What is Data Quality Assurance (DQA)?
Data Quality Assurance (DQA) ensures data is in the right format and structure without errors before and during the acquisition to serve your strategic company goals and purposes. It detects inconsistencies and anomalies by performing data cleansing, eliminating obsolete information, removing outliers, and fulfilling missing data at an earlier stage.
Why data quality assurance?
As data specialists, we spend a large amount of time preparing data for analytics. From our experience, we know that data quality is one of the biggest struggles and challenges you can face when analyzing data. We know the importance and the role of data quality in performing accurate analysis and generating good reports to help you make optimal decisions.
The importance of data quality assurance:
Risk Mitigation
Organisations nowadays collect huge amounts of data. This data becomes an important asset for organisations, and just like any other asset, it has some value and therefore has some level of risk of getting modified or changed by internal and external factors and individuals. Influencing the state of data quality and therefore increasing the risk of compliance issues due to inaccurate reporting, can lead to fines and regulatory scrutiny.
Implementing data quality assurance procedures will help you to identify and address data issues that could lead to business risks and therefore take the right action at the right time. This way you can reduce risks related to data errors that affect your business’ operations and results.
Improving data quality reduces the risk of penalties and enhances regulatory compliance by ensuring and simplifying accurate compliance reporting and audits. Moreover, it helps your organisation to minimize risks associated with non-compliance, such as fines and reputational damage.
Data quality controls
Data Quality Assurance can help your organisation to protect the value of your data and define SMART metrics and measures that can help implement effective data controls to manage data quality audits more efficiently. Data Quality Assurance can also help implement policies, strategies, and governance measures to remediate different data security issues.
Decision-making improvement
Making the right decision requires high-quality data that helps generate good reports and get accurate and reliable insights therefore enabling better business decisions.
As data specialists, we use analytics daily to generate reports that help our financial audit colleagues with better and faster data analysis for the audit. However, sometimes due to the inaccurate data from the client’s systems, it becomes a challenge to get optimal results to help managers make better audit conclusions.
By establishing data quality procedures in our clients' organisations, these organisations can improve data quality management and therefore help generate data quality rules compatible with business data governance which will ensure the accuracy of data used for analysis and decision-making.
Cost Savings
Improving data quality may help reduce the time and resources needed to prepare data for the analysis, it can help reduce the inefficiencies associated with poor data quality therefore leading to cost reductions by reducing the amount of time spent on cleansing and correcting data errors.
Examples from our daily work show how bad-quality data can affect data analytics tasks and make it expensive for clients who struggle with inconsistent sales and purchase data which is entered manually. Entering data manually leads to inaccurate data which makes data analytics impossible or at a very high cost with the risk of wrong results.
AI and Competitive Advantage
Bad data quality may be a barrier to the use of data analytics and AI/machine learning models to gain competitive advantages. Competitive advantage based on modern technologies comes from creating business models that use AI and digital innovation where data quality is the heart of implementing these modern information technologies.
Implementing data quality assurance will help businesses differentiate from their competitors and allow organisations to bring their objectives to the next level by enabling them to use AI and machine learning models and techniques to achieve more accurate, efficient, and scalable solutions and therefore gain competitive advantages.
Moreover, improving data quality can support companies to develop new products and services, as a result, it can improve their market positioning and strategic decisions.
More information on data quality assurance?
If you want to know more about the importance of data quality assurance for your organisation, please get in touch with our Business Risk Services team to see how Grant Thornton can offer bespoke support for your data quality projects.
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