What is data management?
Data management is collecting, processing, and using data securely and efficiently for better business outcomes. It involves a comprehensive strategy that addresses how to collect, integrate, and store data from diverse sources; maintain high availability and disaster recovery; build fit-for-purpose databases; ensure data sharing and collaboration; secure and govern data; manage the data lifecycle; and automate data discovery and analysis with generative AI.
A modern data management system integrates with existing technology to provide high-quality, usable data for data scientists, AI and machine learning engineers, and business users.
Why is data management relevant in the era of AI?
Generative AI and other AI technologies require vast amounts of high-quality, properly organized data for training models and deriving insights. However, many organizations still face fundamental data challenges, such as data silos, complexity, and inconsistency, which hinder their ability to use data effectively for AI.
A strong data management strategy, equipped with modern data architecture, governance, and security tools, enables organizations to transform their data into a trusted asset, fueling innovation, enhancing customer experiences, and driving data-driven business models powered by AI.
Benefits of data management
- Reduced data silos: Modern data management tools and frameworks help eliminate data silos and dependencies, facilitating data integration and collaboration across functions
- Improved compliance and security: Data governance councils establish guardrails to protect businesses from fines and negative publicity due to noncompliance with regulations and policies
- Enhanced customer experience: Successful data management enables teams to better understand and personalize the customer journey through holistic data analyses
- Scalability: Cloud platforms and modern data management practices enable businesses to scale their data infrastructure flexibly, adapting to changing needs and workloads
