US & Canada, Aug. 07, 2024 (GLOBE NEWSWIRE) --
AI Data Management Market Overview:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
Artificial Intelligence (AI) Data Management is the planned and systematic management of an organization's data assets with the goal of improving data quality, analysis, and decision-making with AI technology. It encompasses all of the procedures, rules, and technical approaches used to effectively collect, organize, store, and use data. Corporate data management necessitates trustworthiness, accessibility, security, and compliance. Data is collected, stored, retrieved, and changed to ensure its accuracy, consistency, and currency. This technique is critical for regulatory compliance, sound decision-making, and a competitive advantage.
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Key Aspects of AI for Data Management:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
Key Market Findings:
- North America dominated the AI Data Management Market in 2023 and is expected to grow significantly during the forecast period. This growth may be attributed to the increasing focus on streamlining business operations among enterprises.
- The AI data management market in the Asia Pacific region is expected to grow at the fastest CAGR during the projected period. Countries in this region are focusing on digitization across numerous industry verticals, resulting in a generation of large data volumes. Thus, the demand for AI data management solutions is expected to grow in the Asia Pacific region during the forecast period.
- Based on vertical, the BFSI segment is expected to hold significant market share throughout the forecast period. This growth may be attributed to improved fraud detection, loan & credit decisioning, automated investment processes, and enhanced customer experience, among others.
- The platform segment held the largest revenue share in 2023. AI data management platforms enable businesses to gain useful insights from their data using advanced analytical techniques, resulting in better strategic decision-making. In the current business environment, organizations are becoming more reliant on data-driven decision-making. Thus, the demand for AI data management platforms is expected to grow during the forecast period.
Key Market Dynamics:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
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Adoption of AI-Powered Data Fabrics:
Data fabrics are gaining popularity as firms seek to improve data management and make better use of their data. Data fabric solutions use artificial intelligence (AI) to automate ingestion procedures by extracting and analyzing metadata from new sources. Machine learning algorithms recognize metadata patterns to construct knowledge graphs that organize linked datasets for different use cases, considerably easing data discovery. Data Fabric is an eco-system that uses smart and automated solutions to help integrate all data pipelines and the cloud from beginning to end. Thus, the adoption of data fabrics for AI data management is expected to grow during the forecast period.
Predictive Analytics-based Decision-Making:
Predictive analytics is an important tool for business decision-making, but by integrating AI models, it can provide even more accurate and actionable insights. AI models, which use machine learning and artificial intelligence approaches, enable to analyze massive amounts of data and generate predictions accurately. By incorporating AI into predictive analytics, data analysis, algorithm creation, and predictive modeling capabilities can be further improved. Thus, the integration of predictive analytics for improved decision-making in AI data management is expected to grow during the forecast period.
Competitive Landscape / Government Regulation and Authorities:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
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Recent Market Developments:
- In June 2024, Telefónica Tech and IBM announced a new collaboration agreement to drive the deployment of Artificial Intelligence (AI), analytics and data governance solutions and respond to the constant and dynamically evolving needs of enterprises. The agreement initially limited to Spain, establishes a framework of collaboration between the two companies to help their customers deal with the complexity of managing new technologies in a heterogeneous and changing environment and to extract the full value of these technologies in their business processes.
- In May 2024, Amazon Web Services (AWS), an Amazon.com company, and SAP SE announced a strategic collaboration to transform modern cloud enterprise resource planning (ERP) experiences and help enterprises drive new capabilities and efficiencies with generative artificial intelligence (AI).
Key Companies Offerings:
| Sr. No | Company | Product/Solutions/Services |
| 1 | Microsoft | Microsoft Intelligent Data Platform |
| 2 | IBM | Data management solutions |
| 3 | AWs | Data Management, Processing & Analytics |
| 4 | Oracle | Data Management |
| 5 | AI & Machine Learning Products & Services, Data Management |
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources, Company Websites, Annual Reports
Regulatory Framework:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
The EU AI Act requires that general-purpose AI models, including generative AI systems such as large language (LLMs) and foundation models, adhere to a classification system based on systematic risk tiers. Higher risk tiers have more transparency requirements, including model evaluation, documentation and reporting. The EU AI Act follows a risk-based approach, using tiers to classify the level of risk that AI systems pose to an individual's health, safety or fundamental rights. The three tiers are:
- Low-risk systems such as spam filters or video games have few requirements under the law other than transparency obligations.
- High-risk AI systems such as autonomous vehicles, medical devices and critical infrastructure (water, gas, electric, etc.) require developers and users to adhere to additional regulatory requirements:
- Implement risk management, provide accuracy, robustness and a framework for accountability that includes human oversight
- Meet transparency requirements provisioned for users, record keeping, and technical documentation
- Prohibited systems, with little exception, are systems posing unacceptable risks, such as social scoring, facial recognition, emotion recognition and remote biometric identification systems in public spaces.
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The US government's Office of Management and Budget (OMB), on March 28, 2024, introduced a new policy establishing requirements from federal agencies for the responsible deployment of artificial intelligence (AI) and minimizing risks that impact the rights and safety of the public when AI is used for governance. In order to ensure accountability towards the duties outlined in the memorandum for AI governance, the policy requires the agencies to:
- Submit a plan to OMB within 180 days to meet memorandum goals or state that the agency does not use or does not anticipate using the AI covered under the policy and publicly post this information on its website.
- Annually provide an AI use case inventory to OMB (excluding Defense and Intelligence), highlighting safety and rights impacts, along with risk mitigation plans, and post this publicly.
- Annually report aggregate metrics on AI use cases, their impact on rights and safety, and compliance with policy guidelines.
Technology Landscape:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
- Data Extraction, being the first stage in data management, faces numerous issues. However, enterprises are developing solutions to crawl, sync, and integrate apps and platforms, combining data in many formats (including real-time). This automation has sped up the decision-making process and reduced the time to action. AI and machine learning, as well as business intelligence (BI) technologies, are essential enablers of this development.
- Data catalogs Powered by machine learning technology improve the search function by categorizing and scanning data, eventually enabling Dynamic Search. Organizations can use this technology to improve data accessibility and discoverability, resulting in more effective data management.
- Augmented analytics use technology such as AI, machine learning, and natural language processing to aid in data processing and insight production, augmenting how people explore and analyze data while dramatically decreasing data administration responsibilities.
- NLP helps computers interpret human language, allowing non-technical business users to perform queries and receive clear analytical findings.
- The rise in data management trends has resulted in more regulation. With the GDPR (General Data Protection Regulation, 2018) and other privacy legislation, data governance will become a more regulated part of data management.
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How AI is Improving Data Management?
Artificial intelligence is enhancing data management, including quality, access, and security. Data management is critical for creating an environment in which data may be utilized throughout the firm. Effective data management reduces the difficulties caused by inaccurate data, such as increased friction, inaccurate predictions, and even basic inaccessibility, preferably before they arise. Thus, in five typical data management areas, AI plays a major role:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
What are the Applications and Benefits of AI Data Management for Businesses?
In the age of big data, organizations are flooded with massive volumes of data. The challenge is not only gathering this data but also efficiently managing, analyzing, and extracting meaningful insights from it. Artificial intelligence (AI) is developing as a valuable ally in this endeavor, providing sophisticated tools for navigating and leveraging data. AI is changing the way businesses handle data by automating data processing, enabling predictive analytics, and improving decision-making.
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Key benefits of AI data management for businesses:
Source: The Insight Partners Analysis, Primary Interviews, Magazines and Journals, and Secondary Sources
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