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With the diversification of business operations, the data structure has become more complex, and manual management has already reached its limits. Manual maintenance and operation not only lead to human errors but also pose significant barriers to sustainable utilization. To eliminate human errors and management burdens, automation and labor-saving through external tools are essential. We will explain the benefits of automating batch processing and updating report data, transitioning to a stable operational foundation. We propose a system of automation that reduces operational costs and achieves stable operations in our documentation.

Data users want to "analyze easily with their favorite tools," while administrators prioritize "control and stable operations." This gap in needs leads to a common issue of data stagnation and underutilization. It is important to build an analytical environment that can be easily used by non-engineers while complying with compliance regulations. To promote data utilization across the organization, we propose breaking the "utilization stagnation" through inventorying data and restructuring management systems. We will explain in the materials how to remove organizational barriers and accelerate data utilization governance.

In today's rapidly changing market and needs, the introduction of agile methods is required for building data infrastructure. By not deciding everything in the initial stages and repeatedly implementing and improving in short cycles, we can sequentially provide high-priority features. This method, which immediately reflects feedback, maximizes development speed and enables a direct connection to business value. We will provide a detailed introduction to the process of building a foundation that continues to evolve flexibly and quickly. You can learn agile development techniques for building a resilient data infrastructure through our materials.

To enable accurate and efficient data analysis, it is essential to design the relationships between data through "data modeling." By organizing business information and defining its structure, we can proactively eliminate data duplication and inconsistencies. When this is visualized, it serves as a clear guideline for foundational design and becomes the basis for advanced data utilization. We will explain how to create high-quality data that directly contributes to business outcomes. Please check the materials for the blueprint to build a consistent data infrastructure.

The use of an ETL tool that can flexibly respond from a small scale is the first step in building an advanced data infrastructure. It enables rapid implementation and operation by a small team without requiring specialized skills, eliminating the reliance on manual processes. By automating the processes of data extraction, transformation, and loading, it can prevent human errors and reduce operational burdens. We will discuss the benefits of storing data in a DWH, maintaining consistency, and enhancing the reliability of the analytical infrastructure. The advantages of utilizing ETL for efficient data collection and processing are condensed in the materials.

The creation of business value is influenced by the organization of "metadata," which defines the meaning and context of data. When metadata is properly managed, the reliability of the data and the accuracy of its interpretation are ensured, facilitating smooth decision-making across the organization. Going beyond traditional static management, "Active Metadata Management," which dynamically updates information during the analysis process, is essential. We will explain the three classifications of metadata management that serve as the shortest path to becoming an AI-ready organization. We will publish materials on how to organize "metadata" so that AI and BI can truly function in practice.

Metadata is classified into three categories based on its purpose: "Business," "Technical," and "Operational." Business metadata defines the meaning in a business context and maximizes practicality, while technical metadata defines types and relationships, supporting integration. Furthermore, operational metadata records update frequency and history, maintaining the freshness of the foundation. By centering on these axes, a reliable foundation is established where discovery, understanding, and control are optimized. The document details the classification and management methods of metadata that enable advanced data governance.

The in-house design and manual operation of metadata come with risks such as the hollowing out of governance and the obsolescence of information. To resolve this, it is essential to select tools that automate collection and updates, creating a foundation that AI can access instantly. We will explain the features and necessity of major metadata management tools such as Databricks Unity Catalog and AWS Glue. We will present best practices to prevent information silos and ensure that data assets are not left unused. We have compiled comparison points in a document to help you choose the most suitable management tool for your organization.

The "Semantic Layer" prevents discrepancies in different metrics between departments and supports cross-company decision-making. It interposes a common understanding between complex data structures and users, ensuring the consistency of outputs from BI tools and AI. Without the need for specialized queries, each department can directly access data using familiar "business terminology." We will also introduce major services such as dbt Semantic Layer and Looker. The document explains the mechanism that accelerates data democratization and enhances the accuracy of decision-making.

In the implementation of generative AI in business, robust data governance and security are essential. If the management of supply data is inadequate, it can lead to incorrect judgments and the risk of information leaks, making comprehensive design necessary. We will explain specific security items that support safe integration, such as access control, authentication and authorization, encryption, and log monitoring. We will present an organizational structure to ensure governance across the organization and promote projects in a healthy manner. We will publish materials outlining the procedures for formulating governance to safely integrate AI and data infrastructure.

To fully utilize AI in practical applications, it is necessary to update the traditional data infrastructure and meet specific conditions. The four essential points are real-time capability, flexibility, reusability, and "meaningful connectivity." A foundation equipped with these features enables advanced interactive collaboration with AI and facilitates rapid business reflection. We will present the conditions to create an environment where all employees can freely handle this infrastructure and accelerate the cultivation of a data-driven culture. The requirements for next-generation data architecture to survive in the AI era will be detailed in the document.

The rapid evolution of AI has expanded the possibilities for data utilization, but the premise lies in a consistent data foundation. A company's true competitive advantage depends not on how many AIs it has implemented, but on how sustainably it can maintain data that responds to practical needs. First, you should start by taking inventory of the existing data foundation and visualizing metadata to accurately understand your company's current position. Sharing "meaningful data" that is guaranteed to be trustworthy across the entire organization will be a sure first step toward business transformation. Please use this document as a "current situation assessment checklist" to accelerate transformation.

We provide the design and implementation of a modern data stack (such as Datalakehouse) that is essential for the effective use of AI and data. As an official consulting/SI partner of Databricks and Fivetran, we support the adoption of the latest technologies. We design and compare optimal architecture patterns that meet requirements, from ETL and DWH to AI model integration. We achieve sustainable foundational operations, including the establishment of operational rules and a Center of Excellence (CoE). We are currently publishing materials on proposals for building highly efficient data infrastructure using world-standard tools.

This is a project that achieved the visualization of equipment status and failure prediction using IoT data in the manufacturing industry. We implemented Azure Databricks (Datalakehouse) and built a foundation for processing and analyzing vast amounts of sensor data in real time. As a result, it has generated direct business impacts such as diversification of after-sales services and improvement of customer service. This is an example that supports advanced data utilization with a multi-layered structure from raw data to the Gold layer. We have published a document detailing the entire construction of a data lakehouse that transforms manufacturing site data into profit.

If you have concerns about data utilization or infrastructure development, please feel free to consult with NTP, which specializes in manufacturing and IT. This document presents the concept of an AI-ready data infrastructure, but flexible designs tailored to each company's situation are possible. We offer support at various phases, from the conceptual stage of the project to the implementation and operation of specific systems. We also accept more detailed case studies and individual consultations that go beyond just excerpts from the document. Take your company's data utilization to the next stage. Before making an inquiry, please first refer to the comprehensive document.

This is an example of a refresh of IT infrastructure that significantly reduced the burden of "data preparation," a major factor hindering the utilization of data. By implementing Databricks, we eliminated data silos that were scattered across various departments and established an integrated data platform. As a result, data scientists and analysts can now utilize data through self-service, successfully reducing preparation time by 80%. Currently, a culture where over 300 employees regularly use data has been established. We have published a roadmap for building an efficient data infrastructure in our materials.

"Although we have sensor data, it cannot be used on-site," and "We tried a PoC but couldn't achieve accuracy, and management approval has not been granted" — equipment maintenance often comes to a standstill at this "one step away." This case study reveals the steps taken to overcome three challenges hindering equipment maintenance in aging plants: 1. The risk of unexpected shutdowns due to aging, 2. The retirement of skilled maintenance personnel and the loss of tacit knowledge, and 3. The barrier of lacking a data infrastructure that halts PoC efforts. We will outline a four-step process centered around the Databricks Data Lakehouse (digitizing records → data integration → assetizing "intuition" → automating maintenance planning). [Recommended for those who:] - Want to implement equipment maintenance but are struggling with data infrastructure development - Have not achieved accuracy in their PoC and have not received management approval - Face challenges in inheriting tacit knowledge due to the retirement of skilled maintenance personnel - Have sensor data but are not fully utilizing it.

We will clarify the challenge faced by companies that say, "Although we have accumulated vast amounts of internal data, we are not able to utilize it at all for actual operations or AI applications." We will build an environment that smoothly integrates core systems (ERP/CRM), IoT sensors, and unstructured data such as images and audio. NTP is an official consulting and SI partner of Databricks, which provides cutting-edge "Data Lakehouse," and Fivetran. From the implementation of predictive dashboards (BI) to marketing AI support, we will create a robust foundation that can process data in real-time. *The configuration diagram of the modern data infrastructure proposed by Databricks/Fivetran official partners can be found in the materials.

This is a development case in the real estate industry that established a system for the automatic extraction of high-quality properties and the rapid display of promising property candidates on the web. We adopted Google Cloud Platform (GCP) as the cloud infrastructure and implemented a scalable AI data platform by combining Vertex AI and BigQuery. As a result, we have successfully standardized the procurement process, which previously relied on the expertise of the personnel. Consequently, we achieved an overwhelming result by reducing the enormous labor involved in property extraction by 80%. *Specific system configurations and secrets to success during the implementation and development phases are available in the presentation materials.

Do you feel that the data within your company is fragmented and not interconnected? NTP's manufacturing industry DX support service visualizes the reality of siloing. In many manufacturing sites, different systems and management methods are used by each department, leading to data fragmentation known as "siloing." This hinders overall optimization and is a significant factor delaying the progress of DX. Our company will thoroughly check how your internal data and business processes are fragmented and visualize it in a tangible way. We will propose an appropriate integration plan, incorporating successful case studies from other companies and BPR (Business Process Reengineering) know-how. Let's start by organizing the information structure. Download the introduction materials for our manufacturing industry DX support service now.