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Data quality encompasses a variety of factors such as accuracy, completeness, consistency, and timeliness of the data, as well as its relevance and integrity. Ensuring data quality is important for making accurate and reliable decisions based on the data and for maintaining the credibility of the information it contains. Data quality management is the process of identifying, assessing, and improving the quality of data. It involves implementing standard methodologies and procedures for data validation, monitoring, and analysis and ensuring compliance with relevant regulations and industry standards.
Data quality is important for a number of reasons. One of the most critical is that poor data quality can lead to inaccurate or unreliable conclusions, which in turn can negatively impact important business decisions. Additionally, data quality is beneficial for maintaining stakeholders' credibility and trust and ensuring compliance with regulations and industry standards. Furthermore, poor data quality can lead to inefficiencies and wasted resources, such as when data must be corrected or when duplicate records must be removed. Overall, data quality is critical for ensuring that data is useful, accurate, and valuable for the intended purpose.
Data lakes are a popular data storage and management solution, but they can also be a source of data quality issues. Data lakehouses are an evolution of data lakes, where data governance and management are integrated with the data lake to address data quality issues. Here are a few benefits of data quality within data lakehouse:
Overall, data quality within data lakehouse can help organizations manage and utilize their data more effectively, leading to better business decisions and overall performance.