The Importance of Data Quality for the Cloud and AWS

Table of Content

    Data stored in the cloud is notoriously error-prone. In this post, you’ll learn why data quality is important for cloud data management – especially data stored with Amazon Web Services (AWS).

    With every step that error-ridden data moves downstream, those errors get compounded – and it takes 10 times the original cost to fix them. Unfortunately, most companies monitor less than 5% of their cloud data, meaning the remaining 95% is unvalidated and unreliable.

    How can you improve the quality of your cloud data? It requires automating data quality monitoring, as you’ll soon learn.

    Quick Takeaways:

    • Data quality has six distinct dimensions: accuracy, completeness, consistency, timeliness, uniqueness, and validity.
    • High-quality data is essential to provide accurate business analytics and ensure regulatory compliance.
    • Cloud data quality is notoriously error-prone.
    • The only practical way to ensure data quality is with an AI-driven data quality monitoring solution.

    What is Data Quality?

    Data quality (DQ) is determined by six primary dimensions. You can evaluate the DQ of your cloud data by tracking these metrics.

    The six dimensions of data quality


    Quality data must be factually correct. The data values reported must reflect the data’s actual value. Inaccurate data leads to inaccurate results and bad decision-making.


    A data set with missing values is less than completely useful. All data collected must be complete to ensure the highest quality results.


    Data must be consistent across multiple systems and from day to day. Data values cannot change as it moves from one touchpoint to another.


    Old data is often bad data. Data needs to be current to remain relevant. Since data accuracy can deteriorate over time, it’s important to constantly validate and update older data.


    Data cannot appear more than once in a database. Data duplication reduces the overall quality of the data.


    Validity measures how well data conforms to defined value attributes. For example, if a data field is supposed to contain date/month/year information, the data must be entered in the correct format, not in year/date/month or some other configuration. The data entered must reflect the data template.

    Why High-Quality Data is Essential

    High-quality, reliable data is important for all organizations in all industries. Data can drive revenues and minimize costs – but only if that data is accurate. The impact of data error on analytics can be substantial.

    Quality data is also essential in complying with industry and government regulations. For example, banks and financial institutions must demonstrate to regulators that their data programs are accurate, which requires strict data controls. If a bank’s data is inaccurate, it could face hefty fines from regulators. Both Forbes and PwC report that poor DQ is a major contributor to noncompliance.

    Ensuring this type of accurate data depends on good data quality systems. Unfortunately, anywhere from 60% to 85% of data initiatives fail because of poor quality data.

    Poor data quality data can also destroy business value. Gartner reports that poor data quality costs the average organization between $9.7 million and $14.2 million each year – a number that’s only going to increase as the business environment becomes more complex.

    Data Errors in the Cloud

    Data warehouses, lakes, and clouds are notoriously error-prone, which can have significant issues for any data-driven project or process. What are the primary data quality challenges you’re likely to encounter? Here’s a short list:

    • Missing data
    • Unusable data
    • Expired data
    • Incorrectly or inconsistently formatted data
    • Duplicate records
    • Missing linkages

    Monitoring and ensuring data quality is a complex undertaking. (Gartner outlines 12 actions you can take to improve your DQ – it’s a lot of work!)

    Twelve actions to improve DQ


    Our research estimates that for the average big-data project, 25% to 30% of the time is spent on identifying and fixing these data quality issues. And, if your data is stored on AWS and other cloud providers, the number of data quality issues is significantly higher. Cleaning this “dirty data” is not the responsibility of AWS or other cloud hosts – it’s purely your problem to solve.

    How AI Can Help Improve Data Quality in the Cloud

    How can you ensure the quality of the data you use to run your business? While you could try to manually examine each record in your database, that’s practically impossible. Just setting rules for thousands of data tables – each with thousands of columns – is unrealistic and increasingly so as your data evolves.

    Manually trying to find errors in large amounts of data is much like looking for the proverbial needle in a haystack. It’s a virtually impossible task when you have large data sets flowing at high speeds from a variety of different sources and platforms.

    Consider a bank that onboards several hundred new applications to their IT platform. If there are four data sources per application and 100 checks required per source, more than 100,000 individual checks would be required. It simply isn’t doable.

    The only practical way to improve data quality for large data sets is to employ a robust data quality monitoring (DQM) solution powered by artificial intelligence (AI) and machine language (ML) technology. An AI/ML-powered solution provides autonomous data quality monitoring without the need for constant human intervention. The system is not constrained by a fixed set of rules but instead learns and evolves as circumstances change. It also scales as your needs change. The AI-based system is capable of handling the DQ needs of even the largest and most complex organizations.

    In short, you can reduce the risk arising from unreliable cloud data and increase your employees’ productivity by leveraging AI-based DQM. Automating this process should be the first step in hardening your organization’s data pipeline.

    In the following video, Microsoft’s Aitor Murguzur further discusses how AI can automate data quality.

    Let DataBuck’s Autonomous Data Quality Monitoring Ensure Data Quality for Your Cloud Data 

    FirstEigen’s DataBuck is an autonomous data quality monitoring solution powered by AI/ML technology. It automates more than 70% of the laborious work for data monitoring and provides you with dependable reports, analytics, and models. It also lowers your cost of data maintenance and is fully scalable.

    Think of DataBuck as a digital assembly line for creating and enforcing data quality validation rules. DataBuck can autonomously validate thousands of data sets in just a few clicks. It not only automates data monitoring processes but also improves and updates them over time. Contact us today to learn more and begin improving your cloud data quality almost immediately. 

    Contact FirstEigen today to learn how DataBuck can improve the data quality of your cloud data!

    Check out these articles on Data Trustability, Observability, and Data Quality. 

    Posted in