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Data Management: Major Inhibitor to Deploying AI
Artificial intelligence (AI) is transforming the world in unprecedented ways. From healthcare to finance, from education to entertainment, AI is enabling new possibilities and creating new value propositions. However, AI is not a magic bullet that can solve all problems. It requires a solid foundation of data to learn, improve, and deliver insights. Data management is the key to unlocking the full potential of AI, but it is also the top challenge that many organizations face in their AI journeys.
Data management is the most frequently cited technological inhibitor to AI deployments, followed by security and compute performance. Data management challenges include preparing and cleaning data, integrating data from diverse sources, training AI models, and ensuring data governance.
These challenges can hamper the performance and value of AI initiatives, as well as expose organizations to ethical and regulatory risks. To overcome these barriers, organizations need to adopt data management technologies that are infused with AI. These technologies can help streamline data practices and enhance AI capabilities by simplifying data ingestion and preparation, accelerating data analysis and discovery, enforcing data quality and security, and automating data workflows.
By using AI-powered data management tools, organizations can leverage their data as a strategic asset and drive innovation with AI. They can gain competitive advantages by generating deeper insights, improving decision-making, increasing efficiency, and reducing costs. They can also ensure compliance with data policies and regulations, and foster trust and transparency with their stakeholders.
AI and data management are two sides of the same coin. They complement each other and create a virtuous cycle of improvement and innovation. As more organizations adopt AI and data management solutions, they will be able to scale their AI projects and create new value propositions for their businesses and industries.
