By Zhou Wenchao
Enterprises face unprecedented challenges and opportunities during their digitalization. With the rapid development of AI, the integration of data and AI accelerates the reshaping of operations models, competitive policies, and market prospects for enterprises and becomes the core driving force for enterprises to adapt to varying business requirements, improve competitiveness, and remain innovative. This article describes the importance and role of the Data + AI platform in the intelligent transformation of enterprises.
AI first appeared in the 1950s and has been continuously improved since the 1990s with the explosive growth of data volume and continuous optimization of computing power. It is now widely used in various industries, bringing great opportunities worldwide. AI helps enterprises make decisions in an efficient and accurate manner, promote innovation, optimize operations, implement organizational reforms, and enhance competitiveness.
Although AI brings unprecedented opportunities for enterprises, enterprises face a series of challenges during its actual implementation. These challenges affect the application and value realization of AI technologies in enterprises.
(1) Data quality and governance: The application of AI depends on high-quality data and is affected by factors such as data accuracy, dispersion, and freshness.
(2) Integration of data assets and AI: To realize the value of a large number of data assets, enterprises must apply an efficient integration mechanism to enable not only the mutual empowerment of data assets and AI but also the collaboration between the data asset team and AI team.
(3) Technical thresholds, maturity, and reliability: Although AI develops at a fast speed, it still faces challenges of high technical thresholds and technical immaturity during implementation. Immature technologies make business become unstable and insecure, and high technical thresholds decrease the efficiency of AI implementation.
(4) Costs, talents, and organizations: AI implementation always requires high investments in the early stage, including investments in infrastructure and talent training. Enterprise transformation also involves the reformation of business processes and organizations. In this case, enterprises need to estimate the return on investment to reduce costs and improve efficiency.
Enterprises can use the Data + AI solution to effectively deal with the challenges in AI implementation.
The Data, Information, Knowledge, and Wisdom (DIKW) model proposed by T. S. Eliot makes it clear that data is the basis for building AI. To realize the large-scale and high-quality implementation of AI, enterprises must use the Data + AI solution to obtain strong data support. The research of Deloitte shows that 28% of leading enterprises in the AI industry are using the Data + AI solution to integrate data and AI for efficient and high-value AI implementation.
Data + AI refers to the combination of data and AI to support full-lifecycle workflows that include data collection, data preparation, model development, model deployment, model monitoring, and model governance.
Data + AI can help enterprises resolve AI implementation challenges by providing the following benefits:
Improved data governance and quality
Data + AI provides a unified data governance framework to ensure data accuracy and availability and improve data quality.
Efficient integration of data and AI on one platform
Data + AI allows the data asset team and AI team to collaborate on end-to-end AI development on the same platform. Data Management (DMS) provides strong data support for AI implementation, and AI increases the intelligence level of DMS, such as building DMS Data Copilot based on large language models (LLMs). This forms a virtuous circle in which data and AI promote and improve each other.
Lower technical thresholds and mature and reliable AI technologies
Data + AI not only provides verified AI technologies and services but also supports visual dragging operations to lower technical thresholds. In addition, the lifecycle management and operations capabilities of Data + AI allow enterprises to continuously raise the AI maturity and reliability and improve AI-based productivity.
Fewer investments required for infrastructure, talent development, and organizational reforms
Data + AI allows enterprises to establish their business on a cloud platform and provides cost-benefit analysis and automated AI application development for enterprises to reduce costs and increase the return on investment. It also simplifies AI applications to reduce the dependency of enterprises on AI talents. In addition, all teams of an enterprise can work by using a single data source on the same platform to collaborate and share knowledge across departments. This reduces the investments in talent development and organizational reforms.
Customers from many industries have used Data + AI to realize continuous and high-quality AI implementation. Compared with the traditional AI implementation, AI implementation with Data + AI has the following advantages:
Data + AI provides high-quality data for AI to generate more accurate and reliable results. For example, e-commerce platforms predict the purchase habits and preferences of users by analyzing high-quality user behavior data. This way, the conversion rate (CVR) and customer satisfaction are improved.
Data + AI provides more real-time and dynamic data to help AI quickly adapt to changes in markets and improve decision-making efficiency. For example, based on real-time and dynamic sales data, enterprises in the retail industry can identify abnormalities and trends at the earliest opportunity and then make decisions.
Data + AI summarizes information from scattered data for AI to provide more personalized services. For example, AI in the gaming industry identifies the intentions of players based on their historical feedback and behaviors in each game and then provides corresponding game services.
Data + AI provides data and knowledge support in multiple dimensions, such as business domains and individuals. This lowers the threshold for starting AI application and improves service efficiency. For example, AI in the financial industry generates knowledge that can be identified by LLMs based on technical and operation metadata in platforms and automatically maintains the knowledge. This way, investments in cold start are effectively reduced and more accurate results are available.
Data + AI enables multimodal data management and end-to-end AI development. This accelerates AI services and saves labors, management costs, and resources. For example, in the intelligent cockpit field of the automotive industry, the establishment of AI scenarios can be accelerated based on multimodal data management combined with end-to-end Data + AI development. This reduces the R&D investment.
Data + AI is the core driving force for the intelligent transformation of enterprises. It contributes to the high-quality and large-scale application of AI in enterprises. Based on the report of Deloitte on the current situation of AI application in enterprises and the recent best practices of Alibaba Cloud, Data + AI requires enterprises to provide a unified platform to deeply integrate data and AI to continuously improve the efficiency of data-based decision-making and AI application. The platform must support the following capabilities:
Structured and unstructured data are always used in the application of AI. Therefore, the Data + AI platform must support multimodal data management to allow enterprises to efficiently use data of various types.
Data + AI development includes data processing, model building, and LLM training. The platform must provide comprehensive development tools and manage the full development process from data to AI models to ensure the deep integration of data and AI. In addition, the platform must allow different teams to collaborate with each other. This reduces management costs and improves development efficiency.
To ensure that AI applications can efficiently generate results, the platform must provide accurate, reliable, and secure data and monitor the quality of models and the actual performance of AI applications. Therefore, the platform must have the capabilities to manage metadata, data quality, and data security. A unified governance solution must be used to manage data and AI in a comprehensive manner. This increases the overall performance and reliability of AI applications.
A single engine cannot meet the requirements of all AI applications due to differences in data processing requirements and algorithms. Therefore, the platform must be able to adapt to different engines. Then, you can select engines based on your business requirements. This is crucial to ensuring the effectiveness and efficiency of AI solutions.
In September of this year, Alibaba Cloud ApsaraDB announced DMS + X: Unified, Open, and Multimodal Data Management and Data Serving Driven by Data + AI at Apsara Conference 2024.
The platform simplifies data management and AI development by using OneMeta and OneOps. OneMeta unifies metadata services across clouds and supports more than 40 types of data sources, which seamlessly integrates multiple clouds and self-managed data sources. OneOps is integrated with Notebook and Copilot to provide an integrated Data + AI development environment for data, machine learning models, and LLM models. This allows DMS + X to manage Data + AI development throughout its lifecycle. X in DMS + X represents a data engine, such as PolarDB, ApsaraDB RDS, AnalyticDB, and Lindorm. Based on DMS + X, Alibaba Cloud can help enterprises integrate data with AI, deploy business, and realize business values at the earliest opportunity.
In the future, data and AI will be connected on the Data + AI platform in a closer manner to achieve great breakthroughs in the AI construction of enterprises. The breakthroughs include but are not limited to:
• Intelligent decision-making: Data and AI are used for market forecasting and customer insights. This allows enterprises to develop more timely and accurate business strategies.
• Personalized experience: AI processes big data and provides personalized services to improve user satisfaction.
• Automation and efficiency: Automated processes improve operational efficiency. Resource allocation is optimized by using AI to reduce costs.
• Data-driven innovation: New services are developed to expand the market.
• Enhanced security: AI monitors security data to prevent cyber threats and strengthen information protection.
• Automatic execution: AI models automatically execute tasks based on decisions to improve management efficiency.
• Cross-field integration: Data from different fields are integrated to promote cross-field cooperation and innovation.
Data + AI will not only change the operations mode of enterprises, but also provide new methods for business growth. Enterprises must recognize the importance of Data + AI and use it as the key solution to promote intelligent transformation. This way, enterprises can remain competitive and leading in the market and seize opportunities in the future.
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