With the development of science and technology, a large amount of data from production to operation of enterprises is continuously recorded. Data is a telescope to discover completely different business boundaries; data is a microscope to judge consumption and life behaviors that are invisible to the naked eye; data is a radar to help companies predict future behaviors in advance.
Through artificial intelligence, the value of data is refined, combined with scenarios to solve problems, it can reduce costs and increase efficiency, and finally bring new vitality to business. This is the advancement from digitization to digital intelligence.
In short, digital intelligence is the organic integration of the two processes or levels of digitization and intelligence, and it is a higher demand on the basis of digitization.
From the perspective of management scope, digital enterprises focus on the internal management of the enterprise to meet relatively deterministic needs; digital and intelligent enterprises focus on customers from the perspective of the overall supply chain of the enterprise, and eliminate uncertainty in demand delivery as much as possible factor.
From the perspective of pain point resolution, digital enterprises are mainly to improve the operating efficiency of enterprises; while digital and intelligent enterprises are to find a support point for enterprise innovation and iteration. The digital and intelligent transformation of enterprises is to use the flow of intelligent data services to resolve uncertainties in complex systems, optimize resource allocation efficiency, and build new competitive advantages for enterprises in a world defined by data + algorithms.
Digital and intelligent transformation is a complex system engineering. In the process of further promoting digital and intelligent work, enterprises will face many difficulties and challenges. From a business perspective, enterprises need to optimize and reengineer business processes, restructure business operation management systems, improve collaborative processing mechanisms, establish data standards and specifications, integrate data resource systems, and strengthen data asset management; from a security perspective, it is necessary to establish corresponding Data security management and personal information protection system, strengthen the dynamic monitoring of data operation and use; from the perspective of intelligence, it is necessary to strengthen the construction of vertical application algorithm development for industry fine-grained problem scenarios, fully tap data intelligence, and provide agile solutions for smart applications. Serve.
As a digital tool, low-code can meet the digital needs of all corners of the long tail in terms of breadth; in terms of height, it is limited by the efficiency bottleneck of professional developers, and the production capacity of the construction system is insufficient. BI, AI and other capabilities, digital intelligence will be further improved; in terms of depth, the integration of high/low/zero code is deeply integrated to meet different development needs and environments; On the one hand, it can improve the efficiency of digital asset accumulation; on the other hand, it can build ecological co-construction and precision twisting in terms of precision.
From the perspective of operators, on the one hand, the low-code platform can shorten the development cycle, improve efficiency, accumulate value, productize capabilities into reusable assets, and establish a new model of safe, reliable, and quality-assured software supply to realize value realization; On the one hand, low-code capabilities help it break through service boundaries, extend the coverage of digital intelligence, allow ecological aggregation, upgrade from traditional communication services to digital intelligence society and digital intelligence life services, and improve the level of digital intelligence openness.
After years of experience in software development, LeaRun has independently developed a full-stack enterprise-level development tool. Through the development model of "framework + components + services", we will build an integrated digital platform for customers to support the rapid construction and iterative upgrade of applications. In the system development phase, it provides a wealth of components and services to help developers quickly build business applications; in the system operation phase, it provides a variety of operation and maintenance and monitoring tools, combined with open low-code, micro-services, and containerization technologies, for system deployment , System update, system operation, and system monitoring continuous service, realize the integration of DevOps development, operation and maintenance, and respond to demand changes efficiently.
Digitalization is the premise and foundation of digital intelligence, and digital intelligence is the fusion of digitalization and intelligence, the evolution and advancement of digitalization, the higher-level goal of enterprises, and the adaptation to the current world economic environment and the domestic economic environment. An inevitable part of the cycle. In the transformation of digital intelligence, enterprises need not only digital transformation, but also need to innovate a whole set of life-cycle service and operation solutions to realize the commercialization of data and provide enterprises with the flexibility needed to gain a competitive advantage and speed, enabling innovation and rapid response to market changes.