delayed data

简明释义

延时信息

英英释义

例句

1.We cannot proceed with the analysis until we receive the delayed data from the last quarter.

在收到上个季度的延迟数据之前，我们无法进行分析。

2.The project timeline has been pushed back because of delayed data from the research team.

由于研究团队的延迟数据，项目时间表被推迟了。

3.Our marketing strategy needs to be adjusted due to delayed data regarding customer preferences.

由于关于客户偏好的延迟数据，我们的营销策略需要调整。

4.The financial forecast is unreliable because it relies on delayed data from previous years.

财务预测不可靠，因为它依赖于来自前几年的延迟数据。

5.The report was incomplete due to the presence of delayed data, which refers to information that was not provided on time.

由于存在延迟数据，报告不完整，这指的是未能及时提供的信息。

作文

In today's fast-paced digital world, the importance of timely information cannot be overstated. Businesses rely heavily on data to make informed decisions, and any lag in obtaining this data can lead to missed opportunities and inefficiencies. One of the most critical issues that organizations face is the problem of delayed data. This term refers to information that is not available in real-time or is provided after a significant lag, which can hinder decision-making processes and affect overall performance.The consequences of delayed data can be far-reaching. For instance, in the financial sector, investors depend on accurate and timely data to make investment choices. When stock prices fluctuate rapidly, any delay in receiving market data can result in substantial financial losses. Similarly, in healthcare, timely access to patient data is crucial for making quick and effective treatment decisions. A delay in data can lead to misdiagnoses or inappropriate treatments, ultimately jeopardizing patient safety.Moreover, delayed data can also impact customer satisfaction. In retail, for example, businesses need to monitor inventory levels in real-time to meet customer demand. If there is a delay in data reporting, a company may run out of stock on popular items, leading to frustrated customers and lost sales. This situation emphasizes the necessity for businesses to adopt systems that provide real-time data analytics to avoid such pitfalls.To combat the challenges posed by delayed data, organizations are increasingly turning to advanced technologies such as artificial intelligence (AI) and machine learning. These technologies can analyze vast amounts of data quickly and provide insights that were previously unattainable. By leveraging AI, companies can predict trends and make proactive decisions rather than reactive ones, significantly reducing the impact of delayed data.Furthermore, cloud computing has revolutionized the way data is stored and accessed. With cloud-based solutions, businesses can ensure that their data is updated in real-time, allowing for seamless collaboration among teams regardless of their geographical locations. This shift towards cloud technology not only mitigates the risks associated with delayed data but also enhances overall operational efficiency.However, while technology can significantly reduce the occurrence of delayed data, it is essential for organizations to implement robust data governance policies. Proper management of data sources and ensuring data accuracy are vital steps in minimizing delays. Training employees to understand the importance of timely data and how to utilize it effectively can also contribute to better outcomes.In conclusion, delayed data poses a significant challenge across various sectors, impacting decision-making, customer satisfaction, and overall efficiency. To address this issue, organizations must adopt innovative technologies, embrace cloud computing, and establish strong data governance practices. By doing so, they can ensure that they have access to timely information, enabling them to thrive in an increasingly competitive landscape.

在当今快速发展的数字世界中，及时信息的重要性不容小觑。企业在做出明智决策时严重依赖数据，而获取数据的任何延迟都可能导致错失机会和效率低下。组织面临的一个关键问题是延迟数据的问题。这个术语指的是未能实时提供或在显著延迟后提供的信息，这可能会妨碍决策过程并影响整体表现。延迟数据的后果可能影响深远。例如，在金融行业，投资者依赖准确和及时的数据来做出投资选择。当股价迅速波动时，任何延迟接收市场数据都可能导致巨大的财务损失。同样，在医疗保健中，及时获取患者数据对于快速有效地做出治疗决策至关重要。数据的延迟可能导致误诊或不当治疗，最终危及患者安全。此外，延迟数据还可能影响客户满意度。例如，在零售行业，企业需要实时监控库存水平以满足客户需求。如果数据报告出现延迟，公司可能会在热门商品上断货，导致顾客失望和销售损失。这种情况强调了企业采用提供实时数据分析系统的必要性，以避免此类陷阱。为了应对延迟数据带来的挑战，组织越来越多地转向先进技术，如人工智能（AI）和机器学习。这些技术可以快速分析大量数据，并提供以前无法获得的见解。通过利用人工智能，公司可以预测趋势并主动做出决策，而不是被动反应，从而显著减少延迟数据的影响。此外，云计算彻底改变了数据存储和访问的方式。借助基于云的解决方案，企业可以确保他们的数据实时更新，从而允许跨地区团队之间的无缝协作。这种向云技术的转变不仅减少了与延迟数据相关的风险，还提高了整体运营效率。然而，尽管技术可以显著减少延迟数据的发生，但组织必须实施强有力的数据治理政策。妥善管理数据源和确保数据准确性是最小化延迟的重要步骤。培训员工理解及时数据的重要性以及如何有效利用它，也可以有助于改善结果。总之，延迟数据在各个领域都构成了重大挑战，影响决策、客户满意度和整体效率。为了解决这一问题，组织必须采用创新技术，拥抱云计算，并建立强大的数据治理实践。通过这样做，他们可以确保获取及时的信息，使他们能够在日益竞争的环境中蓬勃发展。