Data Integrity and Data quality are terms used to characterize and describe the condition of data.
Data integrity is the phrase used to describe the certainty the data which has been collected is accurate and unaltered. Data which has been changed by an unknown source loses its integrity. Data quality refers to the completeness and importance of the data in question.
Below, we explore the nuances pertaining to the differences between data quality and data integrity.
Data Integrity
Data Integrity simply refers to the accuracy, fidelity and consistency of data over its specific lifecycle. The data requires a certain degree of maintenance and assurance to keep up with its consistency. Such consistency ensures the data is efficaciously liable with its critical aspects depending on the design or task required. The integrity of data enables its usage and proper implementation in organizations and companies.
It is candid clear that compromised and distorted data is essential of little or no use to an organization especially in consideration of the faults and flawed results associated with sensitive data loss and miscalculations. Such issues have made maintaining data integrity become such a core principle for any organization.
Data Quality
Data Quality is the measure of the state or condition of data with factors such as consistency, accuracy, competence, authenticity and whether the data is outdated or not. This is merely the standard of qualitative, effectiveness and quantitative of various pieces of information. Data is generally regarded as quality data if it can be efficiently used for its intended purposes and operations. The data should have a positive and reliable impact on the nature of decision making and planning.
An organization that implements the usage of an improved data quality leads to the success of the organizational activities as a result of the effectiveness of the decision-making process. High-quality data brings about a certain heightened level of confidence when it comes to making decisions and planning on the execution of strategies. Enterprises have highly invested in quality data to reduce the risks that are associated with dreadful and unsatisfactory data. Moreover, the quality of data facilitates the consistency of organizational activities.
Data Integrity represents the validity and accuracy of data while data quality shows the importance or usefulness of data and its ability to meet the expected purpose and demands. Data integrity could be seen as the complete opposite of data corruption. Data Quality is regarded as a subset of data integrity.
Maintaining Data Quality
When it comes to data quality, the data has to have certain features to be considered good quality data. The features include completeness, validity, uniqueness, consistency and timely. The features highly differentiate the quality of data from the integrity of data.
Completeness– This is where the data presented is considered whether or not it represents the larger percentage of the total amount of data that is actually needed.
Validity– In this case, the data has to be able to meet the intended purpose to which it was collected.
Uniqueness- The quality of data has to be fostered with authenticity and the stored data records should not have entries that are extraneous and redundant.
Consistency– The data should be kept and represented in a standard way that makes it easily accessible.
Timely– In order for data to benefit an organization, it has to be provided at the right time and the data has to sufficiently be up to date for its intended purpose.
Maintaining Data Integrity
When it comes to Data Integrity, unlike data quality, there is a provision of context on accurate and reliable data. It ensures the databases make use of information that is highly relevant and complete. Data Integrity is differentiated from data quality in regard to four main pillars. From the pillars, it is seen how data quality is one of the main components of data integrity. The other pillars include local intelligence, data integration and data enrichment. They create the distinctive and unique character of Data Integrity.
Data enrichment– This is where the organization merges and compares data from an external source to an existing database of the first-party consumer data. This data enhances the making of more informed decisions.
Data integration– Data should be easily accessible and easy to read. It should be integrated or partitioned into a particular view that quickly provides the enterprise with enhanced visibility of the data.
Location Intelligence– This requires some added features to data that make it more actionable and enforceable for organizations. This is done by adding some richness and complexity to give the data more information through location insight and analytics.
Data quality– This simply requires data to be complete, unique, timely, consistent and valid. Such features make the data useful and reliable for the enterprise.
Contrasting Data Quality and Data Integrity
Data quality refers to a state of complete features and characteristics that show the efficiency of information used to address specific needs with real-world repercussions and implications. In retrospect to this, data integrity is contrasted and regarded as the lack of unintended change to the data involving two immediate updates to the records of data.
In addition, in order to maintain and implement the integrity of data, one must ensure numeric columns do not accept or use alphabetic data. As for quality data, the data has to meet the time standards in relation to outdated information that is no longer necessary.
Measuring Data Integrity
There are various factors involved in the measurement and evaluation of both data quality and data integrity. In the measurement of data integrity, the ratio of data to errors is usually considered. This enables an organization to find out the number of known and unknown errors.
The known errors could be due to incomplete records and redundant entries. The empty values are also considered because they are proof that there is information missing due to incomplete records or being recorded in the wrong field. It also key to consider the data storage costs, consistency and validity in order to get the accurate Key Performance Indicators that are useful for the organization.
Furthermore, data integrity can be measured through checksum hashing which is a method where a checksum value of a part of data is calculated and included with the data. The checksum hashing method is usually performed again after the data has been provided.
Measuring Data Quality
When it comes to data quality, its measurement is done differently depending on the organization. The ratio of data to errors is considered and it is usually the most common type of data quality metric. The data transformation error rates, amount of dark data, email bounce rates, data storage costs, time and number of empty values are all identified and calculated to give the key performance indicators. Such indicators track and help improve the efficacy of data quality improvement strategies and efforts. The difference between data quality and data integrity can evidently be depicted from the distinct strategies used to measure them.
Maintaining Data Quality & Integrity
Data quality and Data integrity can both be maintained in different ways independent from each other. For instance, data quality can be maintained through strategies such as building a data quality team. The team is involved in carrying out the tasks required in data maintenance. Data quality is ensured by not cherry-picking the data. This is where only a certain piece of data is considered and some are ignored.
This reduces the level of accuracy and competency. The margin for error should always be considered especially because data is not always perfect. There is minute but possible chances that the data provided is not a hundred per cent authentic. Furthermore, change should be easily accepted and adapted to. This is mere because data is always subject to change and evolvement.
Last but not least, in order to foster the quality of data, you have to sweat the small stuff. This is where one is keen and considers all possible events without neglecting anything regardless of how insignificant it is.
Data Integrity can also be maintained through some specific strategies. Performing risk-based validation activities highly fosters the integrity of the data involved. One is also required to select appropriate system and service providers who are well aware of the rules placed in the organization. This would reduce the chances of data corruption. The audit trails have to be audited from time to time to facilitate consistency and avoid incomplete records.
The software systems should be able to meet the changing demands of technology when it comes to storing and managing data. When it comes to the validated systems, there is a need to ensure that you deploy an IT environment that is fully qualified which would ensure the data meets some of the expected unique features. In addition to this, the organization has to have a reliable plan for the continuity of the business.
There also has to be disaster recovery methods that would retrieve any lost data to ensure the data integrity is maintained. Such maintenance methods and strategies clearly show the differences between data integrity and data quality.
Conclusion
It is crystal clear that while data quality shows whether data is accurate and reliable, data integrity goes a step further in the data analysis. Data can be considered an organization’s most valuable asset but only when it is accurate and trustworthy. In conclusion, both data quality and data integrity have to be considered for the success of any enterprise.
