trustworthiness so they can be used to support decision-making and various applications. 

The criteria for data accuracy are determined by data creators, owners, and users. Based on their requirements, data governance and data quality programs are used to maintain acceptable levels of data accuracy based on established use cases and norms—for instance, the formatting of dates for the United States versus Europe. While MM/DD/YYYY (e.g., 08/01/1999) is correct in the United States, it would not be accurate in Europe, where the date format standard is DD/MM/YYYY (01/08/1999); using the wrong format can create many problems. 

Data accuracy vs data integrity

Data accuracy and data integrity are related elements of data management that address different areas of data quality. Below is a summary of the differences between data accuracy and data integrity.

Data accuracy	Data integrity
Data accuracy focuses on the correctness and reliability of data, ensuring that it is free from errors caused by data entry mistakes or faulty processing.	Data integrity focuses on maintaining the consistency, trustworthiness, and reliability of data throughout its lifecycle to ensure that it remains unchanged from its source and is protected from alteration without proper authorization.
The primary objective of data accuracy is to identify and correct errors in data values, such as transcription mistakes, duplicate entries, and incorrect values.	The primary objective of data integrity is to maintain the accuracy and consistency of data over time, whether stored in a single system, transferred between systems, or manipulated.
Data accuracy is measured by evaluating the degree to which data values are free from errors and accurately represent the real-world entities they are intended to describe.	Data integrity is measured by evaluating data’s consistency, reliability, and trustworthiness throughout its lifecycle.
Data accuracy measurements are performed through data validation, data verification processes, and by comparing data to agreed-upon sources of truth.	Data integrity measurements are conducted through data governance practices, validation, and verification processes. Monitoring and audits are also used to measure data integrity.
Data accuracy is achieved by using:  –Data cleansing to identify and address errors and inconsistencies in datasets, such as removing duplicates, correcting misspellings, and standardizing data.  –Data validation with predefined rules or algorithms to detect errors, inconsistencies, and inaccuracies in data during the data entry process or subsequently.  –Data profiling to identify patterns, trends, and anomalies in datasets that can be the sign of inaccuracies or inconsistencies.	Data integrity is achieved by using:  –Access controls to prevent unauthorized access to data, such as multi-factor authentication, encryption, and network firewalls.  –Backup and recovery systems to ensure that data can be restored to its original state in the event of data loss or corruption.  –Data governance practices to define criteria for and who is responsible for maintaining different aspects of data integrity, including its accuracy, consistency, and reliability.  –Error detection and correction processes and tools to detect and correct errors that may have occurred during data transmission, processing, or storage, such as checksums, cyclic redundancy checks, and digital signatures.

Why is data accuracy important?

Data accuracy is vital to the success of all organizations—from sales to accounting and marketing to human resources. Data informs decisions, creates impressions about an organization, and drives revenue. Reasons why data accuracy is important and a priority for the enterprise are that data accuracy: 

• Delivers better results to the organization’s users 
• Drives more value from artificial intelligence implementations with accurate and consistent data to feed algorithms 
• Enables better decision-making 
• Enhances efficiency 
• Expedites identification of the root cause when issues occur 
• Fosters and preserves brand credibility 
• Helps users produce better outputs 
• Improves customer satisfaction 
• Increases the confidence level of workers 
• Lowers the costs associated with data management 
• Makes it easier to achieve consistent results 
• Mitigates risks associated with flawed data 
• Provides confidence to users who depend on the data 
• Reduces the need to spend time and money finding and fixing errors in the data  
• Supports focused audience targeting and marketing efforts 

Causes of data inaccuracy

Understanding the factors that influence data accuracy helps optimize the quality of data. The following are some of the factors that diminish data accuracy. 

Data migration and transfer

Whenever data is transferred between platforms or systems, there are data accuracy risks, such as discrepancies in formats, truncation, and data loss. This risk is amplified when data is transferred between old and new systems. 

Data misinterpretation  

Data accuracy can be compromised by inaccuracies or incorrectly drawn conclusions caused by complex data or misinterpreting the meaning or implications of data. 

Duplicate records

Duplicate records create a number of data accuracy issues as they skew and complicate analytics and are tedious to identify and rectify.   

Inaccurate data sources

Data accuracy is often degraded by poor-quality data sources, such as social media, which are prone to poor formatting, typos, and inaccurate information.   

Incomplete data

Data accuracy is reduced when datasets are missing information in required fields. Human error, system errors, poor-quality external data, or incomplete forms can cause missing data. 

Inconsistent data

Data accuracy decay can be caused by inconsistencies within a dataset, such as contradictory information or information that conflicts with established patterns or trends. 

Lack of data accessibility regulation

Data accessibility is important for all organizations. However, the more access provided, the higher the data accuracy risks. When multiple users, especially from different groups, access a data set, the risk of duplicated, inconsistent, or inaccurate data increases significantly if data governance rules are not established and enforced.  

Malicious data manipulation  

Unauthorized data manipulation can occur when a malicious insider or outsider makes changes for nefarious purposes—for instance, tampering with or misrepresenting data to support an agenda or malware attacks that corrupt data.   

Measurement errors

Data accuracy related to tools and instruments can be compromised by poorly calibrated or malfunctioning tools or sensors.   

Outdated information

Maintaining and updating information is vital to maintain data accuracy. Without regular reviews and updates, data becomes stale, with accuracy continuing to deteriorate over time. This is of particular concern for records related to people or organizations as contact information changes. 

Poor data entry practices

One of the most common causes of data accuracy issues is related to data entry. A lack of data governance rules to dictate processes and formatting results in data quality issues when information is entered in multiple formats.  

In addition, simple human error significantly contributes to data accuracy issues. Accuracy is at risk when people manually enter information due to typographical errors, misunderstood instructions, or failure to complete all required fields from several factors (e.g., fatigue, carelessness, or lack of adequate training). 

Sampling errors 

Sampling errors can impact data accuracy when a data set is created from a sample rather than the complete pool of available data. This occurs when the sampling methods are flawed or the sample size is inadequate.  

Subjectivity and bias

Research data accuracy depends on the elimination of subjectivity or bias, such as personal beliefs or selective observations. The resulting inaccuracies are the result of deliberate manipulation of the research data collection process or unintentional biases.   

System errors

Even computer systems can make mistakes. While not frequent, errors introduced due to bugs or outdated software can affect data accuracy.  

Databases that are not properly maintained or have design flaws are also sources of inaccurate data. In addition, errors within data analysis systems can compromise accuracy. Data aggregation, integration, and transformation can also create accuracy issues. 

Costs of data accuracy

Organizations bear the cost of data accuracy failures in a number of ways. The monetary costs vary, but can be significant. Costs related to poor data accuracy include the following. 

Compliance failures

Data accuracy is crucial for ensuring compliance with government and industry regulations. Poor data quality can cause errors that result in fines and other penalties for noncompliance. 

Faulty targeted marketing programs 

Poor data accuracy inhibits marketers’ ability to execute targeted marketing campaigns. Campaigns conducted without accurate data result in the wrong messages going to prospects in the wrong media. At best, prospects simply ignore the messages. At worst, they become annoyed, and their impression of the organization diminishes.   

Lost revenue

Data accuracy gaps can cause system downtime, lead to poor decision-making, and cause missed sales opportunities, which can have a deleterious effect on revenue. 

Misleading results from data analytics 

A lack of data accuracy undermines data analytics work. When the underlying information is not accurate, trends and patterns in the data are misaligned and lead to poor decision-making.   

Reputational damage 

Poor data accuracy can cause a range of problems that reflect poorly on an organization and can damage its reputation. From mistargeted messages and shipping errors to strategic missteps and ill-informed decisions, bad data can result in long-term negative impressions.   

Unwanted downtime

Many systems and devices depend on data for predictive maintenance. Without data accuracy, analytics tools receive bad data, which can result in missed maintenance and failures that result in downtime.   

Wasted resources and time

Valuable time and resources are wasted when data accuracy is subpar. Time and money must be diverted from activities that can drive growth and innovation to clean and correct data.   

Challenges to data accuracy

Lack of a data culture

Organizations that have not embraced a data-driven culture struggle with data accuracy, because it is simply not a priority. Accuracy is almost impossible without a commitment to prioritizing and investing in data, because users lack the tools, training, and processes to support it.   

Reliance on outdated methods and technologies

Many organizations use legacy tools to prepare data manually. While these tools provide basic functionality, they are not capable of handling the complexities of modern data sources (e.g., social media, web forms, or chatbots) that are usually filled with errors and require sophisticated software to develop data accuracy.  

Data integration issues

Data accuracy is complicated by data integration challenges that come from combining data from disparate sources with differing structures and data quality levels. When diverse datasets are integrated, errors and discrepancies create degradation that plagues data accuracy. 

Data accuracy best practices

The following are commonly cited best practices for ensuring data accuracy. 

• Assess the organization’s use of data-related automation. 
• Define the ideal state for data accuracy. 
• Develop and implement a strategy. 
• Establish goals and metrics to quantify success. 
• Evaluate data processes and implement any changes required for optimization. 
• Leverage automation and other software solutions to increase data accuracy and productivity. 
• Measure accuracy to identify issues and direct maintenance efforts. 
• Review and update the data collection plan. 
• Set guidelines for what kind of data the organization collects, how it is collected, and how it is managed. 
• Solicit feedback about data accuracy from users to identify areas that need improvement.   
• Train users on accuracy goals and how to ensure that they are met. 
• Use data cleansing tools to identify and correct inaccurate, corrupted, or duplicated data.  
• Use data profiling to review and analyze the existing data to surface inconsistencies, anomalies, and duplications.   
• Validate data sets against trusted sources. 

A comprehensive approach to data accuracy

Organizations implement data controls following data governance and data management best practices to address the challenges and embrace the opportunities afforded by high data accuracy. These include establishing data quality standards, conducting regular data audits, and investing in employee training. 

A comprehensive approach to data accuracy reduces errors that compromise all areas of the enterprise and drives data quality standards into processes and systems. This can be facilitated by software, especially when complemented by a commitment to understanding the nuances of data accuracy and the factors that influence it. 

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