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Artificial intelligence is not only a technology, but also a business model change.

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With the development of technology, cloud computing technology is progressing, and its purpose is also changing. In the current new normal, there are five main elements of cloud computing, which are critical for organizations that want to remain competitive and relevant: cloud native applications, deployment of cloud-based strategies, integration of mobile applications into the cloud, building a viable data lake, and democratization of data use. These analytical tools are essential to help industry departments become AI-driven enterprises. Artificial intelligence is not only a technology, but also a business model transformation that can not be ignored.

Gartner points out that 80% of the internal deployment development software now supports cloud computing or cloud native. The growing cloud computing ecosystem enables enterprises to operate faster, more flexible and more real-time, thus bringing competitive pressure. Acceptance of cloud native and multi-cloud approach as a new normal means that enterprises can avoid cloud computing vendor lock-in and provide more than 5.9 response rates (99.999%) to avoid an average loss of millions of dollars per downtime.

Since 68% of organizations have formulated or are implementing digital transformation strategies, most of them regard cloud computing as an important part of their transformation strategies, the debate on the term "digital transformation" continues, because the essence of what enterprises must do always exists in the cloud. In short, companies need to embrace these five key elements of cloud computing in order to remain relevant in the fierce digital arena of all industries.

In addition, in 2019, three AI technologies that are critical to enterprises are vision, language and dialogue. Industry leaders need to take advantage of these services in their own environments, introduce AI in cloud computing into existing applications, and enable enterprises to use data science in short supply. Therefore, having a viable data lake and marking and receiving data in the right way is more effective than simply investing in analytical services.

Cloudy Drives Digital Transition

A recent study by the Cloud Computing Industry Forum (CIF) found that organizations are more open to multi-cloud environments, and three quarters of them use a variety of cloud computing services to promote their digital transformation process. Business managers are finally realizing that cloud computing vendor lock-in can hinder creativity, availability and liquidity brought about by cloud-based approaches.

More and more enterprises are using hybrid cloud and multi-cloud environments from AWS, Google, Microsoft Azure and other large vendors. Cloud computing providers have also created hosted versions of open source stacks (such as Apache Kafka) for certain functions, encouraging this trend. This makes it easier to migrate from one cloud platform to another, which is the key to avoid vendor lock-in, while still allowing businesses to focus on digital transformation.

The standardization of cloud computing means that cloud computing provides enterprises with more cost-effective services to run workloads, and cloud service prices of cloud computing service providers often change. For enterprises with critical workloads and cloud experience, cloud computing can improve uptime and competitiveness.

Enterprises can maximize IT expenditure through a multi-cloud strategy, because the standardization of cloud native technology allows enterprises to use appropriate cloud computing providers to get the right products. For example, microservices implement event-driven extensions (such as Black Friday) through containerization (such as Docker) and orchestration (such as Kubernetes). Very large-scale configurations through cloud computing infrastructure, such as thin clients (web applications, native mobile applications, Alexa skills), use multiple micro services, provide strong flexibility and flexibility, and self-repair functions and designs. Container orchestration, combined with cloud computing provider structure and regional functions, helps to resist some cloud interruptions.

Will native applications die out?

Migrating native mobile applications to the cloud is also critical to the implementation of the Internet of Things (IoT), artificial intelligence and virtual reality, which means that native applications need to be synchronized. If it is not part of the cloud computing portfolio, the cost of migrating applications will be high. Finally, a viable data lake is needed to manage information in a pragmatic way and avoid turning it into a swamp, which is essential to maintain a competitive advantage when introducing artificial intelligence and machine learning (ML) into a combination of data science tools. So for companies to remain relevant, they have to accept AI, because it is not just a technology; it is a business model change that can not be ignored.

These cloud computing trends will continue to play a role in the enterprise digital transformation strategy, and will help to become an AI-driven business, including in-depth understanding of the role of applications, data, analysis and identity management will promote the efficiency and compliance of enterprises.

Creating viable data lakes

Over the past five years, Internet users have increased by more than 82%, while Gartner, a research firm, expects data volume to grow by 800% by 2022, 80% of which is unstructured data.

With the continuous deployment of cloud services, 2019 is critical for enterprises to build available data lakes in their organizations. Enterprises can add a set of intelligently discoverable metadata tag data to all systems, devices and services, extract value from a large number of structured and unstructured data generated every day, which will enable them to run analysis, business intelligence, machine learning and artificial intelligence, and gain important insights into new efficiency in order to gain competitive advantage.

Compared with traditional data warehouse methods, one of the key principles of data Lake architecture is to provide a location where all raw data can be placed without conversion or loss, so that any conversion of data can be replayed at will. The challenge for this method in enterprises is to maintain the control level of data landing so that the quantity and accuracy will not become too large or data marsh.

By using Lambda architecture, enterprises can benefit from using near real-time streaming data, and can see important events almost immediately. Compared with the traditional data warehouse method, this has taken a significant step. The traditional method has to wait 24 hours. Then, companies need practical ways to understand data, such as storing taxonomies, managing data workloads through taxonomies (e.g., data security and who has access), and data science tools to help data scientists create/apply good equations to data pools to improve future analysis.

Democratization of Data Science

Artificial intelligence is a business model transformation that can not be ignored. In 2018, AI and machine learning began to gain more attraction, especially when dealing with structured and unstructured data to help enterprises make intelligent decisions and discover trends. Nowadays, cloud computing artificial intelligence can provide large-scale intelligent functions, scanning a large number of images, audio, video or text files to track patterns and anomalies. Some AI levels of operation were impossible even two years ago, and will produce unparalleled commercial value. Nowadays, people are more and more aware of how cloud computing artificial intelligence will innovate business models in the cloud native ecosystem.

In 2019, more and more companies are incorporating AI into their digital strategy. The biggest gains will be the use of cloud computing artificial intelligence to replace human beings to accomplish more trivial tasks, and apply the level of intelligence to basic business processes. For example, AI chat robots can answer 80% of the repetitive questions in contact centers, allowing staff to deal with more complex and important questions. These intelligent tools can eliminate the management burden while providing a higher level of customer experience.