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The quest for trusted, actionable insights is driving a rapid evolution in analytics. Between access to powerful AI models, the need for robust business intelligence (BI), and the hype around agentic AI, the market has quickly evolved from simple, rule-based automations to deeply integrated, intelligent agents that empower all stakeholders. Organizations are aggressively pursuing AI-driven analytics to gain a competitive edge, driven by a clear recognition of its transformative power. A staggering 96% of organizations recognized the critical role of AI in their business and they are prioritizing AI-driven insights as the way forward.1 The demand is matched by escalating investment: 97% of organizations have increased their financial commitment to analytics and business intelligence within the last year. And core to these commitments are a clear demand for user-friendly, AI-enhanced tools that deliver true self-service analytics. |
对可信、切实可行的见解的追求正在推动分析的快速发展。在访问强大的人工智能模型、对强大商业智能 (BI) 的需求以及围绕代理人工智能的炒作之间，市场已迅速从基于规则的简单自动化发展为深度集成的智能代理，为所有利益相关者提供支持。各组织正在积极追求人工智能驱动的分析，以获得竞争优势，这要归因于对人工智能变革力量的明确认可。惊人的 96% 的组织认识到人工智能在其业务中的关键作用，他们优先考虑人工智能驱动的洞察作为前进方向。1 需求与不断增加的投资相匹配：97% 的组织在去年增加了对分析和商业智能的财务投入。这些承诺的核心是对用户友好、人工智能增强型工具的明确需求，这些工具可提供真正的自助式分析。 |
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But while businesses are increasingly seeking AI solutions to improve data-driven decision-making, the journey to intelligent and actionable insights remains fraught with complexity. Data silos hinder analysis, creating a fragmented view of the business landscape. The sheer volume of data, coupled with its rapid change, overwhelms even the most seasoned data teams. Limited user adoption of their analytics platforms is compounded by a lack of skilled personnel to bridge the gap between technical capabilities and business understanding. And the need for scalability is also high, with massive data sets straining current architectures. These are not abstract problems; they represent real hurdles to becoming truly data-driven. The result is that 78% of organizations agreed that it takes too long to act on insights. |
但是，尽管企业越来越多地寻求人工智能解决方案来改善数据驱动的决策，但获得智能且可操作的见解之旅仍然充满复杂性。数据孤岛阻碍了分析，形成了分散的业务格局视图。庞大的数据量，加上其快速的变化，即使是最有经验的数据团队也会不知所措。由于缺乏能够弥合技术能力和业务理解之间差距的熟练人员，用户对其分析平台的采用率有限。而且对可扩展性的需求也很高，海量数据集给当前的架构带来了压力。这些不是抽象的问题；它们是真正实现数据驱动的真正障碍。结果是，78% 的组织认为根据见解采取行动需要很长时间。 |
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Organizations are seeking solutions that streamline data integration, enhancing data quality and consistency while making data more accessible to a wider range of users. They desire tools that improve data governance and security and simultaneously support advanced analytics through deeply integrated AI agents that shorten time to insight and time to take action. The need for self-service analytics is particularly important, highlighted by the fact that 75% of organizations agreed that the promise of self-service analytics has not lived up to the hype. Businesses are actively searching for solutions that will effectively empower business users through simplified interfaces and automated processes, enabling them to focus on strategic decision-making rather than grappling with complicated analytical tools. |
各组织正在寻求能够简化数据集成、提高数据质量和一致性的解决方案，同时让更多用户更容易访问数据。他们希望工具能够改善数据治理和安全性，同时通过深度集成的人工智能代理支持高级分析，从而缩短获得见解的时间和采取行动的时间。对自助分析的需求尤为重要，75％的组织认为自助分析的承诺没有兑现炒作，这一事实突显了这一点。企业正在积极寻找解决方案，通过简化的界面和自动化流程有效地增强业务用户的能力，使他们能够专注于战略决策，而不是努力使用复杂的分析工具。 |
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1. Source: Enterprise Strategy Group Research Report, Unleashing the Power of AI in Analytics and Business Intelligence, May 2024. All research presented in this brief are taken from this research report. |
1。来源：企业战略组研究报告，释放人工智能在分析和商业智能中的力量，2024年5月。本简报中介绍的所有研究均取自本研究报告。 |