Find stats on top websites
Industry Landscape
The data management and analytics industry is experiencing robust growth, driven by digital transformation, increasing data volumes, and the need for data-driven decision-making. Cloud adoption, AI/ML integration, and specialized solutions like PIM/MDM are key trends.
Total Assets Under Management (AUM)
Data Management Software Market Size in United States
~Expected to reach over 100 billion USD in 2024
(15-20% CAGR)
- Cloud data platforms driving adoption.
- Increased investment in data governance.
- Demand for real-time analytics solutions.
Total Addressable Market
Over 100 billion
Market Growth Stage
Pace of Market Growth
Emerging Technologies
Generative AI for Data Augmentation
Generative AI can create synthetic, realistic data to augment existing datasets, improving model training, testing, and filling data gaps, especially in privacy-sensitive scenarios.
Data Mesh Architectures
This decentralized architectural paradigm treats data as a product, owned by domain teams, fostering data democratization, scalability, and agility across large organizations.
Automated Data Governance & Observability
Leveraging AI and machine learning to automate data quality checks, policy enforcement, and real-time monitoring of data pipelines ensures data reliability and compliance with minimal manual intervention.
Impactful Policy Frameworks
California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) (2020/2023)
The CCPA, updated by the CPRA, grants California consumers extensive rights over their personal information, including the right to know, delete, and opt-out of sales or sharing, and establishes the California Privacy Protection Agency (CPPA) for enforcement.
Businesses must implement robust data governance and PIM/MDM solutions to accurately identify, track, and manage personal data to comply with consumer requests and avoid significant fines.
SEC Cyber-Disclosure Rules (2023)
The U.S. Securities and Exchange Commission (SEC) mandates that public companies disclose material cybersecurity incidents within four business days and provide annual disclosures regarding their cybersecurity risk management, strategy, and governance.
Companies must enhance their data observability and incident response capabilities to rapidly detect and report cybersecurity breaches, directly impacting their data management practices and board-level oversight.
NIST AI Risk Management Framework (AI RMF 1.0) (2023)
Developed by the National Institute of Standards and Technology, the AI RMF provides voluntary guidance for organizations to manage risks associated with artificial intelligence systems, focusing on trustworthy and responsible AI development and deployment.
Credencys and its clients leveraging AI/ML will need to integrate the framework's principles into their AI engineering and MLOps practices, ensuring fairness, transparency, and accountability in their data-driven AI solutions.
Transform Your Ideas into Action in Minutes with WaxWing
Sign up now and unleash the power of AI for your business growth
