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Legacy systems have various inherent problems and challenges, including the maintenance of vast system documentation. ITS adopts a data model-centric approach using the "TM method," achieving a reconstruction that incorporates new business requirements while inheriting the strengths of legacy systems. There are benefits such as "untangling and analyzing complex information (data)" and "analyzing facts that occur in business and structuring business transactions." 【Features】 ■ Untangling and analyzing complex information (data) ■ Analyzing facts that occur in business and structuring business transactions ■ Proof of Concept (PoC) for future system models that also fit new business models (ToBe) ■ Elimination of individual dependency through rule-based model creation techniques ■ Logical models provide a consistent common blueprint from system construction to maintenance *For more details, please refer to the PDF document or feel free to contact us.

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When drawing a data model, you can choose from multiple notations such as ER diagrams, UML class diagrams, and TM (T-shaped ER method). However, the significance of describing a data model lies in accurately expressing the meaning and rules of the business. If the interpretation of the business meaning and rules varies among readers of the same data model, or if the creator of the data model can choose multiple expression methods to represent the same business meaning and rules, it becomes difficult to share the business meaning and rules correctly among stakeholders using the data model as a common language. When creating a data model, it is necessary to choose a notation that is easy to share among stakeholders in line with the purpose. In this column, we introduce two simple examples of data model notations and the expression of business meaning and rules, so please take a look. *For more details on the column, you can view it through the related links. For further inquiries, please feel free to contact us.*

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In the field of building information systems, data models appear in various scenes of the development process, such as planning, requirements definition, and database design. Positioning the data model as a "common language" for constructing information systems, it is ideal to utilize the same data model consistently from the beginning to the end of the information system development. However, in practice, it is often the case that "conceptual data models," "logical data models," and "physical data models" are used interchangeably according to the progress of the development process. These data models are distinguished based on the perspective of representing data structures and the level of abstraction, but there are no clear definitions, and interpretations vary among individuals. In this column, based on the author's experience, I would like to introduce an overview of each model, including their usage and approaches to creation. [Overview] ■ Conceptual Data Model ■ Logical Data Model ■ Physical Data Model *For detailed content of the column, please refer to the related links. For more information, feel free to contact us.

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The first step for those involved in building information systems is likely to visualize the user's business operations. What methods are available to visualize business operations? One method that comes to mind is the "business flow diagram." The advantage of a business flow diagram is that it allows for an intuitive grasp of the flow of operations and serves as a common language for communication with users who may not be familiar with information systems, making it essential for building information systems. In any business process, information (data) is conveyed through forms and screens. The method of structuring (modeling) the meaning of business operations based on this information is called a "data model." A data model not only visualizes the information conveyed in business operations but also makes the rules of the business visible. In this column, we will explain its effectiveness with specific examples. [Overview] ■ Methods for visualizing the user's business operations ■ Benefits of describing a data model *Detailed content of the column can be viewed through related links. For more information, please feel free to contact us.

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There can be instances during requirement definition where what the user department wants to achieve is not accurately communicated to the system department. As a method to facilitate smooth requirement communication from the user department to the system department, our company implements requirement definition that connects to the next steps by adopting a data model analysis approach. By modeling the current business management information structure, we visualize it, organize user requirements, and translate them into the management information structure, clarifying what and how to manage. 【Ideal Requirement Definition】 (1) Organized user requirements (business requirements) (2) Systematized management information structure (3) Design that meets the requirements (system functional requirements) *For more details, please refer to the related links or feel free to contact us.

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We would like to introduce our "Data Consultation Service." A key point in promoting DX (Digital Transformation) is the establishment of an "information management infrastructure" that enables the effective use of data within the company. At ITS, we utilize the TM method to conduct data-driven business analysis and visualization, as well as to build the information management infrastructure. This approach reduces the risks associated with individual quality and ensures the consistency between logical management of internal data resources and physical database management, enabling effective data utilization. In supporting our clients' DX initiatives, we contribute to the construction of the "information management infrastructure" from both logical and physical perspectives. [Features] ■ TM method that enables business analysis, visualization, and the construction of an information management infrastructure - By using the TM method, which has clear rules and procedures as a methodology, we visualize the actual state of the business by mapping it to the TM data model diagram, while expanding the To-Be model to establish a stable information management infrastructure that is optimized as a whole. *For more details, please refer to the PDF document or feel free to contact us.

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We provide a management service for Safety Data Sheets (SDS) using RPA. The management of SDS information utilizing RPA involves extracting and processing the "SDS" sent from suppliers with a robot, and then importing it into our company system. 【Features】 ■ Correction of errors through visual inspection ■ Automatic input into the chemical substance management system *For more details, please download the PDF or feel free to contact us.

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