How to Ensure Data Quality in the Cloud

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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? As more and more applications move to the cloud, information quality becomes a growing concern. Bad data can cause many business problems, including reduced efficiency, lost revenue, and even compliance issues. This blog post discusses 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 is amplified when it comes to data in the cloud or when data is shared with different external organizations due to technical and architectural challenges. Cloud data sharing has grown in popularity recently as companies seek to take advantage of the scalability and cost-efficiency of the cloud. However, without a strategy to ensure data quality, the return on investment of these data analysis projects can be questionable.

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What Contributes to Data Quality Problems in the Cloud?

Four main factors contribute to data quality issues in the cloud:

  • When you migrate your system to the cloud, the legacy data may not be of good quality. This will not transfer enough data to a new system.
  • Data may become corrupted during migration or cloud systems may not be properly configured. For example, one Fortune 500 company limited its cloud data warehouses to storing numbers with up to eight decimal places. This challenge caused truncation errors during migration, resulting in a $50 million reporting issue.
  • Data quality can be an issue when data from different sources needs to be combined. For example, two different departments of a pharmaceutical company use different units (count versus packs) to store inventory information. When this information was integrated into the cloud data warehouse, the inconsistencies in the unit made it a nightmare to report and analyze the data.
  • Data from external data providers may be of questionable quality.
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Why is data quality validation difficult in the cloud?

Everyone knows that data quality is crucial. Most companies spend a lot of money and resources to improve data quality. Despite these investments, however, companies lose money annually, ranging from $9.7 million to $14.2 million per year, due to insufficient data.

Traditional data quality programs are not good at identifying data errors in cloud environments because:

  • Most organizations only look at the data risks they know about, which is probably just the tip of the iceberg. Typically, data quality programs focus on completeness, integrity, duplicates, and range checking. However, these checks account for only 30 to 40 percent of all data risks. Many data quality teams don’t check for data drift, anomalies, or inconsistencies between sources, which contributes to over 50 percent of data risk.
  • The number of data sources, processes and applications has exploded due to the rapid adoption of cloud technology, big data applications and analytics. These data sets and processes require careful data quality control to avoid 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 the data quality team about a week or two to search for each new set of data. This means the data quality team needs to prioritize which assets need to be reviewed first and as a result many assets go un-reviewed.
  • Organizational red tape and red tape can often slow down data quality programs. Data is a company asset, so each 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 approval before implementing a data quality rule. This process can take weeks or even months when the data may have changed significantly.
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What can you do to improve cloud data quality?

It is important to use a strategy that takes these factors into account to ensure data quality in the cloud. Here are some tips for achieving data quality in the cloud:

  • Check the quality of your legacy and third-party data. Fix any errors found before migrating to the cloud. These quality checks will increase the cost and time to complete the project, but a thriving data environment in the cloud will be worth it.
  • Reconcile cloud data with legacy data to ensure data is not lost or modified during migration.
  • Establish governance and control over your cloud data and processes. Continuously monitor data quality and initiate corrective actions when errors are found. This prevents problems from getting out of hand and becoming too expensive to fix.

In addition to the traditional data quality process, data quality teams must analyze and set up predictive data checks, including data drift, anomaly, data inconsistency between sources, etc. Identify data errors and extend current data quality practices. Another strategy is to take a more agile approach to data quality and align with data operations teams to accelerate deployment of data quality checks in the cloud.

Migrating to the cloud is complex, and data quality should be a top 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 putting processes and tools in place, you can ensure data is of the highest quality and your cloud data projects have a greater chance of success.

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