Is your data consistently of high quality? What can you do to ensure this? The importance of data and its impact on the quality of decisions is becoming more well-known, and inaccurate data can have disastrous consequences. Enterprises must ensure that they collect/source relevant data for their businesses, maintain and manage that data efficiently to ensure the quality and accuracy of master data, to analyze the high-quality data to achieve their goals. Based on the findings of data quality experts and practitioners, here is the Data Quality Framework that can help in a successful data quality assessment. But first, what is a data quality assessment? Let’s explore!
Data Quality Assessment
Using scientific and statistical methods, data quality assessment (DQA) determines whether and how the data meet the needs of projects and business processes and whether it is of the appropriate type and quantity to enable their intended uses. In terms of quality, it may serve as a set of guidelines and techniques that help describe data and assess and improve data quality in an application context.
Analyzing data quality (DQA) lets the organization know what data issues need fixing and develops data cleansing and enrichment strategies accordingly. Efforts like this are essential for ensuring quality assurance standards, the integrity of systems, and compliance. Issues with technical quality, such as poor standards and structure, missing data or incorrect default information, are easy to spot and fix, but more complex problems need more defined processes.
In DQA, qualitative problems are usually resolved, for instance, to produce accurate reports or to ensure standard processes rely on data correctly. The five-dimensional aspects of a data quality assessment involve
- Accuracy and reliability
- Serviceability
- Accessibility
- Methodological soundness
- Assurances of integrity
Accuracy and reliability – of data quality assessment
In the data quality sense, this indicates that the information in question is both accurate and reliable. Check whether the information represented in the data corresponds to reality. As an example, a customer with a bank account for $1 million has it? Accuracy is one of the most crucial characteristics of data quality since inaccurate information can result in dire consequences. Using the example above – if a customer’s bank account shows an error, it could be due to unauthorized access.
Serviceability –
Data serviceability refers to how effectively the data available meets the needs of users. Data serviceability is a practical aspect of data usability. By emphasizing “use,” the assumption is that the data are accessible. Relevance, timeliness and frequency, consistent practices and revision policies are significant aspects of usability.
External sector statistics must be comprehensive to serve the needs of various users. As a result, data and metadata must be constantly updated, produced regularly within a defined time frame, totally consistent, and governed by a clear revision policy.
Accessibility –
It is crucial to present statistics clearly and understandably to disseminate statistics efficiently. Accessibility enhances data quality available to everyone and provides timely and knowledgeable support services. There should be public access to relevant information in a format and delivery method appropriate for the target group. The language should be simple and to the point. Accessibility is a fundamental aspect of Data Quality Assessment because it proves an impartial and unbiased approach to data availability.
Methodological Soundness –
It is a dimension dedicated to implementing international standards, guidelines, and directives. Statisticians compile data according to established standards. Implementing policies of this type fosters Comparability internationally.
Assurance of Integrity –
Assesses the elements that maintain the data collection’s objectivity. It is essential to compile and disseminate statistics so that their confidence is maintained. Policies and practices follow professionalism and ethical standards. Transparency should reinforce these practices.
Other Characteristics considered in a DQA
Completeness
Information is said to be complete if it is comprehensive. Consider data completeness when evaluating the accuracy of the data; for instance, in a customer data record, you may use a customer’s first and last name, but not the middle initial.
Completeness is one of the characteristics of data quality, so why is it important? The information might be unusable if it is incomplete. Imagine you are sending out a mailer. For the mail to go to a customer’s address, the last name is crucial. Without it, the information is incomplete.
Reliability
An indication of reliable data is devoid of contradictory information in a different source or system. The information is unreliable if, for example, a patient’s birthday is different from one system to another.
Data reliability is a vital characteristic. You cannot rely on data that contradicts itself. The risk of making a mistake can cost your organization money and jeopardize your reputation.
Relevance
In assessing data quality, relevance becomes crucial since you need to understand why you want to collect this data. Make sure you don’t collect information for the sake of capturing it. In terms of data quality, why does relevance matter? You are wasting your time and money if you search for irrelevant information.
It is a fact that data quality plays a critical role in business processes and provides management with information about business performance. We have discussed in this article what data quality is and how to conduct a successful data quality assessment. Because business data is the foundation for decision-making within a company, data must meet sufficient quality to support sound decisions.
