Data integrity precisely is the assurance and consistency of data accuracy, completion, reliability, and uniformity over its life cycle. It is also an important prospect to the design, implementation, and usage of any system that stores, processes, and recovers data. There is a tendency to use the term data quality as a proxy of data integrity, but two of these hold different meanings and definitions. Data integrity refers to the features that determine the reliability of the information in terms of physical and logical validity while data quality refers to the aspects that determine the authenticity of information including planning, decision making, operations, etc.

Data quality consists of multiple factors-accuracy, completeness, consistency, usefulness and validity.

Accuracy

It is one of the major components of data quality. Accurate data collection helps in drive sales, reduces financial investments on ineffective strategies, and enhances the data quality. Authentic or accurate data assists to land a progressive decision followed by actions of an enterprise.

Completeness

In a data quality framework, completion of data is very much necessary. Completion of data refers to the full availability of data in the data set. The measurement of complete data is done by finding out the missing record entries.

Consistency

Consistency in data quality is the reflection of the same information and synchronization among each other in the entire business. In more firm words, data values taken from separate data sets must not clash with each other.

Usefulness

Proper and accurate data increases the confidence level while taking any decision and decreases the risk of heading towards the wrong way. It provides information to the user that is required to do a task efficiently.

Validity

This aspect in the data quality dimension, indicates information that doesn’t obey or follow specific business rules and formats, for example, date of birth. In some systems, if a person doesn’t enter his/her birthday in the format as instructed, it remains invalid. But popular survey engines have structured the format in such a way that while entering the date of birth, the date picker will automatically pop up, resulting in zero chance of invalidity.

Poor data quality can seriously turn out dangerous while taking decisions resulting in massive damage to the reputation of a business. There can be various reasons for poor data collection. Some of them are an excessive amount of data collected, error in communication and data coding, unclear and wrong interpretations, inefficient collection process, incompetent field staff, and poor collection tool, etc.

So to conclude with, for ensuring quality in data collection, we have to keep the above-mentioned factors in mind while collecting data then building up a proper dataset for analysis will easily be accomplished. RomaScript provides features like response validation logic, regular expression matches and OLETA (Online Text Evaluation for Text Analysis) to help clients ensure quality in the data they capture.

Try out RomaScript for faster and easier programming and quicker and quality data collection.