Organizations collect many types of data and need to manage the data quality. Data quality is crucial, and inaccurate data is unreliable. Sometimes, a single inaccurate data can wreak havoc and cause significant loss. Data quality monitoring is done to ensure the quality of each acquired data. Organizations do routine monitoring processes to maintain data quality in enterprises. When the monitoring system detects data that doesn’t meet the quality thresholds, it alerts the data administrators. Monitoring data quality involves the acquisition, implementation, distribution, and analysis of data.
The need for data quality management
Better performance: When data is managed well, a business’s basic operations are completed quickly and efficiently. High-quality data helps organizations to make better decisions at all levels of management.
Increased efficiency: Low-quality data leads to inefficient use of resources. However, when the data quality is monitored and maintained, it prevents wastage of resources and yields better results.
Outstanding results: Every business needs a good reputation to stand out from the competitors. High-quality data helps the organization maintain its reputation. Low-quality data is unreliable. Soon customers stop trusting the organization and availing their services.
How is data quality maintained?
Data cleansing
Data cleansing involves correcting duplicate records, data types, and substandard data representations. First, it ensures that the data standardization rules set by the organization are followed. Then, it customizes the data to fit the organization’s unique requirements.
Data profiling
Data profiling is done to monitor and cleanse data. It involves validating available data against the standard data, creating relationships between data, and verifying available data against the descriptions. Moreover, data profiling helps discover, expose, and understand inconsistencies in data.
The standard metrics used to measure data quality are:
Accuracy: The data must accurately reflect the object it describes.
Consistency: Two data values retrieved from separate data sets should not conflict.
Completeness: Data is complete only when it helps to obtain meaningful conclusions.
Integrity: Data is tested to ensure compliance with the organization’s data procedures.
Timeliness: If data is not ready when users need it, it fails to meet the organization’s data quality.
Data accuracy is the most significant feature of high-quality data. Inaccurate data leads to problems and makes people make incorrect conclusions. On the other hand, good data helps to make the right decisions about finance, marketing, and product development.
Benefits of good quality data
Informed decision-making: Good quality data helps to make better decisions. The decisions made have fewer risks and lead to consistent improvements in the results.
Better audience targeting: Good data helps to identify the right target audience and find potential customers with similar attributes. Organizations can develop products and services to meet the needs of the customers.
Effective marketing campaigns: Organizations can improve their marketing campaigns with better information about the target audience. They create content that appeals to the customers.
More straightforward data implementation: High-quality data is easier to use and increases the organization’s efficiency. Poor quality data makes the enterprise spend many hours fixing it to make it usable. Therefore, it affects the efficiency of the company.
Increased profitability: Effective marketing campaigns and increased efficiency enable the organization to profit more. Having good data helps organizations to focus on generating more revenue and profits.
Data quality monitoring is essential for all organizations to catch unexpected influxes of poor-quality data and track its source. Monitoring helps to spend less time on fixing bad data. In addition, tracking changes to data is crucial to making better decisions at the right time and achieving good results.
