What is Data Quality?

What is Data Quality?

Data quality simply means whether a particular set of data is reliable or not and whether it is good enough for a user to employ in making the decision or not. The quality is measured often by degrees.

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What is Data quality, in Practical Terms?

It helps to measure the condition of data, relying on various factors like how useful it is to the specific purpose, accuracy, completeness, timeliness, validity, consistency, and uniqueness. It is responsible for conducting quality data assessments that help to involve assessing and interpreting every quality. It also creates an aggregate score which helps to reflect the overall quality of data and helps to give the organization a good percentage rating which shows how accurate the data is.

Data quality indicates how good the data is and how useful is it for doing any task at hand. But this term also refers to implementing, planning, and controlling the activities which help to apply the needed management quality practices and also techniques that is required to ensure the data is valuable and actionable to the data consumers.

There are six data quality dimensions they are accuracy, completeness, consistency, Timeliness, Uniqueness and validity.

?How Do You Improve Data Quality?

Data quality management aims to leverage a balanced set of solutions for preventing future quality of data issues and clean data that fails to meet the quality of data KPIs. These actions help businesses to meet their current and future objectives. There is more to data quality than just cleaning data. With that in mind, here are eight compulsory disciplines which is used to prevent the quality of data problems and help to improve the quality of data by cleansing the information of all bad data that are data governance, data quality report, data matching, Master Data Management, Customer Data Management, Product Information Management, Customer Data Integration, Digital Assets Management.

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Data Quality Best Practices

Data analysts who improve the quality of data need to follow the following best practices:

• Make sure top-level management is involved. Data analysts can solve many data quality problems through cross-departmental participation.
• ?Include the management of data quality activities as part of the data governance framework. The framework sets data policies and data standards, and required roles, and offers a business glossary.
• ?Any data quality problem must begin with root cause analysis. If you don't address the root cause of the data problem, the problem will inevitably reoccur. Don't just address the symptoms of the disease; you have to treat the disease itself.
• ?Maintain a log of data quality issues. Each problem needs a record complete with information about the assigned data owner, the data controller involved, the impact of the problem, the final resolution, and the timing of the necessary procedures.
• ?Fill the data owner and data steward roles from the business side of your company, and fill the data steward roles from business or IT whenever possible and makes the most sense.

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Why data quality is important?

Bad data can have significant business implications for companies. Poor quality data is often seen as the source of operational problems, inaccurate analysis, and poorly conceived business strategies. Examples of economic damages that can result from data quality issues include additional costs when products are shipped to the wrong customer address, lost sales opportunities due to incorrect or incomplete customer records, and penalties for incorrect financial or regulatory reporting.

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Data quality management tools and techniques

Data quality projects typically include several additional steps as well. For example, the data quality management cycle outlined by data management consultant David Loshin begins with identifying and measuring the impact that bad data has on business operations. Furthermore, data quality rules are defined, performance goals are set for improving relevant data quality metrics, and specific data quality improvement processes are designed and implemented.

These processes include data cleaning or data cleansing to correct data errors and work to improve datasets by adding missing values, more current information, or additional records. Results are then monitored and measured against performance targets, and any remaining gaps in data quality provide a starting point for the next round of planned improvements. Such a cycle is intended to ensure that efforts to improve overall data quality continue even after the completion of individual projects.

Conclusion

The time has come to recognize that an organization can no longer treat data as a byproduct of its systems. In an intelligent enterprise, information is the product and data is its raw material. Because the quality of a product can only be as good as the quality of its raw materials, organizations must bite the bullet and invest in data quality improvement practices. While you can start small with limited data profiling and data cleansing activities, you need to quickly grow into a robust data quality improvement program focused on restoring a 360-degree view of your business across organizations.