Characteristics of Big Data Auditing

With the development of information technology, the explosive growth of the volume of data and the increasing importance of enterprise data governance, big data auditing, as an emerging auditing approach, is gradually being emphasized. Big data auditing has many characteristics that traditional auditing methods do not have, and we will introduce the characteristics of big data auditing from the following aspects.

One, data volume, speed

Big data auditing data volume is usually several times or even dozens of times the traditional auditing data, the speed of its data processing is also faster, the data storage, integration, analysis and visualization and other processing capabilities require more. Big data auditing requires the use of big data technologies, such as Hadoop, Spark, etc., to achieve high efficiency in data processing and data analysis.

Two, data diversity

Traditional auditing usually can only deal with structured data, while big data auditing can deal with a variety of types of data, including structured data, semi-structured data and unstructured data. Big data auditing can integrate and analyze multiple data sources of an enterprise to provide a more comprehensive understanding of its business.

Third, high data accuracy

Big data auditing can help auditors analyze data in a more refined way, so as to more accurately identify potential problems and risks. Through big data auditing, auditors can conduct more in-depth mining and analysis of the data to find anomalies and patterns in the data, so that potential problems can be identified and dealt with in a timely manner.

Four, data visualization

Big data auditing can present the results of data analysis through data visualization, so that auditors can more clearly understand the business situation and potential risks. Big data auditing can be achieved through data visualization tools, such as Tableau, QlikView, etc., to improve the auditor's ability to understand and analyze data.

V. Data security

Big data auditing requires more data security than traditional auditing, and requires more stringent data security measures to protect the confidentiality, integrity and availability of data. In the process of big data auditing, the need for data encryption, backup and recovery measures to ensure data security.

In summary, big data auditing is characterized by large data volume, fast speed, data diversity, high data accuracy, data visualization and data security. Big data auditing requires auditors to have a higher ability to analyze and process data, but also need to use more advanced technologies and measures to ensure data security and integrity, so as to achieve a more accurate, comprehensive and efficient audit.