Digital Transformation Accelerated by Big Data Intelligent Engine with Integration of Hardware and Software
        Now, the ability to obtain insights quickly in massive amounts of data has become the key factor in digital transformation. Facing the diversified data in the age of the customer, organizations must acquire high-speed storage, efficient management, and intelligent analytics to create value through data insights both for themselves and customers.
        Challenges Posed by the Complex Technology Stack of Big Data and AI in Solution Selection
        Forrester classifies enterprise data processing into the data storage, elastic integration, situational service, and extreme transaction processing scenarios. Every scenario needs various technologies to handle heterogeneous data, multiple computing engines, and efficient data management.
        More than 90% Respondents
        More than 90 percent of those surveyed said their organizations would maintain or increase budgets for big data technologies in the coming 12 months.
        Maximized Utilization of Big Data and AI Through Optimal Full-Stack Deployment
        Big data and AI are distributed applications that require intensive computing, memory, storage, and network resources, which is a great obstacle for system architects.
        69% Respondents
        69 percent pointed out that considerable data silo issues within their organizations make the management and integration of big data projects more complex.
        Demands for Fast Deployment and Iteration Ability of Big Data Projects
        In the age of the customer, both internal and external environments of organizations are constantly changing. Long deployment time and iteration cycle cannot meet the needs of service departments.
        97% 97 percent of the respondents require big data solutions to be deployed and put to use quickly.
        91% 91 percent of those surveyed hope that big data projects can be flexibly adjusted and iterated.
        • Masking Technology Complexity by Solutions Integrating Hardware and Software
          More than 60 percent of the respondents show concerns about the out-of-the-box functionality, simplification of technology composition, full-stack tuning, and full integration of hardware and software of big data solutions.
        • Data Value Increased by AI
          93 percent of the respondents express that their organizations’ investment in the data field is on machine learning and AI.
        • Focusing on Specific Industry, the Key Factor in Releasing Data Value
          More than 94 percent of the respondents think the number of customers and the number of segmentation scenarios covered by solutions are important factors.
        • More than 60%
        • 93%
        • More than 94%
        • Government
        • Public security
        • Education
        According to our survey, the government, education, and public security fields have been exploring application of big data in various scenarios. We believe digital transformation of these fields can be greatly accelerated by intelligent data engines with hardware and software integration.
        Forrester considers that digital transformation requires improvement of digital experience, optimal digital operations, innovation incubation, and establishment of a digital ecosystem. Forrester suggests non-IT-driven industries adopt the following strategies to resolve these problems:
        • Focus on service applications and select pretuned enterprise-scale intelligent data engines with hardware and software integration to hide the complex technology stack.
        • Rely on industry-specific platform and application ecosystems to deploy big data and AI projects as soon as possible.
        • Make breakthroughs in mature scenarios to drive digital transformation gradually.
        This Technology Adoption Profile was commissioned by H3C. This online survey queried 121 managers and technical leaders in big data-related management, operations, or purchasing departments in Chinese organizations. This survey was completed in May 2018.
        • Respondent industries 38%Government 31%Public security 31%Education
        • Respondent roles in big
          data-related decision making
          73%Decision influencer 22%Member of decision-making team 5%Decision maker
        • Organization size 17%500 to 999 employees 18%1000 to 2999 employees 35%3000 to 4999 employees 10%More than 5000 employees