Quality issues arise in organizations because of the availability of a large amount of data that is stored with them. When dealing with vast volumes of data, the sheer volume of fresh information becomes an important factor to consider when assessing if the data is reliable. Firms need accurate and precise data to deliver quality information for making business decisions. Data quality issues drive up operating expenses and create turmoil for downstream users. Analysts end up relying on faulty reports and deriving misleading inferences from the available data.
Quality data is required for organizations in making informed and accurate business decisions. The quality of data depends upon different factors such as accuracy, consistency, relevance, integrity, and validity. A firm can discover significant issues affecting the quality of data and ensure that shared data is eligible to be utilized for a specific purpose by monitoring data quality. Business organizations are considering data as an integral part of their production function. The impact of quality data has increased on almost all aspects of a business, from sales and marketing to create targeted content.
The study conducted by AIxOutlook states that an organization loses 20% of its total revenue and more than 30% of firms suffer from issues created due to low-quality data. Low-quality data may lead to financial loss for many firms, and quality data is necessary for a firm to gain profits.
The usefulness of data is determined according to the quality of the data. Organizations may end up making the wrong business choices and incur a loss when poor quality data is used for business purpose. And, given today’s fast-paced corporate environment, choices must be made even faster, increasing the risk.
Data Quality
The capacity of a given data set to accomplish an intended function is characterized as data quality. Features such as accuracy and relevance are important factors that determine the quality of data. Quality data enables individuals or enterprises to obtain accurate insights that help them in decision-making.
Data quality monitoring is carried out by data quality analysts, who evaluate and analyze each data quality indicator, compute an overall data quality score, and give organizations a percentage to indicate the correctness of their data. Data quality is measured based on the Data Quality Assessment Framework (DAQF), which consists of six dimensions:
• Completeness – The amount of data missing from a dataset is referred to as completeness. It is crucial for making accurate decisions.
• Validity – Information that does not adhere to specified corporate forms, policies, or processes
• Integrity – The reliability and trustworthiness of the information collected are known as integrity.
• Uniqueness – Uniqueness in data helps a firm to gain an upper hand in the market.
• Timeliness – refers to the relevance of data, whether it is updated on a timely basis or not.
• Consistency – Ensuring that data collection is done in accordance with the requirements.
These factors are considered while assessing the data quality and are given a score ranging from 0 to 100.
Maintaining data quality has become much more important to organizations. By meeting the basic data standards and giving better outputs, informed decisions can be obtained, which helps in making better decisions. Keeping high-quality data helps in providing better business imperatives, which enhances customer engagement and experience. The filtration of data helps in gaining quality information from both good and bad data, which helps in reducing risks.
The authenticity of data is highly dependent on the company’s corporate culture and the level of adaption across the board; it naturally enables organizations to follow compliance regulations and maintain the highest levels of accuracy and governance.
Conclusion
As the data explosion continues to rise, the importance of keeping high-quality data has become a crucial business process. Businesses will be fully dependent on data to formulate focused and accurate decisions. For that, accuracy and quality must be kept high. In the age of digitization, only data-driven enterprises can survive.
Enterprises require robust foundations in an increasingly complicated data landscape. A significant element is the availability of reliable data. Long-term data quality monitoring will reap rewards in the end. Data quality must be managed at the corporate level as data quantities expand, data requirements grow, and data flows via new channels. So, data quality should become one of the important factors in the digital world.
