Cognitive Data Management Market Size (2024 - 2029)

Cognitive Data Management Market Analysis

The Cognitive Data Management Market is expected to grow at a CAGR of 21.7% over the forecast period 2021 to 2026. An acceleration in the usage of IoT-based devices, an escalation in the volume of complex data, and the growth of advanced technology such as AI have led to the enhanced adoption of cognitive data management across the globe. A recent survey by Infosys Limited found that AI and Cognitive Data Management are part of a real-world technology wave that shows tangible business results. According to the study, 86% of organizations have 'middle' or 'late-stage' AI deployments, and view it as a critical technology for future business initiatives. Nearly 75% also stated that AI is already helping to transform the way their organizations do business.

• The accelerated adoption of automation and cloud technologies across companies and businesses resulted in colossal data generation and cloud data traffic. Hence, it stoked the growth of data management software, which in turn has boosted the demand for the cognitive data management market. And over 90% of firms using the cloud as part of their businesses stated that the amount of internet traffic has been growing exponentially.
• According to Cisco Report, global IP traffic will reach an annual run rate of 2.3 zettabytes in 2020, up from an annual run rate of 870.3 exabytes in 2015. Furthermore, the IP traffic will grow 3-fold from 2015 to 2020, a compound annual growth rate of 22% due to the enhanced adoption of cloud technologies across the businesses.
• Progress in IoT based devices is also increasing the amount of digital data, emphasizing the need for Cognitive Data Management. According to Cisco, IoT is supposed to generate about 507.5 zettabytes (1 zettabyte = 1 trillion gigabytes) of data by 2019. Managing, screen through, and interpreting data is supposed to pose a massive challenge for organizations. Cognitive Data Analysis will be essential if IoT initiatives are anticipated to provide insights to drive higher productivity and revenues.
• The cloud deployment type providing a broad range of functionalities, such as pay-per-use model, ease inaccessibility, and multi-user support, is estimated to witness a significant market share in the global cognitive data management market. Owing to the benefits cloud deployment offers, it is witnessing accelerated progress in its adoption. According to Forbes, the need for cloud computing is anticipated to grow to USD 160 billion by 2020, achieving a growth rate of 19%.
• The BFSI vertical is estimated to exhibit a significant adoption of cognitive technologies. The financial institutions around the globe have been deploying such technologies to increase their predictive capabilities, improve the revenue forecast, and support their business planning & budgeting in particular. Also, it enables them to address risk and fraud management more efficiently.
• Besides, thriving data security apprehensions is a key restraint for the market. For instance, according to Thomson Reuters Corporation, the number of data breaches in the U.S. increased from 157 million in 2005 to 1.47 billion in 2019, while the number of exposed records jumped from around 67 million to 164.7 million during the same time frame.
• During the COVID-19 pandemic, the healthcare industry vertical is under enormous pressure to improve and provide PPE, prophylactic, ventilators, and anti-viral drugs globally. Healthcare organizations are utilizing advanced technologies, such as AI, analytics, and machine learning, to interpret the complex data around COVID-19 to monitor and reduce the impact of the virus.

Cognitive Data Management Industry Segmentation

Cognitive data management refers to the use of cognitive computing to automate manual activities in data management. This helps in reducing the administrative burden imposed by data management and minimize errors. Earlier, data management practitioners used manual processes to analyze the data. These initiatives helped developers and analysts to gain an understanding of their data and have improved their ability to comply with data regulations.