User Persona

Updated 2025.03.19

In Mellerikat, the available features and achievable goals vary depending on the user's role. To make the most efficient use of Mellerikat, it is important for each user to identify their type and understand what functions are available to them, and what tasks they can accomplish using those functions. This page is intended to help users gain a clear understanding of the different user types and leverage Mellerikat effectively based on that understanding.

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User Types

Data Scientist

1. Characteristics

   • Possesses strong capabilities and enthusiasm for data analysis and modeling
   • Highly interested in transforming work processes through AI technologies

2. Goals and Needs

   • Efficiently analyze data and derive valuable insights
   • Develop accurate predictive models using the latest AI technologies
   • Deliver data-driven solutions that contribute to real business challenges
   • Automate the data analysis process and improve productivity

3. Pain Points

   • Inefficiencies in repetitive data preprocessing and cleansing
   • Difficulty in continuously managing AI models for real-world business scenarios
   • Challenges in adapting to changes in the operational environment and optimizing AI models

AI Operator

1. Characteristics

   • Values hands-on experience and know-how from working in business or industrial sites
   • Strong interest in improving product quality, productivity, and efficiency
   • Has a general understanding of data analysis and AI, but not a technical expert
   • Prioritizes practical application when adopting new technologies

2. Goals and Needs

   • Improve operational efficiency and productivity through AI adoption
   • Reduce costs by replacing manual labor with AI technologies
   • Require high-performing AI technologies that can be continuously operated

3. Pain Points

   • Lack of knowledge and understanding required to build complex AI models
   • Difficulty in collecting, cleaning, processing, and analyzing field data
   • Challenges in securing budget for AI technology adoption
   • Difficulty in hiring and retaining experts for ongoing AI operations

Data Engineer

1. Characteristics

   • Experienced in large-scale data processing and infrastructure management
   • Skilled in system performance optimization and maintenance
   • Manages and integrates data from diverse sources and formats
   • Understands automation technologies for data pipelines and workflows

2. Goals and Needs

   • Build a reliable and scalable data architecture
   • Improve productivity through automation of AI model deployment and operations
   • Optimize the efficiency of data processing and analysis
   • Seek solutions to reduce operational costs while maintaining consistent performance

3. Pain Points

   • High technical requirements for building complex data pipelines
   • Time-consuming data management and cleansing processes
   • Compatibility issues between different systems
   • Need for continuous learning of the latest data technologies and tools