Data Observability vs. Data Quality

Data quality and data observability are two important concepts in data management, but they are often misunderstood or confused with one another. Understand the what, why, and how of each and you'll be better equipped to get the most value out of your data.
First, let's define both terms. Data quality is the state of data. It answers the question, "is the data usable and relevant?" Often this is identified using indicators like accuracy, completeness, freshness, correctness, and consistency.
Data observability, on the other hand, is a set of techniques that answers the question, "does the data contain any signals that need investigating?" By nature, data observability is continuous and provides real-time or near real-time insights about the data.
As the state of data changes, data observability is able to observe, capture, and notify us about the change in data. This observation could be about data quality issues or about signals in data that although considered healthy from a data quality standpoint, are significant nonetheless. Anomalies, outliers, or drifts in business data such as an unexpected change in a transaction amount fall into this category.
What can be done with data quality and data observability
Data quality focuses on validating data against a known set of policies. This provides a consistent understanding of the health of the data against predefined metrics.
Data observability, on the other hand, leverages ML and statistical analysis to learn from data and its historic trends and identify potential issues not previously known, and predict data changes.
Often the learnings from data observability can also be classified into data quality KPIs, hence accelerating data quality by automating the outcomes of data observability.
Data observability can also further investigate these issues and find root causes, therefore shortening the time to remediate data quality issues.
Upon finding issues and drifts in the data, data observability enables orchestrating and operationalizing data workflows. For example, with data observability, data teams can automate the decisions around the next steps of the pipeline.
Why data observability and data quality are essential to maintaining trust in your data
Data quality assesses the health of the data against predefined rules and expectations. For example, the uniqueness of social security numbers, or valid zip codes within a region. This validation helps data teams look for known or expected issues in order to create analytics or prepare data for data models.
Data quality requires a team – typically technical – to maintain, tweak and test data quality rules continuously.
Data observability, alternatively, detects drifts and anomalies that are outside the realms of data quality and could be unknown. It is a smarter system that helps automate identifying and alerting on both predicted (known) and unexpected (unknown) data changes.
A well-designed data observability tool is equipped with ML and automation to enable business teams to also participate in data quality and data monitoring initiatives.
Unlike traditional data quality tools, data observability tools are low-code / no-code with faster time to value and low cost of implementation and management.
How to implement data observability and data quality
Any organization that depends on data for key business decisions needs:
- Visibility into the latest health of the data via data quality KPIs
- Proactive and automated insights into any new and unexpected issues to handle them before any business impact
Any data team should be equipped with the tools to address these requirements.
Traditional Data quality tools
These tools focus on the validation, cleansing, and standardization of data to ensure its accuracy, completeness, and reliability.
Writing validation rules in SQL or using open source tools is one way to implement data quality. Traditional ETL tools often have data quality rules embedded in their user interface to transform the data into higher quality.
Traditional data quality tools work best for structured data. In order to analyze the health of semi-structured data or data that is in motion and streaming, further programming is needed to transform the data into an analytic-ready format.
To report on data quality issues and historical trends, the output of data validation checks needs to be built into a BI and visualized.
Data observability tools
While data observability tools can also validate data against predefined metrics from a known set of policies, they leverage ML and statistical analysis to learn about data and predict future thresholds and data drifts.
Given their constant monitoring and self-tuning nature, data observability tools can automatically curate issues and signals in the data into data quality KPIs and dashboards to showcase the ongoing data quality trends visually. These tools are equipped with interactive visualizations to enable investigation and further analysis.
Alerts and notifications are often table stakes and do not require programmatic configuration.
More sophisticated data observability tools are also capable of handling data stored in a semi-structured format or streaming data. These well-integrated platforms can span and serve complex data pipelines, in ways that data quality tools can not.
Use a tool like Telmai to orchestrate and operationalize your data workflows and automate the decisions around the next steps of the pipeline.
Conclusion
Data observability and data quality are both important for accurate data analysis and ultimately good decision-making. They both provide visibility into the health of the data and can detect data quality issues against predefined metrics and known policies. Data observability takes data quality further by monitoring anomalies and business KPI drifts. Employing ML has made data observability tools a smarter system with lower maintenance and TCO as compared to what traditional data quality was capable of doing.
Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.
To get started, there are four main steps in building a complete and ongoing data profiling process:
We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.
1. Data Collection
Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.
2. Discovery & Analysis
Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.
Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.
3. Documenting the Findings
Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.
Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.
4. Data Quality Monitoring
Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.
After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.
Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.
Build a platform that can measure and monitor data quality on an ongoing basis.
Take Advantage of Data Observability Tools
Automated tools can help you save time and resources and ensure accuracy in the process.
Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.
Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.
That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.
Data quality and data observability are two important concepts in data management, but they are often misunderstood or confused with one another. Understand the what, why, and how of each and you'll be better equipped to get the most value out of your data.
First, let's define both terms. Data quality is the state of data. It answers the question, "is the data usable and relevant?" Often this is identified using indicators like accuracy, completeness, freshness, correctness, and consistency.
Data observability, on the other hand, is a set of techniques that answers the question, "does the data contain any signals that need investigating?" By nature, data observability is continuous and provides real-time or near real-time insights about the data.
As the state of data changes, data observability is able to observe, capture, and notify us about the change in data. This observation could be about data quality issues or about signals in data that although considered healthy from a data quality standpoint, are significant nonetheless. Anomalies, outliers, or drifts in business data such as an unexpected change in a transaction amount fall into this category.
Data Observability
Data Quality
Leverages ML and statistical analysis to learn from the data and identify potential issues, and can also validate data against predefined rules
Uses predefined metrics from a known set of policies to understand the health of the data
Detects, investigates the root cause of issues, and helps remediate
Detects and helps remediate.
Examples: continuous monitoring, alerting on anomalies or drifts, and operationalizing the findings into data flows
Examples: data validation, data cleansing, data standardization
Low-code / no-code to accelerate time to value and lower cost
Ongoing maintenance, tweaking, and testing data quality rules adds to its costs
Enables both business and technical teams to participate in data quality and monitoring initiatives
Designed mainly for technical teams who can implement ETL workflows or open source data validation software
What can be done with data quality and data observability
Data quality focuses on validating data against a known set of policies. This provides a consistent understanding of the health of the data against predefined metrics.
Data observability, on the other hand, leverages ML and statistical analysis to learn from data and its historic trends and identify potential issues not previously known, and predict data changes.
Often the learnings from data observability can also be classified into data quality KPIs, hence accelerating data quality by automating the outcomes of data observability.
Data observability can also further investigate these issues and find root causes, therefore shortening the time to remediate data quality issues.
Upon finding issues and drifts in the data, data observability enables orchestrating and operationalizing data workflows. For example, with data observability, data teams can automate the decisions around the next steps of the pipeline.
Why data observability and data quality are essential to maintaining trust in your data
Data quality assesses the health of the data against predefined rules and expectations. For example, the uniqueness of social security numbers, or valid zip codes within a region. This validation helps data teams look for known or expected issues in order to create analytics or prepare data for data models.
Data quality requires a team – typically technical – to maintain, tweak and test data quality rules continuously.
Data observability, alternatively, detects drifts and anomalies that are outside the realms of data quality and could be unknown. It is a smarter system that helps automate identifying and alerting on both predicted (known) and unexpected (unknown) data changes.
A well-designed data observability tool is equipped with ML and automation to enable business teams to also participate in data quality and data monitoring initiatives.
Unlike traditional data quality tools, data observability tools are low-code / no-code with faster time to value and low cost of implementation and management.
How to implement data observability and data quality
Any organization that depends on data for key business decisions needs:
- Visibility into the latest health of the data via data quality KPIs
- Proactive and automated insights into any new and unexpected issues to handle them before any business impact
Any data team should be equipped with the tools to address these requirements.
Traditional Data quality tools
These tools focus on the validation, cleansing, and standardization of data to ensure its accuracy, completeness, and reliability.
Writing validation rules in SQL or using open source tools is one way to implement data quality. Traditional ETL tools often have data quality rules embedded in their user interface to transform the data into higher quality.
Traditional data quality tools work best for structured data. In order to analyze the health of semi-structured data or data that is in motion and streaming, further programming is needed to transform the data into an analytic-ready format.
To report on data quality issues and historical trends, the output of data validation checks needs to be built into a BI and visualized.
Data observability tools
While data observability tools can also validate data against predefined metrics from a known set of policies, they leverage ML and statistical analysis to learn about data and predict future thresholds and data drifts.
Given their constant monitoring and self-tuning nature, data observability tools can automatically curate issues and signals in the data into data quality KPIs and dashboards to showcase the ongoing data quality trends visually. These tools are equipped with interactive visualizations to enable investigation and further analysis.
Alerts and notifications are often table stakes and do not require programmatic configuration.
More sophisticated data observability tools are also capable of handling data stored in a semi-structured format or streaming data. These well-integrated platforms can span and serve complex data pipelines, in ways that data quality tools can not.
Use a tool like Telmai to orchestrate and operationalize your data workflows and automate the decisions around the next steps of the pipeline.
Start your data observibility today
Connect your data and start generating a baseline in less than 10 minutes.
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Conclusion
Data observability and data quality are both important for accurate data analysis and ultimately good decision-making. They both provide visibility into the health of the data and can detect data quality issues against predefined metrics and known policies. Data observability takes data quality further by monitoring anomalies and business KPI drifts. Employing ML has made data observability tools a smarter system with lower maintenance and TCO as compared to what traditional data quality was capable of doing.
Start your data observability today
Connect your data and start generating a baseline in less than 10 minutes.