Proven Strategies for Achieving Cloud Data Quality: A Modern Enterprise Guide

Previously published in Entrepreneur.com

You’ve finally moved to the Cloud. Congratulations! But now that your data is in the Cloud, can you trust it?  With more and more applications moving to the cloud, the quality of data is becoming a growing concern. Erroneous data can cause all sorts of problems for businesses, including decreased efficiency, lost revenue, and even compliance issues.

However, ensuring data quality in the cloud is more than just an IT challenge—it’s a business imperative. As organizations scale, the volume and variety of data grow exponentially, increasing the risk of inaccuracies and inconsistencies. Without a proactive approach to maintaining data quality, these issues can snowball, affecting everything from day-to-day operations to long-term business strategies. To succeed in today’s data-driven world, businesses must adopt robust strategies for managing and validating data in the cloud, ensuring that it remains accurate, accessible, and actionable.

This blog post will discuss the causes of poor data quality and what companies can do to improve it.

4 Key Factors Leading to Data Quality Issues in Cloud Environments:

1. Legacy Data Migration Issues: When migrating data to the cloud, legacy data may already have quality problems, and these issues are often carried over. For example, if historical data contains inaccuracies, those errors are transferred to the cloud, perpetuating poor data quality in the new system.
2. Data Corruption During Migration: Data can become corrupted during the migration process, especially if systems are not configured properly. A well-known case involved a Fortune 500 company that limited its cloud warehouse to store numbers with a maximum of eight decimal points. This configuration led to truncation errors during migration, resulting in a $50 million reporting discrepancy.
3. Inconsistent Data from Multiple Sources: Combining data from different departments or systems often creates inconsistencies in the cloud. Take, for example, a pharmaceutical company where one department tracks inventory in “packs” and another in “units.” When consolidated into a cloud data warehouse, these differences led to reporting complications, making data analysis difficult.
4. External Data Vendors: Data sourced from third-party vendors often comes with questionable quality, which can introduce errors into your cloud data environment. Vetting and validating this data is crucial before integrating it into your system.

Why is Validating Cloud Data Quality So Challenging?

Everybody knows data quality is essential. Most companies spend significant money and resources trying to improve data quality. However, despite these investments, companies lose money yearly because of bad data, ranging from $9.7 million to $14.2 million annually.

Traditional data quality programs do not work well for identifying data errors in cloud environments because:

While everyone agrees on the importance of data quality, ensuring it in a cloud environment poses unique challenges. Many organizations, despite investing significant resources into data quality programs, continue to face issues that cost millions annually. Here’s why:

1. Incomplete Data Risk Assessments: Traditional data quality programs tend to focus on risks that are known, such as completeness, integrity, duplicate records, and range checks. However, these checks only account for 30-40% of potential data issues. Many teams fail to consider data drift, anomalies, or inconsistencies across sources, which contribute to over 50% of data risks in cloud environments.
2. Exponential Growth in Data Sources: With the rapid adoption of cloud technology, big data applications, and analytics, the number of data sources and processes has grown significantly. This expansion creates new risks for downstream processes, which require careful data quality management to prevent costly errors.
3. Lag in Implementing Data Quality Checks: While new data assets can be added to cloud systems quickly, data quality teams often take weeks to implement the necessary checks. This delay leaves many data assets unverified for extended periods, leading to unchecked risks and potentially flawed data entering critical business processes.
4. Organizational Bureaucracy: Data quality initiatives are often hampered by corporate red tape. Since data is considered a valuable corporate asset, any changes to data quality protocols require multiple layers of approval from stakeholders. This slows down the implementation of new rules, allowing quality issues to persist in the meantime.

Effective Strategies to Enhance Cloud Data Quality

Below are some tips for achieving data quality in the cloud:

Validate Legacy and Third-Party Data Before Migration

Before moving to the cloud, thoroughly check the quality of your existing data, as well as any third-party data. Fix any errors or inconsistencies to avoid transferring problems into your new cloud environment. While these quality checks may increase the cost and time of migration, they are essential to establishing a successful data ecosystem in the cloud.

Reconcile Cloud Data with Legacy Systems

After migration, validate that the data in the cloud matches the data from legacy systems to ensure no data was lost or altered during the process. Implement reconciliation procedures to catch any discrepancies early.

Implement Cloud-Specific Data Governance

Establish governance and control over your cloud data by continuously monitoring its quality. Governance should include real-time data monitoring and swift corrective actions when errors arise. This will help prevent minor issues from escalating into costly problems.

Ensuring Data Quality in the Cloud – Final Thoughts

In addition to the traditional data quality process, data quality teams must analyze and establish predictive data checks, including data drift, anomaly, data inconsistency across sources, etc. One way to achieve this is by using machine learning techniques to identify hard-to-detect data errors and augment current data quality practices. Another strategy is to adopt a more agile approach to data quality and align with the Data Operations teams to accelerate the deployment of data quality checks in the cloud.

Migrating to the cloud is complex, and data quality should be top of mind to ensure a successful transition. Adopting a strategy for achieving data quality in the cloud is essential for any business that relies on data. By considering the factors that contribute to data quality issues and putting processes and tools in place, you can ensure the highest-quality data, and your cloud data projects will have a greater chance of success.

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

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