Data collection is considered an integral part of every business process. However, data collection is not an end in itself rather, this data has to be refined and improved in quality such that meaningful data analysis can be performed and the conclusions drawn can be used to improve business processes.

Defining Data Quality: High and Low Quality Data

The term ‘quality’ can be defined as ‘conformance to standards’. Therefore, the expression ‘data quality’ is a measure of the standard of data corresponding to the extent of improvement required. As a natural corollary, any high quality data is “fit-to-use” and does not require refinement, as compared to low quality data which requires separation of noise (irrelevant data) from the data set, to make it eligible for analysis.

Why It Is Essential To Maintain Data Quality

Today, the importance of data quality cannot be overstated. A data quality check is essential, as low quality data may not only lead to inconclusive analysis, but it may also lead to wrong conclusions which could have catastrophic effects on the business. In addition, maintaining data quality has the following advantages:

1. Increases Accessibility:

Maintaining data quality increases accessibility to data, which makes it easier to identify and select the relevant data. Along with simplifying the process of data analytics, it also improves the statistical accuracy of the results, making the complete process more reliable and user friendly.

2. Enhances Satisfaction:

Excellent data quality helps save considerable time during the process of analysis, thus optimizing the work-flow as well as the satisfaction level of employees.

3. Facilitates Standardization:

High quality data facilitates standardization of data analysis, which creates an opportunity for process automation.

4. Better Decision Making:

Good decisions are based upon reliable, verifiable data. Superior data quality makes the task of comprehending data easier and improves the decision making ability of the whole organization.

5. Saves Costs:

High quality data saves costs associated with decisions taken on the basis of wrong data. Further, high quality data facilitates improvement in decision making, reduction in analysis time, and simplification of processes, which would indirectly result in cost savings.

6. Improves Customer Satisfaction:

Superior data quality facilitates better analysis of customer-related data, resulting in improved understanding of customer requirements. It improves the success rate of marketing campaigns by helping the marketing team reach the right target audience. This will ultimately improve customer satisfaction.

Criteria Used To Determine Data Quality:

Along with the growth of data analytics, there has been an evolution in data quality standards which has resulted in the development of multiple guidelines and frameworks for data quality. These data quality standards are based on multiple quality criteria. Some of them include:

1. Accessibility:

The data should be such that it can be readily located and accessed in multiple formats.

2. Relevance:

The data should be relevant; this means that it should serve the purpose for which it exists. Additionally, it should adequately represent the whole population. Greater the relevance of the data, higher would be the data quality.

3. Accuracy:

Data should be accurate with respect to estimates or quantities or characteristics described. Typing errors often result in a single category being treated as two different categories, defeating the purpose of analysis.

4. Reliability:

The data should be consistent and complete. This would guarantee objectivity in data and also increase the confidence of users. Further, any data which provides false information is bound to result in wrong decisions and ultimately, loss of revenue.

5. Usability:

Data should be interpretable i.e. users should be able to readily understand, use and analyze the data. Further, it should be accompanied by definitions of concepts and terminology, along with information about limitations of the data.

6. Up-to-Date:

The data set should be regularly updated. The delays between data collection and availability should be minimized. This will ensure that data is readily available and also remains up to date.

7. Confidentiality:

The data should be exclusive; the confidentiality of the data should be ensured and access to it should require authorization. This can be achieved through alignment of the data storage, retrieval, backup and transfer practices, along with the security protocols.

Auditable:

The structure, orientation and documentation of data should be such that it is auditable.

Business processes depend on correct and timely information. When information provided is incorrect or missing, it increases the probability of taking wrong decisions which can damage business prospects. Given the importance of data, it is essential that data cleansing and maintenance is regularly carried out for ensuring the correctness of data. The frameworks and guidelines for data available today promote standardization of data thus improving its accuracy, processing speed and reliability. High data quality would help businesses take decisions with higher accuracy.

‍

For information on how Invensis Technologies will deliver value to your business through Data Cleansing and Enrichment Outsourcing Services, please contact our team on US +1-302-261-9036; UK +44-203-411-0183; AUS +61-3-8820-5183; IND +91-80-4115-5233; or write to us at sales {at} invensis {dot} net.