analyzability

简明释义

英[/ˌæn.ə.lɪˈzæb.əl.ɪ.ti/]美[/ˌæn.ə.lɪˈzæb.əl.ɪ.ti/]

n. 可分析性

英英释义

单词用法

同义词

反义词

例句

1.Analyzability of the model is limited.

模型的可分析性是有限的。

2.The analyzability of English idioms has given insights into compiling English-Chinese idiom dictionaries.

习语的这种可分析性对英汉习语词典的编纂有着积极的启示。

3.The analyzability of English idioms has given insights into compiling English-Chinese idiom dictionaries.

习语的这种可分析性对英汉习语词典的编纂有着积极的启示。

4.Improving the analyzability of data can lead to better decision-making in businesses.

提高数据的可分析性可以促进企业更好的决策。

5.The software's analyzability allows developers to easily identify and fix bugs.

该软件的可分析性使开发人员能够轻松识别和修复错误。

6.The team focused on enhancing the analyzability of their machine learning models.

团队专注于增强其机器学习模型的可分析性。

7.Researchers value analyzability in their experiments to draw meaningful conclusions.

研究人员重视实验中的可分析性以得出有意义的结论。

8.High analyzability in a database can significantly reduce the time needed for data retrieval.

数据库的高可分析性可以显著减少数据检索所需的时间。

作文

In today's data-driven world, the concept of analyzability (可分析性) has become increasingly crucial. Organizations across various sectors are inundated with vast amounts of data, and the ability to extract meaningful insights from this data is paramount. The term analyzability refers to the extent to which data can be examined and interpreted to derive useful information. This characteristic is vital for effective decision-making and strategic planning.One of the primary reasons why analyzability is important is that it allows businesses to identify trends and patterns within their data. For instance, a retail company can analyze sales data to determine which products are performing well and which are not. By leveraging the analyzability of their sales figures, they can make informed decisions about inventory management, marketing strategies, and customer engagement. Without the ability to analyze their data effectively, companies risk making decisions based on guesswork rather than concrete evidence.Moreover, the analyzability of data plays a significant role in enhancing operational efficiency. For example, in the healthcare sector, medical professionals can analyze patient data to identify common health issues and develop targeted treatment plans. This not only improves patient care but also optimizes resource allocation within healthcare facilities. The ability to analyze data effectively leads to better outcomes and more efficient use of resources.Another aspect of analyzability is its impact on innovation. Organizations that prioritize data analysis are often at the forefront of industry advancements. By analyzing market trends and consumer behavior, companies can innovate their products and services to meet the evolving needs of their customers. This proactive approach to understanding data fosters a culture of continuous improvement and adaptation, essential in today’s fast-paced business environment.Furthermore, the rise of advanced analytics tools and technologies has significantly enhanced the analyzability of data. With the advent of artificial intelligence and machine learning, organizations can process and analyze vast datasets more quickly and accurately than ever before. These technologies enable businesses to uncover insights that were previously hidden, leading to more informed decision-making and competitive advantages.However, it is important to note that achieving high analyzability requires not only the right tools but also a skilled workforce. Employees must be trained in data analysis techniques and equipped with the necessary skills to interpret data effectively. Organizations should invest in training programs and encourage a data-driven culture to maximize the potential of their data assets.In conclusion, analyzability (可分析性) is an essential attribute in the modern business landscape. It empowers organizations to make data-driven decisions, improve operational efficiency, foster innovation, and gain a competitive edge. As data continues to grow in volume and complexity, the importance of analyzability will only increase. Companies that recognize and harness the power of data analysis will be better positioned to thrive in an ever-changing environment.

在当今数据驱动的世界中，analyzability（可分析性）这一概念变得愈发重要。各个领域的组织都被大量的数据所淹没，而从这些数据中提取有意义的见解的能力至关重要。analyzability一词指的是数据可以被检查和解释以得出有用信息的程度。这一特征对于有效的决策制定和战略规划至关重要。analyzability重要的主要原因之一是它允许企业识别数据中的趋势和模式。例如，一家零售公司可以分析销售数据，以确定哪些产品表现良好，哪些产品表现不佳。通过利用其销售数字的analyzability，他们可以就库存管理、营销策略和客户参与做出明智的决策。如果没有有效分析数据的能力，公司就冒着根据猜测而非具体证据做出决策的风险。此外，数据的analyzability在提高运营效率方面也发挥了重要作用。例如，在医疗行业，医疗专业人员可以分析患者数据，以识别常见健康问题并制定针对性的治疗计划。这不仅改善了患者护理，还优化了医疗设施内的资源配置。有效分析数据的能力导致更好的结果和更高效的资源利用。analyzability的另一个方面是其对创新的影响。优先考虑数据分析的组织往往处于行业进步的前沿。通过分析市场趋势和消费者行为，公司可以创新其产品和服务，以满足客户不断变化的需求。这种主动理解数据的方法促进了持续改进和适应的文化，这在当今快节奏的商业环境中至关重要。此外，先进的分析工具和技术的兴起显著提高了数据的analyzability。随着人工智能和机器学习的出现，组织能够比以往更快、更准确地处理和分析庞大的数据集。这些技术使企业能够发现以前隐藏的见解，从而实现更明智的决策和竞争优势。然而，需要注意的是，实现高水平的analyzability不仅需要合适的工具，还需要一支熟练的劳动力。员工必须接受数据分析技术的培训，并具备有效解释数据的必要技能。组织应投资于培训项目，并鼓励以数据为驱动的文化，以最大化其数据资产的潜力。总之，analyzability（可分析性）是现代商业环境中的一个重要属性。它使组织能够做出数据驱动的决策，提高运营效率，促进创新，并获得竞争优势。随着数据量和复杂性的不断增加，analyzability的重要性只会增加。认识到并利用数据分析力量的公司将更有可能在不断变化的环境中蓬勃发展。