Data Quality (DQ) tools are software solutions that aid in cleaning data, improving data integrity, and ensuring the accuracy of information. Quality management is a key component of business intelligence and big data. Through Data Quality management, you can ensure that your organization’s data is accurate and relevant. These tools are frequently found within master data […]
Data Quality Tools and Solutions in 2023
Data Quality tools can help to make data more trustworthy and more manageable. Inaccurate data promotes poor decision-making, missed opportunities, and lower profits. As use of the cloud continues to grow and become more complex, Data Quality has become a critical issue. Data Quality tools, when used effectively, resolve the issues that cause these problems. […]
How to Become a Data Quality Analyst
Data quality refers to the planning and implementation of quality management measures for the data that companies generate. The idea is that the data should fit the end goal of the data consumer’s needs – and must follow specific quality dimensions to be deemed fit for use. The role of a data quality analyst (DQA) is to ensure […]
Why Data Quality Problems Plague Most Organizations (and What to Do About It)
For business leaders to make informed decisions, they need high-quality data. Unfortunately, most organizations – across all industries – have Data Quality problems that are directly impacting their company’s performance. Case in point: In a recent survey conducted by my company, practitioners were asked about the issues that plague their work, how much they trust their organization’s […]
Data Quality Management 101
Data Quality Management is necessary for dealing with the real challenge of low-quality data. Data Quality Management can stop the waste of time and energy required to deal with inaccurate data by manually reprocessing it. Low-quality data can hide problems in operations and make regulatory compliance a challenge. Good Data Quality Management is essential for […]
Data Quality Dimensions
Data Quality dimensions are useful concepts for improving the quality of data assets. Although Data Quality dimensions have been promoted for many years, descriptions of how to actually use them have often been somewhat vague. Data that is considered to be of high quality is consistent and unambiguous. Poor Data Quality results in inconsistent and […]
A Brief History of Data Quality
The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact checking and Data Governance, and, as a consequence, make decisions that give them an […]
Ease into Data Governance with a Data Quality Pilot
In the banking or pharmacy industry where regulations compel companies to have good governance in place, in industries such as publishing and telecom Data Governance often seems complicated and theoretical. That’s according to Sara Willovit, Product Data Governance at Becton Dickenson. Speaking at the DATAVERSITY® Enterprise Data World Conference, during her presentation titled Using Data […]
The Many Dimensions of Data Quality
Data can be anywhere. Companies store data in the cloud, in data warehouses, in data lakes, on old mainframes, in applications, on drives — even on paper spreadsheets. Every day we create 2.5 quintillion bytes of data, and there are no signs of this slowing down anytime soon. With so much available for data-driven decisions, […]
The Love/Hate Relationship between IT and Data Quality Tools
Click to learn more about author Kevin W. McCarthy. Anyone who has worked in Data Management knows the struggles that IT endures. They typically have limited resources around people and infrastructure. They have protocols and regulations that they must adhere to when handling data that is constantly changing and evolving (hello, GDPR). And the backlog […]