Data quality

Data quality is a measure of how well data is suitable for its intended purpose.

In today's data-driven world, data quality is a decisive factor for a company's success. High-quality data enables better decisions, more efficient processes, lower costs and higher customer benefits. Conversely, poor data quality can lead to incorrect decisions, inefficient processes, increased costs and a loss of customers.

Data Governance and data culture play a central role in data quality. We are happy to assist.

Factors that influence data quality

Data sources: The quality of data sources has a direct impact on the quality of the data. Internal data sources can be caused, for example, by manual data entry errors or inconsistencies in the data sources be affected, while external data sources may be incomplete, inaccurate, or out of date
Data collection processes: The way data is collected can affect the quality of the data. Manual data collection processes are more susceptible to errors than automated processes. In addition, inadequate data collection practices, such as the lack of data validation and cleansing processes, can result in data errors.
Data storage practices: The way data is stored can affect the quality of the data. Incorrect storage techniques can result in data damage or loss. In addition, inadequate data access and control mechanisms may result in unauthorized access to data or data changes.
Data processing processes: The way data is processed can influence the quality of the data. Incorrect data processing processes can lead to data errors or losses. In addition, inadequate data transformation processes can lead to data inconsistencies.
Data usage practices: How data is used can affect the quality of the data. Using data for purposes for which it is not intended may produce incorrect results. In addition, failure to comply with data protection regulations could result in data breaches.

Effects of poor data quality

Bad decisions: Poor data quality can lead to poor decisions that can have a negative impact on the company. For example, a company may mistakenly invest in unprofitable markets due to incorrect sales figures.
Inefficient processes: Poor data quality can lead to inefficient processes that waste time and money. For example, a company may have difficulty processing customer orders due to incomplete customer data.
Increased costs: Poor data quality can result in increased costs due to errors, rework, and losses. For example, a company may sell out or overstock products due to incorrect inventory data.
Loss of customers: Poor data quality can result in the loss of customers who are dissatisfied with the company. For example, a company may unfairly charge customers due to incorrect billing information.
Reputation loss: Poor data quality can lead to a loss of reputation, which can damage the company. For example, a company may lose customer trust due to data breaches.

Improving data quality through data governance

Data Governance is a framework for managing and controlling data throughout its life cycle. Data Governance can help improve data quality by:

Guidelines and standards for data quality define: These guidelines and standards ensure that all data meets the company's requirements. For example, a company may set a policy that states that all customer data must be up to date and accurate.
Determines responsibilities for data quality: These responsibilities ensure that all parties involved are involved in improving data quality. For example, a specific team can be given responsibility for data quality in a specific business area.
Processes for monitoring, measuring and improving data quality are defined: These processes enable the company to continuously improve data quality. For example, a company can implement a process to regularly identify and fix data errors.
Controls implemented to ensure data quality and prevent data errors: These controls help reduce the risk of data errors and maintain data quality. For example, a company can implement data validation rules to ensure that only valid data is entered into the system.

Data governance tools and processes

Data governance tools and processes play a critical role in the implementation of Data governance policies and practices and help companies improve the quality of their data. Key data governance tools and processes include:

Data quality management tools: These tools automate tasks to identify, assess, and fix data errors.
Data profiling tools: These tools profile data that provide insights into the quality and structure of the data.
Data monitoring tools: These tools continuously monitor data quality and identify deviations from standards.
Data lineage tools: These tools make it possible to track the origin and movement of data, which can be useful when resolving data errors.

Integrating data governance tools and processes with data management practices is critical to ensuring high data quality across the organization.

By using these tools and processes, companies can ensure that their data is reliable, consistent, and suitable for decision-making. This can lead to a competitive advantage and contribute to success in a data-driven company.

Data governance provides the framework and processes to improve data quality, while data governance tools and processes support the implementation of data governance policies and practices. By closely linking data quality and data governance, companies can ensure that their data is reliable, consistent, and suitable for decision-making. This can lead to a competitive advantage and contribute to success in a data-driven company.

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.