Data Quality: The Importance of Quality in Data Management

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Quality in data management influences the reliability of the information that teams have. Discover good practices to achieve this.
Data quality becomes more critical than ever as organizations increasingly rely on data analytics for the many processes they run within them, from driving data-driven decision making to discovering new lines of business and optimizing tasks.

What is quality data and why is it essential? What actions can be taken to improve it?



HubSpot BLOG

Data professionals spend 40% of their time assessing or self employed database data quality, and the remaining 75% can take four hours or more to detect a data quality incident.

Source: The State of Data Quality 2022



Data quality as an essential part of MDM

What is meant by “quality data”?
Within data management , there is quality and it corresponds to a set of characteristics such as precision, consistency, reliability and integrity. In this sense, the data must:

Be consistent, complete and appropriate for their function.

Provide a unique vision.

Be correctly related and interrelated with all sources.
Having quality data is important because it offers a competitive advantage through better execution of daily operations, highlighted sales opportunities, a deeper understanding of customers and their demands, and closer relationships with them.



HubSpot BLOG 2




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In practice, companies have a major quality problem that extends from within the organization outwards, affecting aspects such as customer relations , decision-making and productivity. In such an environment, it is difficult to trust the information available , and this is precisely the objective of the quality function .



HubSpot BLOG

Poor data quality costs organizations an average of $12.9 million each year.

Source: Gartner





Best practices for obtaining quality data
Organizations can take a number of actions to improve the quality of their data . Among the most relevant are:

Identify precise links between data and key indicators: List the organization’s data quality issues and how they impact revenue and business metrics. After this diagnosis, begin outlining a program that defines scope, stakeholders, and an investment plan.

Understanding what data standard you want to achieve: This task falls to data and analytics leaders and interested users to identify what data they use and the required quality parameters.