数字化转型在互联网上不是一个新词汇，但数字化转型已经成为不可逆转的浪潮和趋势，本文从数字化转型的三个阶段，以及针对不同阶段的企业和个人来说，都有哪些机会点做了解释说明。
互联网上岸，做了一年多的数字化转型战略落地的项目，项目间隙，再次复盘总结一下对数字化转型的认识。
一、数字化转型是不可逆转的浪潮和趋势
虽然数字化转型不是一个新词汇，但是数据资产成为国家级的生产要素，也就是近几年的事情。从 21 年数据安全相关法律的颁布，到数字中国建设规划，到国家数据局的成立，从政策、法规层面，官方盖章了数据要素的价值，而从企业自身而言，除了响应政策外，越来越多的企业在宏观经济不景气，增长乏力的情况下，开始自驱地进行降本增效，目光也从资本驱动、经验驱动、转向数据驱动。
二、数字化转型的三个阶段
由于所属行业不同、管理者的管理思维方式不同，目前企业所属的阶段也参差不齐。数字化转型的程度可以分为三个层次。
1. 数字化阶段
有人也把他称之为信息化（IT 化阶段），互联网行业可以说是从一出生就天然带着数字化的基因，从搜狐、网易早期的门户替代纸媒，到各种 B2B、B2C、O2O、B2S、OTA 等电商挤压实体经济。
现在数字化产品已经渗透到我们的日常生活的点点滴滴，吃住行，游购娱。互联网的红利就是基于数字化的产品，来降低传统模式的价值链的每一环的成本。数字化是基础，没有二级制数字的采集，其他的数据挖掘、人工智能都是无源之水。 数字化转型网www.szhzxw.cn
现阶段来说，传统行业的数字化的程度相对低一些，比如制造业等。
现在很多车企的数字化转型，第一步就是将产品生命周期的全流程的数字化，从过去的线下管理，通过线上的产品（APP、小程序等）, 来实现流程的快速流转，并且整个过程可见、可追溯。
比如，要生产一辆汽车，车间进行车辆组装，需要库管查询有没有库存，经过各个层级的申请。有了数字化的产品后，直接手机或移动设备上，基于客户诉求，按需申请，并可实时查看审批进度。大大节省库存成本、等待时间成本等。
再举个栗子，有一个机械制造厂，主要是用车床机器生产一些汽车所需要的零部件，但是机器嘛，运行了几年总会出现各自各样的故障。一般来说，出了故障，会导致次品率提升，但如果设备可以正常运转，就需要到质检环节才能发现，此时打电话给机器厂家售后，厂家安排维修工单，最好的情况是师傅到了之后，当天可以修好，但从故障到恢复至少需要 2 天，比较糟糕的情况是师傅到了现场一通监测，发现零部件坏了，还需要再调货，一周过去了。
对于制造业，机器停产一方面会带来机器闲置的成本，如果影响交付周期可能会导致订单积压和违约风险。数字化转型可以做什么呢？
首先，通过传感器采集机器运行的各种参数，然后，对机器运转的健康度进行监控，一旦参数异常即触发预警，可以第一时间发现问题，停止生产，避免原材料浪费，而且，运行的参数也可以传递给厂家售后，厂家远程即可指导修复或者找到解决方案，这样就大大缩短了机器闲置的窗口期。详见往期文章：数字化转型的本质是什么？ 数字化转型网www.szhzxw.cn
2. 数据化阶段
有了数据这一基本的生产要素后，才有进一步的数据分析、数据挖掘的可能性。这个阶段是从经验驱动到数据驱动的转型阶段，一切用数据说话，数据化运营、数据化管理。一个被说烂了的典型词汇就是智慧 XX，智慧城市，智慧文旅，智慧校园。一个典型的应用就是做大屏。数据准不准不重要，重要的是大屏要炫酷。此阶段催生和养活了一大批大屏开发的供应商。
除了大屏的华而不实外，可以落到实处的各种数据产品在企业中持续打磨和完善。从最初的数据经营分析平台到数据仓库，用户画像、数据中台，数据湖。数据在分析决策，与产品智能、精细化运营过程中的应用越来越多。
用好了数据，可以真正帮企业理想、精准决策，降本增效。互联网行业现在的数据化程度已经非常高了，遥遥领先。而经过几年的数字化建设后，银行、地产、药企，传统零售商，已经开始在数字化的基础上，进行数据化的应用落地。
3. 数智化阶段
2023 年随着 GenAI 的爆火，越来越多的智能化应用开始涌入大众视野。人工智能是 90 年代就有的词，在过去主要是在机器学习、算法模型在个性化推荐、千人前面、模式识别（图像识别、语音识别）等场景的应用落地。大模型逐步成熟后，应用场景得到了极大的丰富，从数字人智能客服、自动生成图文、视频内容等。 数字化转型网www.szhzxw.cn
三、数字化转型的 5 个机会点
数字化转型的步伐还在继续，对于企业和个人来说，都有哪些机会点呢？针对三个不同的阶段：
1. 面向企业的信息化产品或服务，即企业服务相关的机会
主要机会点在数字化基础较差的一些行业，比如工业、制造业等。可以提供的服务包括：企业信息化服务（OA 办公、在线协同软件、IM、产品生命周期管理、项目管理工具等），这也是多数 B 端产品的主要工作内容。 数字化转型网www.szhzxw.cn
2. 数据化阶段的数据应用类的产品或解决方案
有了数字化的数据基础后，数据究竟该怎么用，在转型初期的企业会比较迷茫。需要一些最佳实践或者解决方案的输入。一般会从最基础的数据可视化开始。不管怎么说，先搞几个大屏再说（数据可能都是假的）。在这个方面，一些企事业单位仍然有较多智慧 XX 的需求。
3. 数据化应用工具
数据分析和使用的效率往往受到专业的数据开发、产品的资源的制约，工欲善其事必先利其器，好的数据化工具可以大大提升数据赋能的效率，现在仍在持续活跃和增长的包括自助 BI 产品、CDP 系统等。
4. 大数据基础设施
经济基础决定上层建筑，数据应用和价值的需要坚实的基础，比如集群、服务器资源，现在云计算为主。比如一站式大数据开发和运维系统、数据资产与治理、数据质量等相关的人才、产品、服务等
5. 智能化应用
除了既往的 AI 算法应用场景外，GenAI 催生了更多的可能性，比如保险行业，很多保险顾问日常的工作就是解答客户的各种问题咨询，其实这些条款都有，但是几十页的文档着实没有人愿意去看，相比较过去的文本匹配、Q＆A 的固定问答，大模型提供了更近智能化的多轮场景对话。可以通过数字人或其他交互方式，实现相关问题咨询的快捷准确回复。 数字化转型网www.szhzxw.cn
翻译：
Three stages of digital transformation and five opportunities
Digital transformation is not a new word on the Internet, but digital transformation has become an irreversible wave and trend, this paper from the three stages of digital transformation, as well as for enterprises and individuals at different stages, what are the opportunity points to explain.
Internet ashore, do more than a year of digital transformation strategy landing project, project gap, once again to summarize the understanding of digital transformation.
Digital transformation is an irreversible wave and trend
While digital transformation is not a new term, it is only in the last few years that data assets have become a factor of production at the national level. From the promulgation of laws related to data security in 21 years, to the construction planning of Digital China, to the establishment of the National Data Bureau, from the level of policies and regulations, the value of data elements has been officially stamped. From the perspective of enterprises themselves, in addition to responding to policies, more and more enterprises have begun to reduce costs and increase efficiency in a self-driven way under the circumstances of macroeconomic depression and weak growth. The focus is also shifting from capital-driven, experience-driven, to data-driven.
Three stages of digital transformation
Due to different industries and different ways of management thinking of managers, the stage of the current enterprise is also uneven. The degree of digital transformation can be divided into three levels.
Digital phase
Some people also call him information (IT stage), the Internet industry can be said to be born naturally with digital genes, from Sohu, NetEase early portal to replace paper media, to a variety of B2B, B2C, O2O, B2S, OTA and other e-commerce squeeze the real economy. 数字化转型网www.szhzxw.cn
Now digital products have penetrated into our daily life bit by bit, eating, housing, travel, shopping and entertainment. The dividend of the Internet is based on digital products to reduce the cost of each link in the traditional value chain. Digitalization is the foundation, there is no two-level digital collection, other data mining, artificial intelligence are water without a source.
At this stage, the degree of digitalization of traditional industries is relatively low, such as manufacturing.
At present, the first step in the digital transformation of many automobile companies is to digitize the whole process of the product life cycle, from the past offline management to online products (apps, small programs, etc.) to achieve rapid flow of the process, and the entire process is visible and traceable.
For example, to produce a car, the workshop for vehicle assembly, the warehouse manager needs to check whether there is inventory, through various levels of application. After the digital product, directly on the mobile phone or mobile device, based on customer demands, on-demand application, and real-time review of the approval progress. Greatly save inventory costs, waiting time costs, etc.
For another example, there is a machine factory, mainly using lathe machines to produce some parts needed for automobiles, but the machine, running for several years, there will be various failures. Generally speaking, a failure will lead to an increase in the rate of defective products, but if the equipment can operate normally, it needs to be found in the quality inspection link, at this time, call the machine manufacturer after sale, the manufacturer arranges maintenance work orders, the best situation is that after the master arrives, it can be repaired on the same day, but it takes at least 2 days from failure to recovery. The worse situation is that the master went to the scene for a monitoring and found that the parts were broken and needed to be transferred again, a week passed.
For the manufacturing industry, machine shutdown on the one hand will bring the cost of machine idle, if the impact on the delivery cycle may lead to order backlog and default risk.
What can digital transformation do?
First of all, collect various parameters of the machine operation through the sensor, and then monitor the health of the machine operation. Once the parameters are abnormal, the alarm is triggered, and the problem can be found in the first time, stop production, and avoid waste of raw materials. Moreover, the parameters of the operation can also be passed to the manufacturer after sale, and the manufacturer can guide the repair or find a solution remotely. This greatly shortens the idle window period of the machine. See our previous post: What is the Nature of Digital Transformation?
Data phase
With data as the basic factor of production, there is the possibility of further data analysis and data mining. This stage is from the experience-driven to data-driven transformation stage, everything speaks with data, data operation, data management. A typical word that has been said badly is smart XX, smart city, smart cultural travel, smart campus. A typical application is to make large screens. The accuracy of the data is not important, the important thing is that the screen should be cool. This stage has spawned and supported a large number of suppliers of large-screen development.
In addition to the gaudiness of the large screen, various data products that can be implemented continue to be polished and improved in the enterprise. From the initial data management analysis platform to data warehouse, user portrait, data center, data lake. Data is used more and more in the process of analysis and decision making, and product intelligence and fine operation.
Using good data can really help enterprises make ideal and accurate decisions, reduce costs and increase efficiency. The digital degree of the Internet industry is already very high, far ahead. After several years of digital construction, banks, real estate, pharmaceutical companies, and traditional retailers have begun to implement digital applications on the basis of digitalization.
Numerical intelligence stage
With the explosion of GenAI in 2023, more and more intelligent applications began to flood into the public field of vision. Artificial intelligence is a word in the 1990s, and in the past it was mainly applied to machine learning, algorithm models in personalized recommendation, thousands of people in front of pattern recognition (image recognition, speech recognition) and other scenarios. After the large model gradually matures, the application scenarios have been greatly enriched, from digital human intelligent customer service, automatic generation of graphics, video content and so on.
5 opportunities for digital transformation
As the pace of digital transformation continues, what are the opportunities for businesses and individuals? For three different stages: 数字化转型网www.szhzxw.cn
Enterprise-oriented information products or services, that is, enterprise-service-related opportunities
The main opportunity points are in some industries with a poor digital foundation, such as industry and manufacturing. The services that can be provided include: enterprise information services (OA office, online collaboration software, IM, product lifecycle management, project management tools, etc.), which is also the main work content of most B-end products
Products or solutions for data applications in the data phase
With the digital data foundation, how to use the data, enterprises in the early stage of transformation will be confused. Some input of best practices or solutions is required. You usually start with the most basic data visualization. Anyway, let’s do a few big screens first (the data might all be fake). In this regard, some enterprises and institutions still have more wisdom XX needs.
Data application tools
The efficiency of data analysis and use is often restricted by professional data development and product resources. To do a good job, we must first use our tools. Good data tools can greatly improve the efficiency of data empowerment.
Big Data infrastructure
The economic base determines the superstructure, and data applications and values require a solid foundation, such as clusters, server resources, and now cloud computing. For example, one-stop big data development and operation and maintenance system, data assets and governance, data quality and related talents, products, services, etc 数字化转型网www.szhzxw.cn
Intelligent application
In addition to the past AI algorithm application scenarios, GenAI has spawned more possibilities, such as the insurance industry, many insurance consultants’ daily work is to answer various questions of customers, in fact, these terms have, but dozens of pages of documents really no one is willing to see, compared with the past text matching, Q&A fixed questions and answers, The large model provides a more intelligent multi-round scene dialogue. You can use digital people or other interactive means to achieve fast and accurate response to relevant questions.
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