The Importance of Data Quality

Data quality is vital in the new era of big data. Companies rely on accurate data for nearly every facet of their business. All it takes is one error to cause major problems.

One report showed that poor data quality costs organizations around $12.9 million annually! You're putting your business at risk if you're not using data quality management software to ensure reliability and resilience.

What is Data Quality?

At its core, data quality is about reliability. Data quality refers to how well a dataset meets several standards. These include:

• Data Completeness: Missing values can lead to inaccurate analysis. To have high data quality, datasets need to be complete. Otherwise, they're unusable.
• Validity: Data validity refers to how the data matches required formatting rules. Data that's the incorrect type or out of applicable ranges isn't reliable.
• Accuracy: Data accuracy is the correctness of values based on the accepted source of truth.
• Consistency: Companies can use multiple sources to report a single metric. Consistency refers to how similar the data is across sources.
• Uniqueness: This standard refers to the amount of duplicate data in the dataset.
• Timeliness: Timeliness covers data readiness, ensuring that the information is ready within an acceptable timeframe.

Why Data Quality is Crucial

Your organization needs reliable data to succeed. No matter how you use it, low-quality data can cause issues that ripple throughout your company.

For example, say that you're using data for business intelligence. Making confident and impactful business decisions is only possible when the data you use is accurate. Prioritizing quality and using data quality management software can dramatically improve decision-making for leaders, giving you an advantage over the competition.

Data quality can also impact business operations. From managing supply chains to contacting clients for renewed sales agreements, incorrect data can cost you. Your teams need data resiliency to do their jobs. Even one small error can lead to substantial time and money wasted fixing problems. Furthermore, poor data quality can negatively impact your company's reputation among customers, suppliers and partners.

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