An approach to Data Quality

IBM estimates that bad data costs the US economy $3.1 Trillion per year.?The costs come from the time employees must spend correcting bad data and errors that cause mistakes.?Clearly improving the quality of data is an opportunity so in this post we’ll share some steps we take when implementing Data Quality programs.

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Data quality management refers to the process of ensuring that data is accurate, complete, consistent, and timely, and that it meets the needs of its intended users.?Below, are some examples of how to execute on Data Quality initiatives:

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·?????Data Quality Assessment: Conduct data quality assessments to identify data quality issues and assess the impact of these issues on business operations. For example, we helped a financial services company identify data quality issues in their customer data and developed a data quality dashboard that allowed the company to monitor data quality issues in real-time.

·?????Data Profiling: Develop data profiling processes to identify and assess data quality issues. For example, we helped a healthcare organization develop a data profiling process to identify data quality issues in their electronic health record system. This allowed the organization to identify and resolve data quality issues before they impacted patient care.

·?????Data Quality Rules: Develop and implement data quality rules to ensure data accuracy and consistency. For example, we helped a retail company develop data quality rules to ensure that their product data was accurate and consistent across all systems and applications. This allowed the company to improve the accuracy of their product data and streamline their supply chain operations.

·?????Data Cleansing: Develop and implement data cleansing processes to improve data quality. For example, Data Meaning helped a telecommunications company develop a data cleansing process to remove duplicate customer records from their customer database. This allowed the company to improve the accuracy of their customer data and streamline their customer service operations.

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In all these examples, our approach is to work closely with stakeholders to develop data quality management programs that are tailored to the specific needs of the organization. ??Regardless of which stage of data management planning your organization might fall, we’re here to help.?Data Meaning has developed Data Quality solution blueprints to help accelerate your organization’s movement along the data and analytics maturity curve.?Don't wait, contact us at [email protected] to ensure data quality and to make the most of your analytics investment.