Data is often referred to as a corporate asset, but without proper utilization, its value remains untapped. Merely holding data is not enough; it must be managed, utilized, protected, kept fresh, and combined in ways that enhance its value. This requires the formulation of plans, policies, schedules, and procedures, as well as the development of systems to oversee these processes, all of which fall under the umbrella of data management.

Recently, data management has grown more complex, not only encompassing structured data traditionally managed within Relational Databases (RDBs) but also unstructured data such as images, audio, and text, along with metadata that explains the data’s meaning. Moreover, the pathways through which data is acquired are expanding beyond core system data like sales and HR information to include customer data from surveys or membership databases, as well as data purchased and used under contract from other companies. Each type of data must be managed according to its intended use and the scope of its disclosure.

As a systematic framework for organizing data management approaches, the Data Management Body of Knowledge (DMBOK) exists, but it is not necessary to implement all aspects of it. Instead, we tailor the necessary components to fit the client’s business orientation (e.g., how heavily it relies on organizational data) and the reality of data collection (e.g., whether purchasing external data or selling data externally is prevalent). We will continue to provide support to ensure the optimal realization of data management tailored to these needs.

Through thorough interviews, we identify the challenges within current data management practices. Based on the principles of the Data Management Body of Knowledge (DMBOK), we then organize the necessary themes and approaches to be addressed. Primarily, the evaluation begins upstream in the data lifecycle, starting with the establishment of essential processes and rules for data governance. From there, the focus shifts to considerations of data architecture, data modeling, data management operations, and security, eventually leading to deliberations on data integration, document management, master data management, Data Warehousing (DWH)/Business Intelligence (BI), metadata management, and data quality management.

While this is the general approach, specific circumstances may require starting with asset management, especially if data is scattered and not fully accounted for. Additionally, we can focus on specific areas that need attention based on client requests, providing targeted support accordingly.

In all cases, the main goal is to enhance the value derived from data. We are committed to proposing the most suitable approach tailored to each client's specific needs.

• Establishment of data management focused on maximizing data value.
• Improved decision-making speed and operational efficiency.
• Enhanced data security.

• Company-wide Data Management Process Reform
• Support for Formulating Management Policies Aimed at the Active Utilization of Customer Data
• Support for Management Policy Deliberation Associated with the Integration of Customer Data within the Group
• Support for Management Policy Deliberation Due to the Integration of Customer Data Infrastructure