The global AI Data Management Market size is estimated to grow from USD 25.1 billion in 2023 to USD 70.2 billion by 2028, at a CAGR of 22.8% during the forecast period, according to research report by MarketsandMarkets™.

AI data management refers to artificial intelligence that leverages a variety of data types, such as video, audio, speech, images, text, and conventional numerical datasets, to enhance its ability to make more precise predictions, draw insightful conclusions, and provide accurate solutions to real-world challenges.

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This approach involves training AI systems to synthesize and process diverse data sources concurrently, enabling them to better understand content and context, a significant improvement compared to earlier AI models.

Services segment to account for higher CAGR during the forecast period.

The services segment within the AI data management market encompasses a wide array of such as consulting services providing strategic guidance and advisory support, system integration & implementation to ensure smooth incorporation of AI solutions into existing frameworks, and support & maintenance for ongoing assistance and issue resolution. Data migration services aid in the seamless transfer of information to AI-enabled platforms, while AI change management and adoption services focus on organizational readiness and user adoption. Moreover, AI platform administration services are instrumental in managing and optimizing the functionality of AI systems, ensuring they operate at their full potential. This comprehensive suite of services forms the backbone for enterprises aiming to leverage AI for data management, enabling them to harness its transformative power effectively.

Cloud segment is expected to hold the largest market size for the year 2023.

Cloud-based solutions have become integral to the effective deployment and scalability of AI applications. Leveraging cloud infrastructure allows organizations to store and process vast amounts of data efficiently, providing the computational power required for complex AI algorithms. Cloud services in AI data management offer flexibility, enabling businesses to scale resources up or down based on demand, optimizing costs. Moreover, cloud platforms provide a collaborative and accessible environment for data storage, sharing, and analysis, fostering seamless collaboration among teams. The Cloud segment not only enhances the agility of AI implementation but also facilitates the integration of cutting-edge technologies, enabling organizations to stay at the forefront of innovation in the rapidly evolving field of AI data management.

The healthcare and life sciences vertical is projected to grow at the highest CAGR during the forecast period.

AI data management in healthcare & life sciences sector involves the comprehensive handling of vast amounts of patient data, genomic information, clinical trial results, and other healthcare-related data. Through AI-powered analytics and machine learning algorithms, healthcare organizations can derive actionable insights from complex datasets. This enables personalized medicine, predictive analytics for disease diagnosis and prognosis, and the identification of potential drug candidates. In addition to advancing clinical decision-making, AI data management enhances operational efficiency by streamlining administrative processes, optimizing resource allocation, and improving patient outcomes through data-driven interventions.

Europe is expected to hold the second-largest market size for the estimated year.

Europe has been actively engaged in the evolution and adoption of AI data management solutions, showcasing significant advancements and a growing market landscape. Countries within Europe, including the UK, Germany, France, and the Nordic countries, have been investing in AI research and development, fostering innovation in data-driven technologies. The region emphasizes data privacy and security, leading to the development of robust regulations like GDPR (General Data Protection Regulation), which has significantly influenced how AI data management is approached, ensuring ethical and responsible data practices. Europe's collaborative approach to research and innovation fosters partnerships between academia, industry, and governments, fueling the continuous growth and innovation in AI data management solutions.

The major AI data management and service providers include Microsoft (US), AWS (US), IBM (US), Google (US), Oracle (US), Salesforce (US), SAP (Germany), SAS Institute (US), HPE (US), Snowflake (US), Teradata (US), Informatica (US), Databricks (US), TIBCO Software (US), Qlik (US), Collibra (US), Dataiku (US), Alteryx (US), Datamatics Business Solutions (US), Accenture (Ireland), Ataccama (Canada), Reltio (US), Tamr (US), ThoughtSpot (US), AtScale (US), Alation (US), Clarifai (US), DDN Storage (US), Dataloop AI (US) Astera Software (US). These companies have used both organic and inorganic growth strategies such as product launches, acquisitions, and partnerships to strengthen their position in the AI data management market.

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Key Dynamic Factors For AI Data Management Market:

Data Security and Privacy Issues:

The need to protect data security and privacy is growing as artificial intelligence (AI) and machine learning are used more often. Rules like the General Data Protection Regulation (GDPR) have a big impact on how businesses handle and use data, which affects how AI data management solutions are developed.

Quick Increase in Data Volume:

AI data management is fueled by the exponential growth of data produced by several sources, including as social media, IoT devices, and other digital platforms. There is a great need for solutions that can effectively handle and analyse massive volumes of data.

Developments in Cloud Computing

One important factor is the proliferation of cloud-based solutions for data processing and storage. Platforms in the cloud offer flexibility and scalability.

AI Incorporation into Business Processes:

Effective data management solutions are becoming more and more necessary as artificial intelligence is incorporated into corporate processes. Businesses are searching for systems that can improve decision-making by smoothly integrating AI into their current workflows.

Data Governance Framework Evolution:

To guarantee data quality, compliance, and accountability, strong data governance frameworks must be developed and put into place. For AI data management systems to satisfy organisational norms and legal obligations, they must be in line with these frameworks.

Variety in Data Types:

AI data management solutions need to be flexible and able to handle a wide range of data sources because to the diversity of data kinds that exist, including text, photos, multimedia, structured and unstructured data, and more.

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Competitive and Segmentation Analysis:

The AI data management market is characterised by fierce rivalry and a wide range of products, according to competitive and segmentation study. Prominent entities in this domain consistently endeavour to set themselves apart by creating cutting-edge solutions that cater to the changing requirements of enterprises in managing extensive and intricate datasets. The market is divided into segments according to a number of criteria, such as end-users (sector verticals like healthcare, finance, and retail), deployment strategies (on-premises and cloud-based), and data types (structured and unstructured). Businesses frequently get into strategic alliances, mergers, and acquisitions to improve their capacities and increase their market share. Additionally, suppliers are vying to offer increasingly advanced algorithms and analytics tools as the integration of artificial intelligence into data management solutions continues to be a key focus.

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