Data Profiling and Data Quality: What You Need to Know
Summary
- Data profiling analyzes the structure, content, and formatting of data to identify missing values, outliers, and anomalies.
- Data quality focuses on identifying and correcting errors to ensure information is accurate, consistent, and relevant for business.
- Profiling serves as a critical first step for quality by revealing where data needs improvement before it is used in reports.
- Automating ingestion and quality rules reduces manual errors and ensures reliable data is delivered to decision-makers at scale.
Data is the lifeblood of business. It drives decision-making, allows for analysis and prediction, and can help drive revenue. But data can also be its own worst enemy: it can lead to analysis paralysis, lead to inaccurate predictions, and even make processes more bureaucratic. That’s where a data profile and data quality come in.
Data profiling is the process of understanding the structure of your data, as well as its semantic and numerical content.
Data quality, on the other hand, is the process of ensuring that your data is free from errors so that operations can be streamlined and improved.
The two are closely related and often implemented together: with a solid foundation in one, you’re more likely to find success in the other. Let’s take a look at each side separately.
What is Data Profiling?
Data profiling is the process of analyzing data, looking at its structure and content so that you can better understand how your data is relevant and useful, what it’s missing, and how it can be improved.
One of the first places to look when data profiling is your data’s structure, as well as its characteristics, such as its size and the number of values it contains.
You can also look into potential anomalies, such as large outliers or anomalous clusters, which could indicate that your structure is incorrect or that the distribution of values within your structure is faulty.
Profiling data can also look at the semantic and numerical content of your data, and can even look at data formatting. For example, if all your salary data is stored as dollars and cents, rather than being rounded, but your reports are showing salaries rounded to the nearest dollar, that could indicate that your data is not formatted correctly and that it’s not being properly imported into your system or used in reports.
What is Data Quality?
Data quality involves identifying errors within your data, and then correcting those errors, so that your data is as accurate as possible. Some errors, such as incorrect values, can be detected and then corrected by the person who entered the data, but some might be more difficult to identify.
Data quality is important because poor data quality can lead to incorrect decision-making, decreased operational efficiency, and lost revenue through poor marketing targeting. There are many ways to improve your data quality, including hiring a data engineer or data scientist to implement software tools, conducting regular data audits, implementing data integrity checks at scale, or creating a governance model for data quality. To improve your data quality, you can also conduct a data inventory to determine what data you have and how accurate that data is.
How Does Profiling Data Help Data Quality?
Data profiling and data quality go hand in hand, as they both fall under the umbrella of data quality assurance. Data profiling is a tool used to identify the structure, content, and formatting of your data, as well as the people responsible for its creation, so that data quality can be assessed and improved. Think of profiling as the first step in enhancing the quality of your data.
Data profiling and data quality are two sides to the same coin: with accurate data, you can better assess its quality, and with better data, you can better profile it.
Data quality is a process of continuously assessing the quality of data, and then working to improve it. It starts with the initial data collection and continues through the post-implementation review of the data collection process. The main goals of data quality are accuracy, integrity, and relevance. Data quality is an important consideration for all businesses, but especially so for those that rely on data-driven decisions. Data quality will vary depending on the type of data and the industry that it is used in.
Automating Data Quality
Automating data processes makes them virtually hands-off for you, which can help increase your data quality. For example, when you have a lot of lead forms that need to get entered into your database, set up a system that will automatically import the information as soon as it’s submitted. Doing this saves time and reduces the chance that errors will be made while inputting the data manually.
Automating ingestion can help reduce errors, but with the volume of business data, it’s nearly impossible to catch all quality issues at ingestion, which is why automated data quality is critical.
Data quality is a challenge for every organization that collects and processes data, but it’s essential for businesses to succeed. Many companies struggle to get their data quality act together and understand the root of the problem. But, with a little research and planning, you can ensure your data is accurate, reliable, and useful for your business.
Actian Data Observability: How We Help You Ensure Data Quality, Continuously
Actian Data Observability provides organizations with comprehensive visibility into the health, reliability, and performance of their data ecosystems. As data environments grow more complex—spanning cloud platforms, on-premises systems, and hybrid architectures—maintaining trusted, analytics-ready data requires continuous monitoring and proactive issue detection.
End-to-End Pipeline Visibility
Actian Data Observability tracks data across the entire lifecycle, from ingestion and transformation to downstream analytics and reporting. This end-to-end transparency enables teams to quickly identify where data issues originate and understand their potential business impact.
Proactive Anomaly Detection
The platform automatically detects schema changes, volume fluctuations, distribution shifts, and other anomalies that may signal data quality problems. Early detection reduces the risk of flawed dashboards, inaccurate reports, and compromised machine learning models.
Comprehensive Data Lineage
Built-in lineage capabilities map data dependencies across systems and workflows. This allows organizations to assess the downstream impact of changes, streamline root cause analysis, and support compliance efforts with clear audit trails.
Automated Data Quality Controls
Teams can define business-aligned rules and thresholds to validate accuracy, completeness, consistency, and timeliness. Continuous validation ensures that data remains reliable as it moves across systems.
Faster Incident Resolution and Collaboration
Centralized alerts, diagnostics, and contextual metadata empower data engineers, analysts, and governance teams to collaborate efficiently. By reducing time to detection and resolution, organizations minimize operational disruptions and maintain confidence in their data assets.
Together, these capabilities help enterprises shift from reactive troubleshooting to proactive data reliability management—strengthening governance, reducing risk, and enabling more confident, data-driven decision-making.
Get a Personalized Demo of Actian’s Capabilities Today
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