Organisations face ongoing challenges when it comes to storing, managing, and analyzing data due to the constant influx of information overwhelming IT systems. This results in IT teams constantly playing catch-up. The average company stores about 10PB of data, with a significant portion being unclassified or redundant. This not only impacts storage costs and cyber security but also makes analyzing and deriving insights from data difficult. As businesses work with increasingly complex data relationships, a shift in traditional data management approaches is necessary.
The rise of generative artificial intelligence (GenAI) is changing how organisations approach data management. While board members are intrigued by the potential benefits of GenAI, practical implementation still presents challenges. Businesses are struggling with data architectures that fail to meet the demands of data, resulting in wasted spending. Legacy technologies, issues with data access, and management all contribute to the inefficiencies.
To address the evolving data needs, organisations are turning to NoSQL databases like graph databases. These databases are becoming essential in managing structured and unstructured data efficiently. They are widely used in various industries like social networks, financial services, and supply chain companies for tasks like fraud detection and optimizing logistics. NoSQL databases provide scalability, flexibility, and support for various workloads, making them valuable tools for handling complex data relationships.
The integration of AI technologies with NoSQL databases is revolutionizing data management practices. Graph databases are particularly suited for AI applications requiring in-depth analysis of data relationships. They support advanced algorithms crucial for tasks like recommendation systems and fraud detection. Companies like Bloomberg are leveraging these technologies to handle vast amounts of data, enabling real-time data analysis for applications like predictive maintenance and dynamic pricing.
The future of databases, especially graph databases, looks promising with anticipated growth in adoption across industries like healthcare, telecommunications, and finance. Cloud providers are expanding their database offerings to provide more robust and scalable solutions. As AI technologies continue to evolve, the integration of AI with database technologies will enable more intelligent and data-driven applications. Experimentation with new technologies is key to finding innovative solutions that optimize data management processes and drive business value.