Data masking is a process of protecting the sensitive and private information of the organization, such that a structurally similar but inauthentic version of the organization’s data is created while ensuring that its format remains similar to the original data sets and only the values are changed and scrambled appropriately. Data masking is also known as data obfuscation, data anonymization, and data scrambling. In the data masking process, the transformation of data can be done in a number of ways, such as encryption, shuffling of characters, and substituting words or characters, by which – the detection and reverse engineering of data is impossible to achieve.
Quadrant Knowledge Solutions’ Data Masking market research includes a detailed analysis of the global market regarding short-term and long-term growth opportunities, emerging technology trends, market trends, and future market outlook. The study provides a comprehensive market forecast analysis of the global market and the overall market adoption rate as well. This research provides strategic information for technology vendors to better understand the existing market and support their growth strategies, and for users to evaluate different vendors’ capabilities, competitive differentiation, and market position.
The research includes detailed competition analysis and vendor evaluation with the proprietary SPARK Matrix analysis. The SPARK Matrix includes ranking and positioning of the leading Data Masking vendors with a global impact including – Axiomatics, BMC Software, Broadcom, Comforte AG, DataSunrise, Delphix, EPI-USE Labs, IBM, Imperva, Informatica, K2View, Mage, Micro Focus, Microsoft, NextLabs, Oracle, PKWARE, IQVIA’s Privacy Analytics, Privitar, Protegrity, Redgate Software, SAP, SecuPi, Solix Technologies, and Thales Cloud Security.
According to Kunal Pakhale, Analyst at Quadrant Knowledge Solutions, “In this digital age, the market is likely to be driven by the growing volume of company data and the development of newer and finer business standards for executing niche business functions. The complex data environment is a threat to data privacy; today’s applications demand multiple copies of data sets for training, testing, and development environments; such increasing data environments necessitate robust data security methods and tools to detect and classify sensitive data at risk. To mitigate the risk of data breaches and unauthorized access, organizations mask the data when confidential data sets need to be shared with various stakeholders, such as application developers and external business partners like offshore testing organizations, suppliers, and customers, which are then utilized for user training, sales demos, or software testing. Data masking solutions are also increasingly being used for emerging use cases such as incorporating AI, ML, and automation as well as robust on-demand processing capability, scalability, and integration required to create a robust data masking framework”
2022 Data Masking SPARK Matrix
Source: Quadrant Knowledge Solutions
Table of Contents
- Key Research Findings
Market Overview and Technology Trends
Factors Influencing Technology Development and Market Growth
Market Adoption Trends
Competition Landscape and Analysis
- Competitive Overview and Analysis
- Key Competitive Factors and Technology Differentiators
SPARK Matrix™: Data Masking
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