2023 will be the year of generative artificial intelligence (AI) and foundational models. However, as organizations rush to incorporate generative AI into their workflows, they realize the importance of data organization. While companies have always understood the role of high-quality data in business success, the rise of generative AI has reinforced its value, making it a focal point for everyone. As we move towards 2024, we can expect more stories about generative AI, with leading industry experts and vendors sharing their predictions on the evolution of various aspects of the data ecosystem in the coming months. Relational databases will break free from the constraints of SQL. "Whether leveraging modern edge computing, the Internet of Things, or generative AI applications to drive business growth, enterprises have bold plans for 2024. All these plans rely on secure access to enterprise data. However, many organizations still rely on outdated operational databases that were built to meet the technological needs of decades ago. SQL, a database language that lacks standardized methods for procedural logic, is embedded in application servers connected to SQL databases and uses stateful, persistent sessions. While this design approach made sense 50 years ago, it is a painful legacy for modern, stateless cloud services. It often requires application code and databases to be located in the same data center region, which severely hampers critical serverless or geographically distributed applications such as IoT and edge computing applications... Looking ahead, we will see enterprises adopting more agile database infrastructures that support the distribution, consistency, scalability, and flexibility of modern applications in the IoT, edge computing, and AI domains. As the limitations of traditional databases become increasingly burdensome and costly for enterprise developers, it will become a bigger bottleneck for business innovation." Vector databases will become the most sought-after technology. "By 2024, vector databases will be the most sought-after technology. In an era where data-driven insights foster innovation, vector databases have gained importance rapidly due to their ability to handle high-dimensional data and facilitate complex similarity searches. Whether it's recommendation systems, image recognition, natural language processing, financial forecasting, or other AI-driven projects, understanding top-notch vector databases is crucial for cross-industry software development." Fishing for large language models (LLMs) in enterprise data lakes. "Statistics on how much information the average enterprise stores are abundant - for large enterprises, this number can reach hundreds of petabytes. However, many companies report that they use less than half of the mined information (mostly structured data) for actionable insights. By 2024, enterprises will start utilizing generative AI to leverage this untapped data by building and customizing LLMs. With the help of AI supercomputing capabilities, enterprises will begin mining their unstructured data - including chats, videos, and code - to expand their generative AI development and train multimodal models. This leap goes beyond the ability to mine tables and other structured data, enabling companies to provide more specific answers and discover new opportunities. This includes helping detect anomalies in health scans, revealing emerging trends in retail, and making business operations more secure." Companies without sufficient automation to support AI will feel the pressure. "As enterprises implement AI to maintain a competitive edge, many companies will feel the impact of their chaotic data infrastructure more deeply. When the stakes rise from merely providing erroneous information on dashboards to potentially making decisions and actions based on automated errors, the impact of bad data (or insufficient data) will intensify. Companies that lack robust data infrastructure, governance, and the use of generative AI in critical operational environments will suffer from accuracy losses sooner or later." Cloud FinOps teams will optimize data pipelines. "Faced with the reality of runaway cloud computing costs this year, true cross-organizational collaboration will become a necessity to identify unnecessary expenses, with finance and engineering teams playing a crucial role. In Ascend's annual survey, 48% of respondents plan to optimize their data pipelines to reduce cloud computing costs, with 89% expecting an increase in the number of pipelines in the next 12 months. The coming year will be critical, requiring platforms that can pinpoint where additional expenses occur in data pipelines and quickly demonstrate cost optimization to avoid misleading directives from above." Intent data will become a must-have for marketing teams. "By 2024, intent data will no longer be a 'nice-to-have' for marketing teams. As companies strive to align sales and marketing efforts, the ability to predict customer needs through behavioral data analysis from intent data will become increasingly important. As AI becomes more complex each year, we expect a shift from passive to active customer engagement, improving conversion rates and fostering long-term customer loyalty." Data teams and business teams will clash over the introduction of AI products. "While the demand for AI products like ChatGPT has surged among business users, data teams will still impose a long list of requirements before granting access to company data. This situation may serve as a forcing function to promote balance, and adoption may come earlier as AI proves its reliability and security. Additionally, companies will prioritize clean datasets to catch up with the trend of AI-driven analytics. Clean datasets will serve as the cornerstone for successful AI implementation, enabling companies to gain valuable insights and maintain competitiveness." Enterprises will face a dual impact of real-time and AI. "AI-driven real-time data analytics will bring unprecedented cost savings and competitive intelligence to enterprises, allowing software engineers to move faster within organizations. For example, insurance companies store terabytes of data in their databases. By 2024, we will be able to process these documents in real-time and extract valuable insights from these datasets without the need for custom models. So far, software engineers have had to write code to parse these documents, write more code to extract keywords or values, put them into databases, and query them to generate actionable insights. With the help of real-time AI, enterprises will achieve significant cost savings as they gain competitive value from data without having to hire many employees." Knowledge graphs will help users eliminate data silos. "As enterprises continue to move more data to the cloud, they have collected hundreds or even thousands of data silos in the cloud. Knowledge graphs will easily drive language models, allowing navigation through all data silos existing in the cloud by leveraging relationships between various data sources. In the new year, we will see a range of mature and novel knowledge graph-based AI technologies emerging to support the development of intelligent applications." AI will change current data management approaches. "Enterprises are realizing that AI contributes to their overall value proposition and competitive advantage. To achieve this, AI needs to be trained and processed on different types of data. Some data is public, but a significant amount of data is consumer personal information or organization-specific intellectual property. Companies will find themselves needing to protect the data used by AI models while leveraging it to support valuable decision-making, finding a balance. These innovative data management solutions will continue to evolve in sync with regulatory compliance and emerging regulations." The role of Chief Data Officer will be a prerequisite for becoming a CIO. "By 2024, there will be a new, definite career path for ambitious individuals aspiring to become CIOs - excelling in the role of Chief Data Officer. In recent years, Chief Data Officers have evolved from low-budget consulting roles to key assets in helping companies fully leverage their data. As more organizations invest in AI and cloud computing to democratize their data and foster innovation, Chief Data Officers are in a position of control - closer than ever to the success of CIOs and the business. Organizations looking for exceptional CIOs will choose candidates who truly understand how data moves, flows, and impacts organizations, giving Chief Data Officers a natural advantage on that career path and continuing to have a significant influence within enterprises." Note: The HTML tags have been retained as requested, with the removal of style and class attributes.