Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose, and it is critical to all data governance initiatives within an organization. Data quality standards ensure that companies are making data-driven decisions to meet their business goals. If data issues, such as duplicate data, missing values, outliers, aren’t properly addressed, businesses increase their risk for negative business outcomes. According to a Gartner report, poor data quality costs organizations an average of USD 12.9 million each year1. As a result, data quality tools have emerged to mitigate the negative impact associated with poor data quality.
When data quality meets the standard for its intended use, data consumers can trust the data and leverage it to improve decision-making, leading to the development of new business strategies or optimization of existing ones. However, when a standard isn’t met, data quality tools provide value by helping businesses to diagnose underlying data issues. A root cause analysis enables teams to remedy data quality issues quickly and effectively.
Data quality isn’t only a priority for day-to-day business operations; as companies integrate artificial intelligence (AI) and automation technologies into their workflows, high-quality data will be crucial for the effective adoption of these tools. As the old saying goes, “garbage in, garbage out”, and this holds true for machine learning algorithms as well. If the algorithm is learning to predict or classify on bad data, we can expect that it will yield inaccurate results.
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Data quality, data integrity and data profiling are all interrelated with one another. Data quality is a broader category of criteria that organizations use to evaluate their data for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose. Data integrity focuses on only a subset of these attributes, specifically accuracy, consistency, and completeness. It also focuses on this more from the lens of data security, implementing safeguards to prevent against data corruption by malicious actors.
Data profiling, on the other hand, focuses on the process of reviewing and cleansing data to maintain data quality standards within an organization. This can also encompass the technology that support these processes.
Data quality is evaluated based on a number of dimensions, which can differ based on the source of information. These dimensions are used to categorize data quality metrics:
These metrics help teams conduct data quality assessments across their organizations to evaluate how informative and useful data is for a given purpose.
Over the last decade, developments within hybrid cloud, artificial intelligence, the Internet of Things (IoT), and edge computing have led to the exponential growth of big data. As a result, the practice of master data management (MDM) has become more complex, requiring more data stewards and rigorous safeguards to ensure good data quality.
Businesses rely on data quality management to support their data analytics initiatives, such as business intelligence dashboards. Without this, there can be devastating consequences, even ethical ones, depending on the industry (e.g. healthcare). Data quality solutions exist to help companies maximize the use of their data, and they have driven key benefits, such as:
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