Global AI Data Management Market 2024-2031

Global AI Data Management Market 2024-2031

Global AI Data Management Market Size, Share & Trends Analysis Report by Data Type (Audio, Speech and Voice, Image, Text and Video), by Application (Data Augmentation, Data Anonymization & Compression, Exploratory Data Analysis, Imputation Predictive Modeling and Process Automation) and by Verticals (BFSI, Retail & E-commerce, Government & Defense, Healthcare & Life Sciences, Manufacturing, Energy & Utilities, Media & Entertainment, IT & Telecommunications and Others) Forecast Period (2024-2031)

Global AI data management market is anticipated to grow at a significant CAGR of 15.7% during the forecast period (2024-2031). The growth of the AI data management market is attributed to the incorporation of machine learning algorithms with data management systems that has been important for withdrawing cherished awareness from extensive datasets driving the growth of the market. AI data management involves ensuring data quality through techniques like data cleaning, normalization, and validation, which enhance the performance and reliability of AI systems. AI data management combining and aggregating data from various sources using techniques like data integration pipelines and data lakes facilitates effective analysis and modeling.

Market Dynamics

Real-Time Data Processing

Real-time data processing enables instantaneous decision-making. Rather than being kept on file, it is made accessible to encourage discoveries as soon as feasible, advancing the business outcomes, efficiency, and profitability of businesses. Businesses benefit greatly from real-time data because it offers invaluable insights derived from the real-time processing of data sets. Real-time data is extremely beneficial to enterprise businesses. It may yield insights that improve operations, increase IT architecture visibility and monitoring, maximize business outcomes, and even improve customer experiences in general. Both batch and real-time processing involve the division of massive amounts of data into groups according to transactions, which must be gathered over time to provide insights. To generate insights, data is continuously batched over a predetermined timeframe rather than in real-time.

Increasing Adoption of Automated Data Labeling Tools and Techniques

The growing use of automated AI data management systems involves the process of annotating or tagging raw data with specific labels or categories, enabling AI systems to recognize patterns and make accurate predictions. The exponential growth of data and the increasing complexity of machine learning algorithms have led to a surge in the demand for data labeling services. Businesses across various industries, including healthcare, finance, retail, and manufacturing, require labeled data to train their AI models effectively. However, many organizations lack the internal resources, expertise, and time required to label large volumes of data accurately. This creates an opportunity for data labeling service providers to offer their specialized skills and infrastructure.

Market Segmentation

Our in-depth analysis of the global AI data management market includes the following segments by data type, application, and verticals.

Based on data type, the market is sub-segmented into audio, speech, voice, image, text, and video.

Based on application, the market is sub-segmented into data augmentation, data anonymization & compression, exploratory data analysis, imputation predictive modeling, and process automation.

Based on verticals, the market is sub-segmented into BFSI, retail &e-commerce, government &defense, healthcare & life sciences, manufacturing, energy & utilities, media & entertainment, IT & telecommunications, and others (travel & hospitality, education, transportation & logistics).

Process Automation is Projected to Emerge as the Largest Segment

Based on the application, the global AI data management market is sub-segmented into data augmentation, data anonymization & compression, exploratory data analysis, imputation predictive modeling, and process automation. Among these, process automation sub-segment is expected to hold the largest share of the market. The primary factors supporting the segment's growth include increasing demand for AI data management automation to manage data production, storage, archive, and destruction. Using AI technology to manage an organization's data assets deliberately and methodically can enhance data quality, analysis, and decision-making. This is known as AI data management. It encompasses all of the protocols, policies, and technical techniques used to effectively gather, arrange, store, and use data. For instance, in January 2024, Automation Anywhere announced the availability of the industry’s first specialized, generative AI automation model to dramatically improve process automation development cycle times and is set to transform the automation market. The Automation Success platform’s responsible AI Layer enforces enterprise security and data privacy standards for safe AI governance.

BFSI Sub-segment to Hold a Considerable Market Share

Based on the verticals, the global AI data management market is sub-segmented into BFSI, retail & e-commerce, government & defense, healthcare & life sciences, manufacturing, energy & utilities, media & entertainment, IT & telecommunications, and others (travel & hospitality, education, transportation & logistics). Among these, the BFSI sub-segment is expected to hold a considerable share of the market. The segmental growth is attributed to the increasing immense volume of financial transactions, customer data, and regulatory requirements, Al-powered data management solutions are employed to streamline operations, enhance customer experiences, mitigate risks, and drive strategic decision-making. Furthermore, by ensuring data confidentiality, privacy, and correctness, these solutions help organizations comply with strict rules. In an increasingly digital and data-centric economy, the BFSI sector depends largely on Al data management to promote innovation, enhance operational effectiveness, and preserve competitiveness.

Regional Outlook

The global AI data management market is further segmented based on geography including North America (the US, and Canada), Europe (UK, Italy, Spain, Germany, France, and the Rest of Europe), Asia-Pacific (India, China, Japan, South Korea, and Rest of Asia-Pacific), and the Rest of the World (the Middle East & Africa, and Latin America).

Increasing AI Data Management Adoption in Asia-Pacific

According to the World Economic Forum, in October 2023, the Asia-Pacific region moved from the digital era to the intelligent era, computing power is the decisive factor for AI innovation and the core productivity of the digital economy.

The AI data management market in China is diverse and covers various applications, such as natural language processing, computer vision, robotics, autonomous vehicles, and virtual assistants. With its substantial population and extensive data resources, China provides a fertile environment for developing and implementing AI technologies.

North America Holds Major Market Share

Among all the regions, North America holds a significant share owing to numerous prominent companies and AI data management providers. The growth is attributed to the increasing expansion of its technological hub. The region boasts renowned tech giants, startups, and research institutions driving progress in AI and data management. According to the United States Census Bureau, in November 2023, 3.8% of businesses reported using AI to produce goods and services but it is more widely used in certain industries such as information and tech sectors. Businesses in the Information sector reported greater levels of AI use than the national average with 13.8% of businesses using the technology.

The major companies serving the global AI data management market include Amazon Web Services, Inc., Google LLC, IBM Corp., Microsoft Corp., and Oracle Corp., among others. The market players are increasingly focusing on business expansion and product development by applying strategies such as collaborations, mergers, and acquisitions to stay competitive in the market. For instance, in February 2024, Wipro Ltd. and IBM Corp. collaborated to offer new AI services. Wipro Ltd., a technology services and consulting company, launched Wipro Enterprise Artificial Intelligence (AI)-Ready Platform, a new service that allows clients to create their enterprise-level, fully integrated, and customized AI environments. The AI and generative AI workloads, including using code-based configurations to enhance automation dynamic resource management to dynamically adjust to varying workloads using predictive analytics.

Recent Development

In January 2024, Oracle Corp. introduced OCI Generative AI Agents to transform data-driven decision-making. Customers may further refine these models using their data with retrieval augmented generation (RAG) techniques, so the models understand their unique internal operations.