5 Critical Things You Should Know About Data Trustability

How much do you know about data trustability? Do you know how data trustability relates to data quality and data observability? Do you know how it works within a data pipeline? Do you know why your firm needs it? 

Data trustability is the next step in data quality. It goes beyond both data monitoring and data observability to automatically identify, isolate, and eliminate poor-quality data. 

In this article, we’ll explain what data trustability is, why it’s critical for your data strategy, and the five most important things to know about it. You’ll also learn how machine learning (ML) is revolutionizing the way organizations handle data trustability, and how to implement it effectively.

Quick Takeaways

• Data trustability bridges the gap between data quality monitoring and data observability
• Data trustability uses machine learning to automatically create data fingerprints and detect data abnormalities
• Data trustability works across the entire data pipeline to catch more errors than traditional tools
• Data trustability helps to build trust in data ingested from third-party sources

What is Data Trustability?

Data is core to just about every organization operating today. We store data in data warehouses and data lakes and move it around through data pipelines. If any of these things don’t work as expected, they can corrupt data—and corrupted, or poor-quality data is not only unusable, it can negatively impact the day-to-day and long-term decisions your firm makes.

A recent survey showed that the data quality and accuracy of the average CRM is less than 80%. This poor-quality data costs the average organization $12.9 million each year. 

Today’s businesses use various tools to improve the quality of their data. Data monitoring tools examine data quality and identify data that isn’t up to standards. Data observability tools inspect key parts of the data pipeline and identify and fix systemic issues that could impact data quality. Together, data monitoring and data observability work to improve overall data quality. 

Data trustability bridges the gap between data monitoring and data observability. It uses machine learning (ML) to create algorithms based on the data flowing through your system. It then uses these algorithms to construct data “fingerprints,” or ideal data models. When a piece of real-world data differs from these data fingerprints, it’s flagged as a data error. 

This differs significantly from traditional data monitoring, which looks for metadata at the record level that doesn’t adhere to human-based rules. Thanks to machine learning, data trustability identifies data errors based on how much given data deviates from those automatically-created data fingerprints. This approach is much faster and more accurate than more traditional methods. It allows data teams to spend less time manually determining data quality and more time using data. 

Why is Data Trustability Important?

Data trustability ensures that the data you rely on — whether from internal systems or third-party sources — is accurate, clean, and ready for use. This not only improves operational efficiency but also helps build trust in your data processes, particularly in industries that rely on data-driven insights like finance, healthcare, and retail.

Data Trustability is especially efficient at identifying specific data quality issues, including: 

• Dirty data with invalid or inaccurate values, such as bad ZIP codes
• Incomplete data, such as records with fields not filled in
• Inconsistent data, such as records that have nonstandard data formats
• Duplicative data, typically ingested from different data sources
• Anomalous data that deviate from a dataset’s norms

How Does Data Trustability Work?

Traditional data quality tools rely on human-defined rules to identify errors. This method works, but it’s slow and doesn’t always catch every issue. Data trustability, on the other hand, uses machine learning to create models based on historical data. It continuously learns and adapts to new data inputs, automatically flagging anything that deviates from expected patterns.

For example, if your CRM typically records customer ZIP codes in a standard format, and suddenly non-standard entries appear, data trustability tools will flag this as a potential error and alert your data team for review. It can also detect issues such as:

• Incomplete Data: Missing fields or inconsistent entries.
• Duplicative Data: Data entered multiple times from different sources.
• Anomalous Data: Outliers that don’t fit established patterns.

Now that we’ve covered the basics, let’s dive into the five things you need to know about data trustability.

Five Important Things to Know About Data Trustability

What are the most important things to know about data trustability? Here are five of them—all important to your business. 

1. It Uses Machine Learning to Detect Abnormalities Automatically

The first thing to know about data trustability is that it uses ML to automate the process of improving data quality. ML is an artificial intelligence technology that excels at “learning” as it encounters new datasets. This helps it identify and solve complex problems than humans can manually—and without any corresponding human error.

Because it uses ML to construct its model data fingerprints, data trustability doesn’t require human beings to write data quality rules manually. These data fingerprints can evolve as new and different data enter the system, enabling fast and efficient adaptation with minimal effort. When analyzing data quality, ML is more efficient, more comprehensive, and more accurate than manual-based methods. 

2. It’s More Catches More Issues Than Data Observability Tools

Data observability is an essential adjunct to traditional data monitoring. As thorough as it is, however, data observability doesn’t catch every data error in a system. Experts estimate that data observability tools address just 20% of knowable data issues.

Data trustability extends beyond data observability to identify and resolve most data issues. ML enables data trustability to see more potential errors in more places than can either data observability or data monitoring tools. It sees the whole picture, not just part of it.

3. It’s Faster Than Data Quality Tools

Traditional data quality monitoring tools are thorough but can be quite slow. Because of the manual nature, data monitoring tools can take up to five days to analyze data quality. That time gets even longer if new data quality rules have to be constructed.

ML-driven data trustability is considerably faster and more efficient than traditional data monitoring. Removing all human interaction speeds up the process and moves quality data faster through the pipeline. 

4. It Works Across the Entire Data Pipeline

Whether ingested from the source data or inadvertently created inside the system, data errors get magnified as they flow through the data pipeline. Unfortunately, data monitoring and data observability tools can only catch errors in certain parts of the pipeline.

Unlike traditional data quality tools, data trustability works across the entire data pipeline. Data trustability tools, employed at multiple points throughout the pipeline, catch data errors in more places than previously possible. This enables data managers to more quickly identify and react to any data quality issues that arise. 

5. It Builds Trust in Third-Party Assets

Finally, it’s important to know that data trustability does as its name implies and builds trust in data assets acquired from outside sources. Your organization may be unfamiliar with data sourced from a third party and unsure of that data’s quality. Data trustability ensures that all data ingested is subject to the same rigorous quality control. You can trust that the data you use, no matter where it is sourced, is of the highest possible quality and ready for use by your organization. 

How Data Trustability Boosts ROI?

Data trustability isn’t just about ensuring data quality — it’s about boosting your business’s return on investment (ROI) from data initiatives. Here are a few ways it achieves this:

• Reduced Operational Costs: By automating data quality checks, you save time and resources that would otherwise be spent on manual data cleaning.
• Faster Decision Making: With high-quality data readily available, your team can make decisions faster and with more confidence.
• Improved Data Accuracy: Eliminating errors early in the data pipeline ensures that your reports, dashboards, and predictive models are based on the most accurate data possible.

How to Implement Data Trustability in Your Organization?

Implementing data trustability is simpler than you think. Modern tools like FirstEigen’s DataBuck offer autonomous data quality solutions that work across all major data platforms — including data lakes, data warehouses, and cloud-based systems like GCP and BigQuery.

Build Data Trustability With DataBuck from FirstEigen

You need to trust the data you use—trust that it’s clean, accurate, and high-quality. Data trustability provides the confidence in data you need to run your business today and for the long term. 

DataBuck from FirstEigen is an autonomous data quality management solution powered by AI/ML technology. It can automatically validate thousands of data sets in just a few clicks and constantly monitor data ingested into and flowing through your data pipeline. DataBuck works with all major data lakes, data warehouses, and data pipelines to measure the trustworthiness and usability of data without IT intervention. 

Contact FirstEigen today to learn more about data trustability.

Check out these articles on Data Trustability, Observability & Data Quality Management-

• 6 Key Data Quality Metrics You Should Be Tracking
• How to Scale Your Data Quality Operations with AI and ML?
• 12 Things You Can Do to Improve Data Quality
• How to Ensure Data Integrity During Cloud Migrations?
• Data Integrity vs Data Trustability