delayed data

简明释义

非实时数据

英英释义

例句

1.The team is working overtime to catch up on the delayed data from last month.

团队正在加班以赶上上个月的延迟数据。

2.Analysts are concerned about the impact of delayed data on market predictions.

分析师对延迟数据对市场预测的影响表示担忧。

3.The project timeline has been pushed back because of delayed data from our suppliers.

由于供应商提供的延迟数据，项目时间表被推迟了。

4.The report was submitted late due to delayed data from the survey.

由于调查的延迟数据，报告提交得很晚。

5.We cannot finalize the budget until we receive the delayed data from the finance department.

在收到财务部门的延迟数据之前，我们无法确定预算。

作文

In today's fast-paced world, the need for real-time information is more critical than ever. However, there are instances when data is not processed or delivered as quickly as needed, leading to what is commonly referred to as delayed data. 延迟数据 can have significant implications across various sectors, including finance, healthcare, and technology. Understanding the causes and effects of delayed data is essential for organizations aiming to optimize their operations and improve decision-making processes.One of the primary reasons for delayed data is the sheer volume of information that organizations must handle daily. In the age of big data, companies collect vast amounts of data from numerous sources. Processing this data in a timely manner can be challenging, especially when systems are not equipped to handle such large volumes efficiently. For instance, in the financial sector, stock prices and market trends change rapidly. If a trading firm relies on delayed data, they may miss crucial investment opportunities or make poor trading decisions based on outdated information. This highlights the importance of having robust data processing systems that can minimize delayed data and ensure timely access to relevant information.Another factor contributing to delayed data is technological limitations. Many organizations use legacy systems that are not designed to process data in real time. These outdated systems can create bottlenecks, resulting in significant lags in data availability. For example, in healthcare, timely access to patient information can be a matter of life and death. If medical professionals have to wait for delayed data regarding a patient's medical history or test results, it could lead to misdiagnosis or improper treatment. Therefore, investing in modern technology and infrastructure is crucial for reducing delayed data and improving overall efficiency.Moreover, human error can also play a role in data delays. When data is manually entered or processed, the likelihood of mistakes increases, which can result in further delays. Organizations must implement rigorous training programs and establish clear protocols to minimize human error in data handling. Additionally, automating data collection and processing can significantly reduce the chances of delayed data caused by human factors. Automation not only speeds up the process but also enhances accuracy, leading to more reliable data outcomes.The implications of delayed data extend beyond operational inefficiencies; they can also affect customer satisfaction and trust. In industries like e-commerce, customers expect real-time updates on their orders. If a company experiences delayed data in tracking shipments or processing returns, it can lead to frustration and loss of business. To maintain a competitive edge, companies must prioritize timely data delivery to enhance customer experience.In conclusion, delayed data poses a significant challenge for organizations across various sectors. By understanding the causes of delayed data and implementing effective strategies to mitigate its impact, businesses can improve their operational efficiencies, enhance decision-making capabilities, and ultimately provide better services to their customers. The importance of addressing delayed data cannot be overstated, as it plays a crucial role in the success and sustainability of modern organizations.

在当今快节奏的世界中，实时信息的需求比以往任何时候都更为重要。然而，有时数据并未按需处理或交付，导致通常所称的延迟数据。Delayed data在金融、医疗和技术等各个行业都可能产生重大影响。理解延迟数据的原因和影响对于希望优化运营和改善决策过程的组织来说至关重要。造成延迟数据的主要原因之一是组织每天必须处理的信息量巨大。在大数据时代，公司从众多来源收集大量数据。及时处理这些数据可能具有挑战性，尤其是当系统未能有效处理如此大量的信息时。例如，在金融部门，股票价格和市场趋势变化迅速。如果一家交易公司依赖于延迟数据，他们可能会错过关键的投资机会，或者基于过时的信息做出错误的交易决策。这突显了拥有强大数据处理系统的重要性，这些系统可以最小化延迟数据，确保及时获取相关信息。另一个导致延迟数据的因素是技术限制。许多组织使用的遗留系统并不设计用于实时处理数据。这些过时的系统可能会造成瓶颈，从而导致数据可用性的显著滞后。例如，在医疗保健中，及时访问病人信息可能关系到生死。如果医疗专业人员不得不等待延迟数据以获取患者的病史或检测结果，可能会导致误诊或不当治疗。因此，投资现代技术和基础设施对于减少延迟数据和提高整体效率至关重要。此外，人为错误也会对数据延迟产生影响。当数据由人工输入或处理时，错误发生的可能性增加，这可能导致进一步的延迟。组织必须实施严格的培训计划，并建立明确的协议，以最小化数据处理中的人为错误。此外，自动化数据收集和处理可以显著减少因人为因素导致的延迟数据的机会。自动化不仅加快了流程，还提高了准确性，从而导致更可靠的数据结果。延迟数据的影响不仅限于操作效率低下；它们还可能影响客户满意度和信任。在电子商务等行业，客户期望实时更新他们的订单。如果一家公司在跟踪货物或处理退货方面经历了延迟数据，这可能会导致挫败感和业务损失。为了保持竞争优势，公司必须优先考虑及时的数据交付，以提升客户体验。总之，延迟数据对各个行业的组织构成了重大挑战。通过理解延迟数据的原因并实施有效策略以减轻其影响，企业可以提高运营效率，增强决策能力，并最终为客户提供更好的服务。解决延迟数据的重要性不容小觑，因为它在现代组织的成功和可持续性中发挥着至关重要的作用。