Data Quality Tools Market Size

Data Quality Tools Market Analysis

The data quality tools market is expected to register a CAGR of 17.5% over the forecast period. Data quality tools generally address four primary areas: data cleansing, data integration, master data management, and metadata management. As data quality is a significant stake for large organizations, software companies are proposing increasing numbers of tools focusing on these issues. The focus of these tools is changing from specific applications (deduplication, address normalization, etc.) to a more global view that includes all aspects of data quality (profiling, rule detection, etc.).

• Furthermore, growing mobile connectivity and IoT adoption across all industries have resulted in a massive data explosion, necessitating data extraction from a variety of sources.The demand for data quality tool solutions is driven by these complex data types and formats.According to the Harvard Business Review (HBR), completing a unit of work with flawed data costs ten times more, and finding the right data quality tools has always been a challenge. One can implement a system of reliability by choosing and leveraging smart, workflow-driven, self-service data quality tools with embedded quality controls.
• Data quality tools generally address four primary areas: data cleansing, data integration, master data management, and metadata management. As data quality is a major concern for large organizations, software companies propose increasing the number of tools that address such issues. The scope of these tools is shifting from specific applications (deduplication, address normalization, etc.) to a more global perspective, integrating all areas of data quality (profiling, rule detection, etc.).
• Recent advances in mobile technology allowed users to automatically record data online, creating massive amounts of data that increased rapidly. Moreover, the capability and size of cloud computing infrastructures are continuing to accelerate, nearly beyond our abilities to leverage the opportunities provided.
• Moreover, the manufacturing sector handles multiple data streams that need to be analyzed to optimize business resources. These industries typically require handling routine, structured in-factory data, analog data, and information churned out from applications, including enterprise resource planning (ERP) systems and various process automation and control systems. Maintaining data quality would be significant for optimizing the manufacturing sector's supply chain. For instance, additive manufacturing (AM) needs tools to manage data to ensure quality, repeatability, traceability, and reliability, especially in the heavily regulated aviation and medical industries.
• Amid the COVID-19 outbreak, many companies were concerned about ensuring the quality and access to their data during this uncertain pandemic. The demand for various solutions that aid enterprises in data analytics has garnered significant attention and a positive trend in adoption. The global shift toward remote working and cloud adoption further intensified the demand for solutions that help increase work efficiency and effectiveness. Companies invested in processes and infrastructure to democratize data and enable access when the majority of the workforce works remotely as a result of the COVID-19 outbreak.