Hitachi Solutions Internal DX Promotion Case Study ~Data Utilization Platform (Data Lake/BI Tools) Edition~

The first case study is an order heatmap. This tool displays companies on the vertical axis (including existing and potential clients) and key business areas/product categories on the horizontal axis. Darker red indicates higher transaction volumes. This helps identify 'white spaces' where sales opportunities exist. For example, by filtering for the real estate industry, sales teams can spot under-served clients compared to competitors within the same sector. This enables customer success sales to devise strategies, insight sales to conduct digital marketing, and field sales to convert leads into orders, resulting in a 26% increase in case creation.

The speaker, Hachisuka from Hitachi Solutions, welcomes attendees to the online seminar on internal DX promotion case studies. The presentation will cover a company introduction, an overview of the data utilization platform, three case studies of the platform's use, and a summary.

Hitachi Solutions has approximately 5,000 employees, with 4 domestic and 8 overseas group companies. They provide solutions to 5,744 clients and collaborate with 142 alliance partners, offering products through direct sales and 169 sales partners. Their key business areas include life sciences, distribution, services, manufacturing, mobility, and general IT. Hitachi Solutions has also received DX certification from the Ministry of Economy, Trade and Industry, and this presentation will share internal DX examples that led to this recognition.

Hitachi Solutions began building its current data utilization platform in 2018 to leverage dormant data assets. The platform collects data from various internal business systems, proprietary data from business units, and data from the Hitachi Group and Hitachi Solutions Group. This data is stored in a data lake, transformed, and then provided via BI tools or CSV data. This system supports data-driven sales activities and addresses compliance risks related to attendance management, with specific examples to be discussed. Key to its success are master data maintenance, including integration with external corporate information databases and product masters linked to 8 key business areas, and the IT DX promotion department's focus on user-friendly content and promoting its use.

Previous attempts at data utilization were unsuccessful due to three main issues: failure to identify user needs, leading to unappealing content; misalignment between management's directives and users' actual needs; and lack of post-creation promotion to ensure content usage. These lessons informed the current project, emphasizing understanding user requirements, developing 'killer content' that encourages action, and fostering a data-driven culture.

The second case study focuses on product-centric analysis. Users can select up to five products and view their order status over time as a graph. For the current year, budget and projected sales figures are displayed. The tool also provides insights into the types of industries and company sizes that purchase these products. This allows for strategic planning based on product demand and market segments.

The third case study is an attendance management system designed to help managers comply with revised labor laws regarding overtime restrictions. The system provides managers with real-time data on employee work hours. For example, if an employee has already logged 13 days out of 20 and has exceeded 60 hours of overtime, the system flags them as being in a 'warning zone,' projecting over 100 hours of overtime if the pace continues. This allows managers to take action to reduce workload. The system also tracks cumulative overtime exceeding 45 hours in a year (up to 6 times limit) for legal compliance.

Key success factors include maintaining master data consistency through external corporate information databases and product masters, enabling analyses previously impossible and increasing content usage. Ongoing efforts involve visualizing usage, continuously providing new content, and gathering user needs to offer timely and relevant information. This has empowered users to move beyond mere observation to data-driven actions. Future plans include further evolving the data-driven culture, utilizing AI for analysis suggestions, and fostering human talent capable of discovering challenges and understanding data implications from all system data.