Estimating the quality of data depends on criteria such as exactness, completeness, uniformity, trustworthiness and whether it’s current. Assessing data quality levels can help organizations recognize flaws in the data that must be fixed and assess whether the data in their IT systems is appropriate for its intended use.
As the use of data has become more closely connected with business activities and businesses are increasingly relying on data analytics to make decisions, the focus on data quality in business systems has increased. Data quality management is a fundamental part of the overall data management process, and endeavors to enhance data quality are oftentimes closely related to data governance programs that try to ensure data is formatted and used uniformly across the organization.
Why data quality is important
Poor-quality data can have a bad effect on organizations. This is because Low-grade information is commonly seen as the cause of operational issues, inaccurate analytics, and ill-informed business plans. Economic losses due to data quality issues may include extra costs when goods are sent to the wrong customer, sales opportunities lost because of inaccurate or incomplete customer accounts, and penalties for incorrectly reported financial or regulatory compliance.
Also, corporate CEOs and high ranked managers often state that a lack of trust in data is a primary factor that prevents the use of Business Intelligent and data analytics tools to enhance decision-making within organizations. Consequently, it is important to have an efficient data quality management plan.
What is good data quality?
The precision of data is a significant element of superior data. To bypass technical issues in operational systems and incorrect outcomes in analytics applications, the data that is employed ought to be precise. Faulty data must be detected, listed, and fixed to make sure that corporate executives, data analysts, and other end users are dealing with valid data.
In addition to that, other facets which are fundamental parts of excellent data quality are as follows:
- completeness, with data sets containing all of the data elements they should;
- consistency, where there are no conflicts between the same data values in different systems or data sets;
- uniqueness, indicating a lack of duplicate data records in databases and data warehouses;
- timeliness or currency, meaning that data has been updated to keep it current and is available to use when it’s needed;
- validity, confirming that data contains the values it should and is structured properly; and
- conformity to the standard data formats created by an organization.
Evaluating the data quality
Organizations usually take the initial step of cataloging their data assets and performing a baseline study to measure the correctness, uniqueness, and legitimacy of data sets. The baseline results can then be compared to the data in the systems over time to help identify any new data quality issues.
In addition, companies often design a set of data quality rules based on the business needs for both operational and analytics data. These rules outline the essential quality levels in data sets and explain what information various data elements should include to be validated for accuracy, consistency, and other data quality qualities. After the rules are in place, a data management team typically carries out a data quality assessment to gauge the quality of data sets and document data mistakes and other problems – this process can be consistently repeated to keep the best data quality levels possible.
Data quality vs. data integrity
Data integrity and data quality are sometimes thought of as the same thing or have data integrity looked at as a part of data accuracy or as an additional characteristic of data quality. Generally speaking, data integrity is considered to be a more comprehensive idea that encompasses data quality, data governance and data security measures to deal with accuracy, uniformity and security all at once.
With the more comprehensive view, data integrity has two aspects: logical and physical. Logical integrity involves data quality standards and database elements such as referential integrity, which ensures that connected data components in different database tables are valid. Physical integrity consists of access control and other security protocols meant to stop data from being modified or harmed by unapproved people, as well as back-up and disaster recovery measures.
Advantages of data quality
From a monetary point of view, having a high data quality level allows organizations to bring down the expense of recognizing and rectifying incorrect data in their systems. Companies can also avoid operational errors and business process breakdowns that could upsurge operational costs and drop revenues.
Moreover, good data quality enhances the precision of analytics applications, which can result in better business decision-making that boosts sales, improves internal processes and gives organizations an advantage over competitors. High-grade data can support the usage of BI dashboards and analytics tools too – if analytics data is accepted as dependable, business users are more likely to rely on it instead of depending on gut feeling or their own spreadsheets.
Additionally, effective data quality management also gives data management teams the opportunity to concentrate on more productive tasks instead of cleaning up data sets.
In my next post(s) I shall try to cover `Data quality management tools and techniques`.