Jim Gary, a famous computer scientist and Turing Award winner, took data technology as the fourth paradigm of scientific research in addition to theory, experiment and Simulation in 2007, thus laying the core positioning of big data technology in academia and industry.
Big data and cloud computing technology have entered the second decade of rapid development. With the rise and outbreak of artificial intelligence technology, more and more enterprises begin to build the next generation of intelligent big data cloud platform as a strategic plan by combining these three emerging technologies, and complete the integration of enterprise applications, data and assets based on it, So as to complete the upgrading and transformation of enterprise infrastructure.
Integration of cloud computing with big data and AI
According to the prediction of Bain & company, a famous analysis company, by 2020, the scale of the global cloud computing market will reach about US $400 billion, of which the compound annual growth rate of PAAS and IAAs will reach 27%. At present, the vast majority of Fortune 50 enterprises have announced their cloud planning, and a large number of start-ups build the basic IT architecture on the cloud from the beginning, and design enterprise applications based on the cloud architecture, that is, cloud native applications.
With the development and maturity of cloud computing, the technical trend of the cloud computing market is changing significantly, including the new generation of big data intelligent cloud, industry cloud, IOT cloud and other new forms of cloud technology, which have achieved very rapid development in the past two years. Meanwhile, public cloud service providers at home and abroad, such as Microsoft azure, AWS, Tencent cloud and Alibaba cloud, are doing in-depth exploration of intelligent scenarios, while public cloud chasers represented by Baidu BDL, JD cloud and Jinshan cloud are taking big data and AI as the next wave of business revolution, trying to improve the way enterprises use cloud to process big data through AI with cloud as the carrier.
Data service evolution route of large enterprises
With the help of the development of cloud computing, big data and artificial intelligence technology, enterprises represented by Google, Facebook and Amazon have completed the transformation from it giant to DT giant, completed the construction of enterprise ecology through the advantages built in the field of data and technology, and quickly transformed the retail industry with big data and technology The form and mode of traditional industries such as advertising industry and media industry. These companies digitize their business operations through rapid business digitization and data capitalization, and accelerate the transformation of business value through data-driven business scenarios. They basically evolve step by step according to the following route, and complete the transformation process within a few years.
At this stage, enterprises use cloud computing technology and big data applications to build a flexible technology platform to support large enough data magnitude, large data dimensions and diversified data types. At the same time, they process real-time low latency tasks, high concurrent query services and computing intensive batch processing and analysis services to meet the needs of various data business scenarios. On the basis of this technical platform, enterprises can start to carry out relevant data unification work, including building a unified computing output platform, uniformly managing metadata and data standards, and gradually integrating data into the platform.
After completing the construction of powerful computing platform and data integration, the data is only saved in a relatively original way. Enterprises also need to complete data integration and final capitalization through data analysis. In this process, ETL is still the most commonly used data integration method, and a large number of low latency computing based on event processing has gradually become the mainstream. After the completion of data integration, it is necessary to ensure the quality and effectiveness of data through effective data quality management, so as to ensure that data can really participate in business production. With more and more high-quality data accumulated in the platform, enterprises can start to complete the asset work according to the characteristics of data, including the connection between data and business dictionary, data management process, etc., so as to turn the original data into valuable assets.
Data is the means of production and computing power is the production tool. After data unification and capitalization, enterprises have strong computing power and rich data assets, so they can easily build data business. At present, the typical data services that produce great value are mainly distributed in the fields of data operation, intelligent application and online data service. Through the effective combination of big data and artificial intelligence technology, we can explore the value from the massive enterprise data. In the era of intelligent cloud, the new generation of business architecture represented by microservices has effectively replaced the traditional single application architecture, so that enterprises can develop and output data services more flexibly; Data analysis modeling has gradually become a trend. By sharing the constructed machine learning and artificial intelligence models within the enterprise, we can maximize the economic value of application business and.
Enterprises have a unified data, computing and business platform, so that more developers can do self-service business development on the platform. At the same time, a large number of businesses will generate new data and assets, attract more developers to build businesses, so as to form a closed loop of data business, form positive feedback between data, business and developers, and build a complete data ecology. In this way, enterprises can complete the transformation and upgrading of a new generation of data business, better operate the company’s business and promote the sharing of services and applications among enterprises. Therefore, the enterprise technology department has changed from the traditional support department to the enabling and innovation guidance department, and completed the business enabling and ecological construction of the business department through good data ecology and standardized data asset services.
At the same time, it should be pointed out that enterprises do not necessarily develop in strict accordance with the above four stages in the process of business evolution, and there may be certain overlap and repeated iteration in each stage. Especially in the early exploration process of big data, enterprises often first explore the idea of big data construction through vertical data business as the starting point. However, with the rapid development of big data, cloud and artificial intelligence technology, the technology evolution based on these four stages will be more mature in technology and business, and can better match the enterprise’s data strategy.
Conclusion and Prospect
The application and development of cloud computing promote the evolution of big data and artificial intelligence, and the integration of the three promotes the arrival of data commerce. It can be predicted that data ecology will become an important market in the future. In the future, data will play an important role in business strategy and operation. Establishing a perfect enterprise data ecology according to the evolution route of data business will help to improve the level of enterprise data management, deepen the value significance of data in business development strategy, help enterprises realize intelligent transformation, and occupy a leading position in the era of data.