How to Ensure Data Quality in the Cloud - Entrepreneur

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The quality of your data may diminish in the cloud. Follow these strategies to mitigate data loss and restore data quality.
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 information is becoming a growing concern. Erroneous data can cause many business problems, including decreased efficiency, lost revenue and even compliance issues. This blog post will discuss the causes of poor data quality and what companies can do to improve it.
Ensuring data quality has always been a challenge for most enterprises. This problem increases when dealing with data in the cloud or sharing data with different external organizations because of technical and architectural challenges. Cloud data sharing has become increasingly popular recently as businesses seek to utilize the cloud’s scalability and cost-effectiveness. However, the return on investment from these data analytics projects can be questionable without a strategy to ensure data quality.
Related: Why Bad Data Could Cost Entrepreneurs Millions
Four primary factors contribute to data quality issues in the cloud:
Related: Your Data Might Be Safe in the Cloud But What Happens When It Leaves the Cloud?
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 insufficient 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:
It is essential to use a strategy that considers these factors to ensure data quality in the Cloud. Below are some tips for achieving data quality in the cloud:
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 contributing to data quality issues and putting processes and tools in place, you can ensure that the highest-quality data and your cloud data projects will have a greater chance of success.
Related: Streamline Your Data Management, Web Services, Cloud, and More by Learning Amazon Web Services
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Entrepreneur Staff
Lucas Miller
Michael Waterbury
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