The content of this report will be updated with the latest scenarios based on the global COVID-19 Pandemic
The Data Masking Market was valued at USD 483.90 million in 2019?and is expected to reach USD 1044.93 million by 2025, at a CAGR of 13.69% over the forecast period 2020?- 2025. In this information age, cyber security is very important. With the growth of breaches and malware attack, data masking is making its way in the data-driven industry.
- Moreover, the enhancement in IT infrastructure, increasing cyber-attacks, which is a serious concern for the enterprises, and surge in the expenditure for safety and security for private and confidential data have further increased the demand for data masking making companies to invest in technologies that enable data masking.
- For instance, in April 2018, Informatica announced AI-driven data privacy and protection solution that helped customers discover new and existing data assets, identify data proliferation, analyze data risk across the enterprise, and determine an effective course of remediation through risk simulation.
- Furthermore, increasing awareness has attributed to the continuous emphasis of companies to upgrade customer service. This is one of the major factors driving the adoption of data masking solutions.
- However, there is a dearth of the professional technicians who can handle the software because of technical complexities, which is creating a temporary hindrance for this industry.
Scope of the Report
Data masking is the process of transforming confidential data elements, such as trade secrets and personally identifying information (PII) systematically into realistic but fictionalized values. Owing to the rising incidences of these data breaches, data masking has become a necessity in the cyber security scenario. Besides this, the dynamic data masking helps to protect sensitive and personal data, while supporting outsourcing, offshoring, and cloud-based initiatives. Dynamic data masking helps to analyze big data without interrupting their regular operations, which are required for the business. Organizations mostly prefer dynamic data masking technology for analysis and testing of data.
Key Market Trends
The Healthcare Industry to Witness a Significant Growth
- The healthcare industry is one of the industries, which was impacted badly by the data breaching in 2018. According to Gemalto, healthcare industry continues to lead in the number of incidents (27%).
- The healthcare industry uses information technology, to support both doctors and patients and to improve the delivery of healthcare services. The use of electronic health record (EHR), where patient information, including protected health information, is stored, is widely used in the healthcare industry. Moreover, hospital administrative and financial staff uses a myriad of other applications to monitor hospital performance, in terms of financial efficiency and treatment success rates. In addition to that, patients use lots of different apps to communicate with doctors via mobile and wireless technologies.
- One of the largest healthcare data breaches reported so far in 2018 was Morrisville, NC-based AccuDoc Solutions, a billing company that operates the online payment system, discovered that some of its databases had been compromised between September 22 and September 29, 2018.
- Such incidents have created a path for data masking market, where the need for data security is vital. This has created a way for the data masking making necessary for the healthcare industry to implement data masking to avoid further loss.
Asia-Pacific to Have the Fastest Growth Rate
- Asia-Pacific is expected to witness a higher growth rate, when compared to other regions because of the developing countries, such as India and China, which are the two biggest economies of the world.
- Currently, these two countries are growing at a faster pace and many developmental activities are being done here for setting up of new manufacturing industries and new product launches. All these activities are contributing to big data. Lots of data are being stored on a daily basis which is very crucial. The rapid digitization among developing countries, such as India, China, and Singapore, has resulted in the production of the bulk of unstructured data.
- The flow of data in these regions has witnessed rapid growth, due to the adoption of IoT and remote sensors across many industries, which further expected to increase the market growth of data masking technology over the forecasted period. The need for solutions to avoid frauds is fueling the growth of data masking technology in Asia-Pacific.
- Owing to this, governments across countries in the region have taken several measures to promote the data security. For instance, in India, the need for data masking is strongly supported by the government. In July 2018, "Personal Data Protection Bill, 2018" was released that notes that the right to privacy is a fundamental right.
The data masking market is highly fragmented, due to the presence of many global players in this industry. This market is highly competitive. Some of the key competitive players in the data masking market are IBM Corporation, CA Technologies Inc., Informatica LLC, Oracle Corporation, and Delphix, among others. The major players in the data masking market follow the strategy of strategic partnership, mergers or acquisition of various local players, and product innovation to gain a competitive edge in the market.
- February 2019 - Informatica acquired AllSight an AI-enabled customer insights startup. With the AllSight acquisition, Informatica accelerated its data hub strategy. Using a data hub for customer engagement, enterprises can create a relationship between master, transaction, interaction, and reference data to discover rich, personalized behavioral insights. These insights can be used across the enterprise to connect omnichannel customer interactions in real time and ensure the delivery of the next best action. The new solution automates and simplifies profile and relationship unification, and scales AI across transactions and interactions in structured and unstructured data sources.
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