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This document explains the shift from fact-based approaches to "on-site implementation" methods. It includes detailed information on on-site implementation engineering (FDE), a roadmap to becoming AI-Ready, and case studies. This is a valuable read, so please take a look. 【Contents】 ■ Executive Summary: Competitive Advantage in the AI Era ■ Challenges: Why do DX/AI implementations stop at PoC? ■ Solutions: On-site Implementation (FDE) and the Palantir Model ■ Three Elements that Constitute AI-Ready ■ NTP's Solutions ■ Case Study: Specific Results at Yamaha Motor Co., Ltd. ■ Conclusion: A Time to Differentiate Through Implementation *For more details, please download the PDF or feel free to contact us.

• Software (middle, driver, security, etc.)

This document is a guide for building an AI-ready data infrastructure for the manufacturing industry. It provides a detailed explanation of the advantages and disadvantages of AI utilization, the importance of "maintenance" and "design" in determining the success of data integration, the worsening shortage of data personnel, and the shift towards "de-personalization." We also introduce methods for accelerating data infrastructure development and metadata management to unlock the true value of data utilization. [Contents] ■ Trends in generative AI utilization and challenges in data utilization ■ Why data utilization is not progressing as expected ■ Requirements for data infrastructure in the era of generative AI *For more details, please download the PDF or feel free to contact us.

• Software (middle, driver, security, etc.)

This document explains predictive maintenance, which directly improves profit margins. It provides detailed information on the risks and costs of maintenance strategies, the biggest barriers for many manufacturing sites, and integrated solutions. Additionally, it includes information about a 30-minute free assessment, so please take a moment to read it. 【Contents (partial)】 ■ Why predictive maintenance directly improves "profit margins" ■ Phase by phase: Three pitfalls that hinder predictive maintenance projects ■ The "three major hurdles" that impede implementation and the barrier of specialized knowledge ■ Solving the three major hurdles with Databricks ■ Digitally capitalizing on the "intuition" of veterans ■ The significance of having professionals who understand the field provide "FDE (support)" *For more details, please download the PDF or feel free to contact us.

• Document and Data Management

In the manufacturing field, unexpected line stoppages pose a fatal risk that can lead to losses on the scale of tens of millions of yen. Traditional reactive maintenance cannot prevent failures, and preventive maintenance has been challenged by increased costs due to excessive parts replacement. Predictive maintenance is a strategic investment aimed at maximizing profit margins by balancing these issues, maintaining operational efficiency while minimizing costs. We will provide a detailed explanation of the maintenance approach that is directly linked to management indicators. You can find the complete overview of the maintenance strategy that dramatically changes profit margins in this document.

• Other information systems

The introduction of predictive maintenance faces different "barriers" at each phase, causing many companies to stagnate along the way. The main hindrances are the "lack of clarity in ROI" at the pre-implementation stage, "low data quality" during the prototyping phase, and "enormous operational costs" during the production phase. At the root of all these failures lies a structural issue: the absence of an "AI-Ready data infrastructure" that assumes the use of AI. Clearly defining measures for each phase is the shortcut to success. Please take a look at the materials for a roadmap to promote projects without failure.

• Other information systems

The implementation of predictive maintenance requires an extremely broad range of expertise that spans both IT and the field. Three major challenges are "network construction" for stable data collection, "secure data management" to prevent siloing, and "AI model development" using advanced algorithms. If you try to optimize these individually within your company, the system will become more complex, and costs and time will escalate endlessly. It is essential to build an efficient implementation process. The technical approaches to minimize implementation costs are detailed in the materials.

• Other information systems

The key challenge is how to efficiently transform the vast and diverse raw data obtained from IoT devices into value. Databricks' data lakehouse integrates low-cost storage (lake) with high-quality centralized management (warehouse) that can withstand AI learning. By eliminating the complexity of infrastructure construction, companies can focus their resources on their primary goal of "prediction and analysis." We will explain the importance of a foundation that enables the shortest route to AI implementation. The ideal configuration diagram for the data infrastructure is included in the downloadable materials.

• Other information systems

We will solve the challenge of technology transfer that many manufacturing sites face by utilizing AI for digital asset creation. Simply analyzing sensor values (such as vibration and temperature) makes it difficult to predict true anomalies. By linking the event logs from the field that experienced professionals use to explain "why that operation was performed at that time," we can incorporate the long-developed intuition for detecting anomalies into the AI model. This enables us to elevate individual technical skills into a lasting asset for the entire organization. We provide a detailed explanation of specific methods for embedding expert skills into AI in our materials.

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Successful predictive maintenance requires not only technical skills but also a deep understanding of the "context" in the field, along with a gritty execution capability. NTP Corporation is composed of domain experts familiar with sites like Kubota and IT professionals from IBM. Our approach is characterized by the "FDE style," which goes beyond merely creating polished strategies; we immerse ourselves in the front lines of the field to ensure implementation. We will guide projects to successful completion even from a state where there is no in-house know-how. You can find more details about our field-led, collaborative engineering approach in the materials provided here.

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We will redirect the investment in the "thick reports" typical of traditional consulting towards building systems that operate on-site. In many projects, excessive requirements definition and report creation consume the budget, often neglecting the crucial implementation. NTP thoroughly eliminates low-value intermediate costs by having professionals from the field directly engage in hands-on work. Even with the same budget, we enable focused investment in "operational systems" that actually generate profits on-site, maximizing the return on investment. A detailed comparison of the cost structure with traditional methods is explained in the materials.

• Other information systems

We will present a current situation analysis and a realistic roadmap to take the first step towards predictive maintenance. Through a 30-minute free assessment, we will estimate specific effects, such as how much we can reduce unexpected downtime, based on the current data and infrastructure diagnosis. We will clarify practical steps on how to leverage existing assets while capitalizing on the insights of veterans. This will serve as a powerful tool to gain project approval from management. We provide detailed information on specific steps and diagnostic menus for implementation in our materials.

• Other information systems

The utilization of AI by domestic companies is rapidly shifting from "special measures" to "standard infrastructure." In a survey conducted in the fiscal year 2023, about half of the companies have already implemented or are considering AI, making it a prerequisite for business. However, while more companies are moving from consideration to proof of concept (PoC), generating results in practice has become a common challenge. To create a true impact, establishing an "AI-Ready" environment to quickly overcome PoC is the shortest path. Please check the materials for the new domestic DX trends and the roadmap for AI utilization.

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The greatest advantage of on-site-driven DX is its ability to quickly reflect the voices of business users and foster a culture of voluntary improvement. By leading from the front lines, trials become easier, and the cycle of hypothesis testing can proceed rapidly. Additionally, even when there are changes in operations, the responsible individuals can move forward with a sense of conviction, which enhances execution capability. In this way, cultivating a corporate culture that "actively engages in problem-solving" becomes the driving force behind digital transformation. We have compiled hints for promoting DX that maximizes the vitality of the front lines in a document.

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If the field promotes DX without company-wide rules, it will lead to the proliferation of systems and data fragmentation (siloing). Data fragmentation makes collaboration between core systems difficult and can lead to security vulnerabilities and increased management costs. To continuously generate results, it is essential to have a system that ensures data integrity while maintaining the freedom of the field. The solution to this is the establishment of a common infrastructure that supports the "AI-Ready" transformation of data. You can learn about the design of a foundation that avoids disorderly systemization and achieves company-wide optimization through the materials provided.

• Other information systems

The failure of data integration can be summarized mainly in two points: "lack of preparation of provided data" and "lack of established integration rules." Simply accumulating data is insufficient; a "transformation process" that optimizes the data into a usable format across systems is essential. Thoroughly understanding the current data retention situation and promoting integration design that aligns with the intended use is the shortcut to success. We will explain how to transform a mountain of non-standardized data into a valuable asset. We provide specific guidance in the materials on how to break down the barriers that hinder smooth data integration.

• Other information systems

In the 2023 survey, "securing talent" in data utilization emerged as a significantly prominent issue, overwhelming other items. The sense of shortage has increased dramatically from 45.5% in 2022 to 57.5% in 2023, becoming a critical bottleneck for many companies. In response to this situation, strategies that promote the establishment of a foundation requiring advanced specialized skills "without relying on personnel" are being sought. We will present how to build an automated foundation using external tools and AI. A "de-personalization" strategy to ensure data utilization continues even in the absence of experts will be made available in the materials.

• Other information systems

Due to the poor quality of data, there is a challenge in advancing its use on-site, and a clear improvement process is necessary. First, based on the purpose of use, we will "define" the standards, and then "measure" the current situation to determine the degree of improvement. After that, we will carry out "improvements" according to the content and apply them to operational data, cycling through the PDCA cycle. By continuing this process, we will finally obtain reliable data that can yield practical benefits for the business. For details on the improvement process that dramatically enhances data reliability, please refer to the materials.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

To maximize the results of AI utilization, it is essential to establish a structured "knowledge base" of internal knowledge. By integrating FAQs, manuals, and metadata into a common language that AI can reference, we can achieve advanced decision-making support. Additionally, by leveraging past experiences and adapting to unknown challenges through "meta-learning," we can enable highly applicable practical support. We will explain a system that eliminates dependency on individuals and fundamentally improves the overall business speed across the company. Please refer to the materials for insights on how to transform AI into an autonomous thinking partner through knowledge integration.

• Other information systems

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.

• Other information systems

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.

• Other information systems

The transition from traditional utilization requiring specialized knowledge to an intuitive operating environment using natural language has begun. With AI-powered chat-based data exploration, even non-engineers can actively access data. This significantly improves the speed of decision-making on the ground and promotes the acceleration of data-driven business improvement cycles. We will explain a new utilization concept where AI assists in creating sales data dashboards and extracting SQL. We will introduce the shock of an interactive UI that allows anyone to act like a data scientist through the materials.

• Other information systems

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.

• Other information systems

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.

• Other information systems

The accuracy of AI output is directly linked to the quality of the input data and the structure of the "connections." It is important to integrate not only structured data but also unstructured data such as PDFs and images, and to provide semantic context. Inaccurate data reduces reliability and can lead to locally optimal judgments. We will explain using a simple architecture diagram for AI utilization, from data lakes to DWH and data marts. The full scope of cross-sectional collaboration design to maximize the potential of AI will be published in the materials.

• Other information systems

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.

• Other information systems

NTP Corporation is a group of professionals whose mission is to design the future from the front lines of the field. We value a sense of ownership that goes beyond mere system development and deeply engages with the business environment. We lead the entire process from IT strategy planning to design, implementation, and subsequent operations, thoroughly eliminating the "translation loss" that often occurs between strategy and implementation. We have deep strengths in both manufacturing and IT, and we are committed to delivering tangible results. We provide detailed documentation on the support system by engineers who are thoroughly familiar with the manufacturing field.

• Other information systems

The representative, Ueno, has extensive experience in DX support both domestically and internationally, including launching new businesses at IBM and Yamaha Motor. In particular, his achievements in executing IT strategies aligned with business strategies on-site in Germany are at the core of NTP's strengths. With consistent experience leading projects as an architect, he bridges the gap between technology and business. He understands the unique challenges of the manufacturing industry and optimizes and provides a modern data stack that meets global standards for Japanese companies. Be sure to have the IT strategy guide specialized for the manufacturing industry, condensed with the representative's insights, at your fingertips.

• Other information systems

NTP's "AI READY Consulting" supports the necessary development of operations, data, and organization essential for effective AI utilization. We visualize the current situation, thoroughly identify "non-AI-Ready factors" such as individual dependence and data silos, and define the ideal state. From hearing business challenges to defining KPIs and creating use case maps based on priorities, we provide supportive, collaborative assistance. We strongly support the groundwork for AI to truly function, particularly in the manufacturing industry. We provide materials that explain the steps to diagnose whether your company is "AI-Ready."

• Other information systems

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.

• Other information systems

We developed a grand design for an IT strategy that considers the utilization of generative AI for clients in the telecommunications industry. Through interviews to understand current challenges, we identified areas where AI utilization is expected and conducted comprehensive consulting. Specifically, our support includes the establishment of AI governance, the creation of an environment, and training and educational activities to develop AI talent. This is an example that led to full-scale implementation, starting from "building the organizational foundation" for transformation rather than just introducing tools. You can learn how to formulate an AI strategy that involves the entire organization from actual project examples.

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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.

• Other information systems

A phased approach from STEP 1 to 3 is effective for the significant transformation of utilizing generative AI in business. We start with "building the foundation," such as establishing a CoE (Center of Excellence) organization, followed by conducting PoCs (Proof of Concepts) and training in specific areas. Ultimately, we progress to full-scale implementation, continuously creating new services, reducing costs, and identifying optimal use cases. We support companies in their trial and error processes, facilitating long-term and effective AI utilization. The document provides a detailed explanation of the "three steps" to avoid failure in full-scale AI implementation.

• Other information systems

The use of advanced AI requires an infrastructure configuration that incorporates robust monitoring and governance. We optimize a series of flows from data collection in Azure Data Factory, processing in Databricks, storage in SQL Server, to visualization in Power BI. Furthermore, by incorporating data governance through tools like Microsoft Purview and cost management, we enable safe and efficient operations. We strongly promote the dashboarding of business metrics by combining specialized technologies. You can check the standard configuration diagram of a modern data pipeline utilizing Azure in the provided materials.

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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.

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With the advent of generative AI, the "democratization of intelligence" is progressing, and we delve into the core of the IT strategies that companies should truly seek. The proliferation of generative AI has ushered in an era where advanced data utilization is possible even for non-experts. What is crucial in future IT strategies is to maximize the "Time-to-Value," the time from conception to value delivery. Breaking through stagnation in PoC (Proof of Concept) and how quickly business impact can be generated will determine a company's competitive advantage. Please make use of this document, which encompasses new AI strategies.

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We will thoroughly analyze the serious issue that many companies face, where AI implementation stops at the PoC stage, along with its contributing factors. Currently, about 70% of AI projects fail to deliver the expected results and remain in a "PoC death" state, unable to be implemented in actual business operations. This situation is rooted in a "lack of presence and division" between management, which prioritizes ROI, and engineers, who focus on feasibility. Additionally, deficiencies in data infrastructure and designs that do not consider company-wide deployment are causing stagnation in throughput. Please check the detailed materials now to discover the secrets to successful AI implementation.

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We will publish the definition of "FDE," a field-oriented promotion system that distinguishes itself from conventional system development (SI). FDE (Forward Deployed Engineer) refers to specialized personnel who deeply engage in the field, responsible for everything from identifying issues to implementation. Based on the "Palantir Model" demonstrated by Palantir Technologies, it is a method to bridge the gap between management and the field. By rapidly cycling through the processes of issue empathy, quick implementation, business integration, and effect measurement in the field, we create "truly usable" solutions that are not just theoretical. Please refer to the materials for a comprehensive overview of the innovative development model brought about by FDE.

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We will specifically explain the framework for infrastructure development that is essential for the successful implementation of AI. To achieve AI readiness, it is important to balance the three elements of "business, data, and organization." Specifically, it is necessary to simultaneously advance the standardization of processes (business), the integrated infrastructure using tools like Databricks (data), and the cultivation of a data utilization culture (organization). FDE strongly supports the integration of these three elements at the operational level, from infrastructure development before implementation to self-sustainability. We are currently offering a free guide for building an organization for AI utilization.

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