Data Accuracy in 2025: What It Is & How to Ensure

Incomplete Data

Incomplete data occurs when required fields in datasets are missing. System errors, human errors, and incomplete user registration forms can lead to missing data.

For example, consider a scenario where some entries in the customer dataset are missing email addresses. When sending out promotional emails, these incomplete entries would result in some customers being left out of the marketing campaign.

Duplicated Data

Duplicated data occurs when the same information is repeated across multiple datasets. This can result in increased storage and operational costs. Identifying and rectifying these duplicates can be effort-intensive.

For example, if the same product information is accidentally entered into your organization’s dataset twice, you may misinterpret the proper distribution of data, leading to incorrect analysis.

Outdated Data

Outdated data arises when your database lacks relevant information or is not consistently updated to reflect changes, causing errors in analysis and decision-making processes.

Consider your organization’s customer dataset, which includes phone numbers that have not been updated in years. Attempting to contact the relevant customer for your product promotions using these outdated numbers would result in unsuccessful collaborations and wasted time.

Inaccurate Data Sources

Data sources from unverified social media accounts, online forums, and websites can provide incorrect, misleading, or incomplete information. This can severely impact the quality and reliability of the data used in various business processes.

Consider a scenario where you are conducting online surveys to assess your product efficiency. Some missing responses or biased samples can skew your understanding of customer satisfaction, and drawing conclusions from such surveys can impact your profitability. 

Human Error

Human errors happen when users make typographical mistakes, omit required fields, or misinterpretations during manual data entry.

For example, you might accidentally enter an incorrect postal code for a delivery package sent to a customer during the marketing campaign. This can result in the package being misdelivered or returned, wasting time and money. 

Integration Issues

Varying data structures across sources can result in misalignment or data loss when integrating them. For example, if one dataset is organized hierarchically while another has a flat file structure, merging them may cause them to lose hierarchical relationships. This occurs as the flat files lack the ability to represent these associations between data entities.

In addition, inconsistencies in date formats, units of measurement, or naming conventions compromise accuracy during data integration. Adding low-quality data from one source can cause inaccuracies throughout the integrated dataset.

Lack of Data Validation

Incorrect or incomplete data can go undetected without proper data validation, hindering the data accuracy. This inaccuracy leads to unreliable analysis and misguided decisions.

For example, you are conducting a survey on customer satisfaction with your company’s products. Few customers may leave out required fields if no validation rules exist. Without the essential information, you cannot perform a valid customer satisfaction analysis.

System Error

System errors such as database crashes or hardware failures can result in data loss. If backups are not properly maintained, you cannot restore the lost data, significantly impacting data accuracy. A single software bug in your database system could incorrectly perform calculations or transformations, leading to inaccurate results.

💡Suggested Read: What is Data Matching?

Validate Data Upon Ingestion

You must utilize automated software tools to validate the information upon data ingestion. This approach ensures that only accurate and properly formatted data is entered into the system, minimizing errors and maintaining data integrity.

For example, when a field requires a date, the system can automatically reject entries that do not align with the specified date format.

Establish a Data Governance Team

Creating a dedicated governance team can define and enforce data quality standards, ensuring the accuracy of your organizational data assets. This team serves as the central authority for addressing data-related concerns, such as inconsistencies in customer or product information.

Conduct Regular Data Audits

Regular data audits involve reviewing the datasets periodically to identify inaccuracies before escalating. This can help maintain high data integrity, enhance decision-making processes, and prevent risks associated with misleading information.

Use Data Quality Tools

Utilizing data quality tools involves identifying and resolving incorrect, incomplete, or duplicate values within datasets. These tools help maintain large, high-quality datasets, especially when manual verification is not feasible.

Train Your Staff

Adequate training is essential for people involved in data processing. This involves understanding how to maintain accurate data, identify sources of errors, and implement effective quality control measures.

After proper training, your organization can significantly reduce the risk of data inaccuracies and maintain the integrity of the data assets.

Check for Missing Values, Fields, or Records

You must identify and rectify missing values, fields, or records within your datasets. Missing data can result in incomplete analysis or incorrect results.

Utilize effective data cleansing tools to remove the missing data from your database. In addition, you can regularly review your datasets to ensure all required information is available and complete.

Verify That Data Is Consistent

Inconsistencies, like using St. and Street for addresses in your organization’s customer dataset, can cause discrepancies when analyzing customer distribution by location. By verifying data consistency, you can spot fields or records that deviate from expected standards or formats. Establishing a standardized format within your organization can ensure data remains consistent across all datasets.

Identify Duplicate Records

Duplicate records can negatively affect the accuracy of data analysis and decision-making processes. Use data quality tools to detect and reduce the number of duplicate records in your dataset.

Ensure That Data Is Up-to-date

You must regularly check and refresh the data to ensure it represents up-to-date and accurate information. Outdated data can cause incorrect conclusions and decisions.

Compare Data with Trusted Sources

Validate your data by comparing it with trusted and authoritative sources. Cross-referencing with reliable external sources can help understand how to verify the accuracy of data.

For example, if you have a database of geographic locations, consider validating it against a reliable geographical database to ensure high precision in geo-analytics. This practice helps identify inaccuracies and ensures alignment with industry standards.

Verify That Data Conforms to the Established Quality Standard

Establishing Key Performance Indicators (KPIs) related to data quality can help monitor and measure accuracy over time. For example, a KPI could be the percentage of data records validated each month.

By consistently using your organization's data quality standards, you can maintain high-quality data, supporting more reliable analysis and decision-making.

Data Profiling

Data profiling provides an overview of structure, content, and relationships within your data. Using data profiling tools allows you to review and analyze the current state of your data. These tools also let you identify anomalies, outliers, and other data quality issues.

Check for Any Data Corruption or Unauthorized Modifications

Data corruption occurs from system errors, while unauthorized data modifications occur through malware attacks. You must regularly check for signs of data corruption or unauthorized alterations.

Compare with Historical Data

Comparing current data with historical information involves identifying trends, patterns, or anomalies. This comparison can help validate the accuracy of the recent data and highlight any significant deviations that may require investigation.

How Does Airbyte Help in Ensuring Accuracy for Efficient Data Integration?

Instead of manually performing each of the steps in the ETL process, you can use an automated data movement platform like Airbyte. This data integration and replication platform helps you extract data from several sources and load it into a destination of your choice without any data loss. You can even apply custom transformation logic using the Data Build Tool (dbt).

Here are some other unique features of Airbyte:

• Extensive Built-in Connectors: Airbyte allows you to access over 350 pre-built connectors, facilitating integration with numerous data sources.
• ‍Connector Development Kit (CDK): It offers CDK for creating customized connectors tailored to your specific business requirements.
• ‍Change Data Capture (CDC): Airbyte’s CDC approach can help seamlessly capture and synchronize changes from source databases to destination, keeping your target system up-to-date for intelligent decision-making.
• ‍Developer-Friendly Pipelines: PyAirbyte, an open-source Python library offered by Airbyte, allows you to interact with Airbyte connectors and extract data from various sources within your Python workflows.
• ‍Data Security: Airbyte supports SSL, TLS, and HTTPS encryption and SSH tunneling for secure data transfer. Airbyte is also compliant with SOC 2 Type II assessment and ISO 27001 regulatory standards, ensuring safe data management throughout the integration process.

1. Are there any tools to perform data accuracy checks?

Yes, you can perform data accuracy checks using Monte Carlo and BigEye platforms.

2. What is the most effective way to ensure data accuracy during ETL?

Your organization's thorough data quality testing ensures that the data being processed and transformed during ETL is accurate.

3. What are the few data accuracy testing methods?

Data cleansing, data validation, data profiling, and data auditing are a few testing methods you can follow to ensure accuracy.

💡Suggested Read:

‍Data Denormalization

Data Quality Monitoring

Data Mesh Use Cases