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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, data quality is becoming a growing concern. Bad data can cause many business problems, including decreased efficiency, lost revenue, and even compliance issues. This blog post will look at the causes of poor data quality and what companies can do to improve it.
Ensuring data quality has always been a challenge for most companies. This problem increases when dealing with data in the cloud or sharing data with different external organizations due to technical and architectural challenges. Cloud data sharing has become increasingly popular recently, as companies seek to utilize the cloud’s scalability and cost-effectiveness. However, the return on investment of these data analytics projects can be questionable without a strategy to ensure data quality.
Related: Why Bad Data Could Cost Entrepreneurs Millions
What contributes to cloud data quality issues?
Four main factors contribute to data quality issues in the cloud:
- When you migrate your system to the cloud, your legacy data may not be of good quality. As a result, insufficient data is transferred to a new system.
- Data may be corrupted during migration or cloud systems may not be configured correctly. For example, a Fortune 500 company restricted its cloud data warehouses to storing numbers up to eight decimal points. This challenge caused truncation errors during the migration, resulting in a $50 million reporting issue.
- Data quality can be an issue when data from different sources must be combined. For example, two different departments in a pharmaceutical company use different units (number vs. packs) to store inventory information. When this information was brought into the cloud data store, it became a nightmare to report and analyze the data due to inconsistencies on the drive.
- Data from third-party data providers may be of questionable quality.
Related: Your data may be safe in the cloud, but what happens when it leaves the cloud?
Why is it difficult to validate data quality in the cloud?
Everyone knows that data quality is essential. Most companies spend a lot of money and resources trying to improve data quality. Yet despite these investments, companies lose money annually due to insufficient data, ranging from $9.7 million to $14.2 million annually.
Traditional data quality programs do not work well to identify data errors in cloud environments because:
- Most organizations only look at data risks they know about, which is probably just the tip of an iceberg. Typically, data quality programs focus on completeness, completeness, duplicates, and range checks. However, these controls only account for 30 to 40 percent of all data risks. Many data quality teams fail to check for data drift, anomalies, or inconsistencies between sources, which contribute to more than 50 percent of data risks.
- The number of data sources, processes, and applications has skyrocketed due to the rapid adoption of cloud technology, big data applications, and analytics. These data assets and processes require careful data quality control to prevent errors in downstream processes.
- The data engineering team can add hundreds of new data assets to the system in a short period of time. However, it typically takes one to two weeks for the data quality team to check each new data asset. This means that the data quality team must prioritize which assets need review first, and as a result, many assets go unverified.
- Organizational bureaucracy and red tape can often slow down data quality programs. Data is a corporate asset, so any change requires multiple approvals from different stakeholders. This can mean that data quality teams must go through a lengthy process of change requests, impact analysis, testing, and approvals before implementing a data quality rule. This process can take weeks or even months when the data may have changed significantly.
What can you do to improve data quality in the cloud?
It is essential to use a strategy that considers these factors to ensure the quality of the data in the Cloud. Here are some tips for achieving data quality in the cloud:
- Check the quality of your legacy and third-party data. Correct any errors you find before migrating to the cloud. These quality checks will increase the cost and time required to complete the project, but it will pay off to have a thriving data environment in the cloud.
- Reconcile cloud data with legacy data to ensure data is not lost or changed during migration.
- Establish governance and control over your data and processes in the cloud. Monitor data quality continuously and establish corrective actions when errors are found. This will help prevent problems from getting out of control and becoming too expensive to fix.
In addition to the traditional data quality process, data quality teams must analyze and establish predictive data checks, including data drift, anomalies, data inconsistencies between sources, etc. One way to accomplish this is by using machine learning techniques to identify and detect data errors and augment current data quality practices. Another strategy is to take a more agile approach to data quality and align with data operations teams to speed implementation of cloud data quality controls.
Cloud migration is complex and data quality must be a priority to ensure a successful transition. Adopting a strategy to achieve 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 by implementing processes and tools, you can ensure the highest quality data and your cloud data projects have a higher chance of success.
Related: Optimize data management, web services, the cloud, and more by learning Amazon Web Services
