AI和机器学习如何分析非结构化数据？2026年最新技术应用解析

Introduction: The Untapped Reservoir of Information

Would you choose where to go on vacation if you could only access 10 to 20 percent of the reviews and information on a travel website? If you do, you will probably have an unforgettable trip, but for reasons you might not like. Yet government organizations and businesses – from manufacturing to insurance companies, and healthcare to banking – are making decisions along this very same line. And they’ve been doing so for years. They look at the easy information they can get from structured data while ignoring their unstructured data, which Deloitte believes may account for 80 to 90 percent of content generated globally, making unstructured data a tremendous source of untapped value.

如果你只能访问旅游网站上10%到20%的评论和信息，你会据此选择度假目的地吗？如果会，你很可能会经历一次难忘的旅行，但原因可能并不令人愉快。然而，从制造业到保险公司，从医疗保健到银行业，政府机构和企业多年来一直在以同样的方式做出决策。他们只关注从结构化数据中轻易获取的信息，却忽略了非结构化数据。德勤认为，非结构化数据可能占全球生成内容的80%到90%，这使其成为一个巨大的、尚未开发的价值来源。

Fortunately, advancements in AI (Artificial Intelligence) and machine learning now make it possible and affordable to sift through and find meaning in vast amounts of unstructured data obtained from video and audio files, emails, logs, social media posts and even notifications from Internet of Things (IoT) devices. All of this data can bring about enormous benefits, such as when used to automate tasks that are manually intensive and often highly repetitive. One task, for example, is to watch out for red flags: specific criteria or behaviors that may indicate something is amiss and corrective action must be quickly taken. Let’s look at a few cases from different industries.

幸运的是，人工智能（AI）和机器学习的进步，使得从视频、音频文件、电子邮件、日志、社交媒体帖子甚至物联网（IoT）设备通知中获取的大量非结构化数据中筛选并发现意义成为可能且成本可控。所有这些数据都能带来巨大的好处，例如用于自动化那些劳动密集型且高度重复的任务。其中一个任务就是识别“危险信号”：那些可能表明出现问题并需要迅速采取纠正措施的具体标准或行为。让我们看看来自不同行业的几个案例。

How about an insurance claim that appears fine on the surface, but deserves to be investigated or, a job applicant who may be hiding information? What about a shipment of highly perishable pharmaceutical products that may not have been refrigerated for a portion of their journey, or a contract that may be in violation of a country’s laws or breaks an existing agreement with another company? The important thing is a red flag indicates issues that if left unchecked could cause great damage.

例如，一份表面看起来正常但值得深入调查的保险索赔，或者一个可能隐瞒信息的求职者？再比如，一批高度易腐的药品在运输途中可能有一段未冷藏，或者一份合同可能违反了某国法律或破坏了与另一家公司的现有协议？关键在于，危险信号所指示的问题如果得不到处理，可能会造成巨大损失。

Key Concepts: The Engine of AI and the Nature of Data

Artificial Intelligence is Massively Data Hungry

How does AI and machine learning enable more efficient and effective data analysis? Through feeding it data. By giving a machine learning model examples of good and bad transactions, it teaches itself to distinguish between the two types. And the more data the machine learning model processes, the greater it reinforces those lessons, enhancing accuracy.

人工智能和机器学习如何实现更高效、更有效的数据分析？答案是：通过向其提供数据。通过给机器学习模型提供正面和负面交易的示例，模型可以自学区分这两种类型。机器学习模型处理的数据越多，这些经验教训就得到越多的强化，从而提高了准确性。

So while AI and machine learning are making great strides, businesses and other organizations need to catch up. Think of it this way: data is like fuel. We need it to power our thinking in order to make wise decisions. But we’ve mined all the easy stuff, the structured data that arrives in nice and neat packages. But here’s where the fuel analogy breaks down: while another gallon of gas lets us drive another 20 to 30-odd miles, the more data we put in enables us to make significantly better and more accurate decisions – not just another 20 to 30-odd miles worth – and to make them even faster.

因此，尽管人工智能和机器学习正在取得巨大进步，但企业和其他组织需要迎头赶上。可以这样想：数据就像燃料。我们需要它来驱动我们的思维，以做出明智的决策。但我们已经开采了所有容易获取的资源，即那些整齐打包的结构化数据。然而，燃料的类比在这里失效了：虽然多加一加仑汽油只能让我们多行驶20到30多英里，但我们输入的数据越多，我们就能做出显著更好、更准确的决策——不仅仅是多走20-30英里的价值——而且决策速度更快。

Yet for so long an enormous portion of our data, our unstructured data, has remained unexploited because it had been too expensive and too difficult to access and process. And while that’s no longer the case as new technology to gather and analyze unstructured data becomes available, many people in business and other organization have overlooked these advances.

然而，长期以来，我们数据中的绝大部分——非结构化数据——一直未被开发利用，因为访问和处理它们的成本太高、难度太大。尽管随着收集和分析非结构化数据的新技术出现，情况已不再如此，但许多企业和其他组织中的人员仍然忽视了这些进步。

Main Analysis: Strategic Advantages and Industry Applications

Where the Smart Money Is

International Data Corporation (IDC) predicts that by 2020 organizations that analyze both structured and unstructured data, that is all relevant data, and deliver actionable information will achieve an extra $430 billion in productivity gains over their competitors that do not perform such data analysis. And businesses that understand this are not waiting until 2020. An executive at a multinational insurance company based in Germany refers to unstructured data as their greatest risk. They understand the numbers involved, and are working to ensure they’re not caught off-guard by writing insurance policies that expose them to liabilities they could have avoided.

国际数据公司（IDC）预测，到2020年，那些分析结构化和非结构化数据（即所有相关数据）并提供可操作信息的组织，将比不进行此类数据分析的竞争对手额外获得4300亿美元的生产力收益。明白这一点的企业并没有等到2020年。一家总部位于德国的跨国保险公司的高管将非结构化数据称为他们最大的风险。他们了解其中涉及的数字，并正在努力确保自己不会因承保那些可能暴露本可避免的责任的保单而措手不及。

The combined power of big data, AI and machine learning can make it easier to process information related to even more complex challenges. For example, banks and other organizations can more accurately and more rapidly detect fraud, tax evasion, money laundering and other schemes by mining what had previously been unprocessed, unstructured data. This enables them to catch and shut down cases of fraud and abuse, as well as avoid the many false positives that can occur when relying only on structured data. Trade finance agreements, including contracts and multiple data sources, between countries or companies can also be scoured to determine if fraud or inequities exists, whether they’re intentional or not.

大数据、人工智能和机器学习的结合力量，使得处理与更复杂挑战相关的信息变得更加容易。例如，银行和其他组织可以通过挖掘以前未经处理的非结构化数据，更准确、更迅速地检测欺诈、逃税、洗钱和其他阴谋。这使他们能够发现并制止欺诈和滥用案件，同时避免仅依赖结构化数据时可能出现的许多误报。国家或公司之间的贸易融资协议，包括合同和多种数据源，也可以被仔细审查，以确定是否存在欺诈或不公平现象，无论其是否出于故意。

Furthermore, AI and machine learning can help banks and other kinds of businesses better identify and verify the identity of their clients through automated Know Your Customer (KYC) procedures. Such procedures can help prevent them from being used, deliberately or inadvertently, for money laundering activities as well as help avert bribery and other forms of corruption from occurring. KYC procedures can also enable businesses to better understand their customers’ financial dealings and needs, as well as help them more prudently manage risk. Other advantages include speeding up time to revenue when onboarding new customers, making KYC not another cost to incur but, instead, a source of profit.

此外，人工智能和机器学习可以通过自动化的“了解你的客户”（KYC）流程，帮助银行和其他类型的企业更好地识别和验证客户身份。此类流程有助于防止其被故意或无意地用于洗钱活动，并有助于避免贿赂和其他形式的腐败发生。KYC流程还能使企业更好地了解客户的财务交易和需求，并帮助它们更审慎地管理风险。其他优势包括在接纳新客户时加快实现收入的时间，使KYC不再是另一项成本，而是利润的来源。

AI and Machine Learning Can Increase Your Competitiveness

With all of the benefits gained through AI and machine learning – and the advances in technology used to process structured and unstructured data – it’s time for more businesses and organizations to take advantage of the greatest source of information available: their own unstructured data.

鉴于通过人工智能和机器学习获得的所有好处，以及用于处理结构化和非结构化数据的技术进步，现在是更多企业和组织利用最丰富信息来源的时候了：他们自己的非结构化数据。

Comparative Analysis: Structured vs. Unstructured Data in the AI Era

To clearly illustrate the paradigm shift and strategic imperative, the following table contrasts the traditional and modern approaches to data utilization, highlighting the role of AI/ML.

The transition from a limited, structured-data-centric view to a comprehensive, AI-powered data strategy is no longer a luxury but a necessity for maintaining competitiveness and managing risk in the digital age. The organizations that successfully bridge this gap will be the ones to unlock unprecedented value and build more resilient, intelligent operations.

从有限的、以结构化数据为中心的视角，转向全面的、人工智能驱动的数据战略，在数字时代已不再是奢侈品，而是保持竞争力和管理风险的必要条件。成功弥合这一差距的组织，将能够释放前所未有的价值，并建立更具韧性、更智能的运营体系。

常见问题（FAQ）

AI如何帮助企业分析非结构化数据？

AI和机器学习技术能高效处理占全球内容80-90%的非结构化数据，从视频、邮件等来源提取有价值信息，用于欺诈检测和风险管理。

为什么说AI是数据饥渴型的？

机器学习模型通过大量数据训练自我优化，处理的数据越多，识别准确率越高，这使其在分析海量非结构化数据时具有显著优势。

哪些行业最适合应用AI数据分析？

保险、银行、医疗等行业能通过AI分析非结构化数据获得竞争优势，实现自动化风险识别和决策支持，提升运营效率。