NTP Product Lineup

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

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.

• Other information systems

We will introduce the process of overwhelming value creation brought by FDE through specific success stories from the manufacturing site. At a mobility vehicle manufacturing site, FDE was stationed on-site to integrate and analyze quality data. As a result, a visible achievement of a 15% reduction in defect rates was accomplished. By directly listening to the voices from the field and immediately reflecting the issues into the model, an on-site implementation approach enabled value creation in a short period. We will provide a detailed report that includes the success stories of a mobility company.

• Other information systems

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.

• Other information systems

We will introduce a practical case of FDE that ensures rapid product development and competitive advantage in overseas expansion. In our global new business, we established a system to dispatch FDE to the local area to engage in direct dialogue with customers. We developed an MVP (Minimum Viable Product) in just a few weeks, reducing the time to market by 20% compared to conventional methods. The ability to launch quickly is a strength of the FDE model, which allows for immediate reflection of on-site feedback. Please check the materials for the secrets of speedy new business development.

• Other information systems

We will detail the three-tiered support solutions provided by NTP, tailored to the phase of AI utilization in companies. We offer a roadmap that includes "1.0" for foundational development, "2.0" for rapid implementation through FDE, and "3.0" for self-sustainability through the establishment of an AI Center of Excellence. By consistently supporting the transformation of business, data, and organization at each phase, we help companies move beyond just proof of concept. In an era where AI technology is becoming commoditized, the true differentiators are "implementation capability" and "reduction of time-to-value." We are also offering guidance on the "AI-Ready Assessment" to help companies understand their current position.

• Other information systems

We will explain the strategic shift needed to break through the "barrier to AI utilization" that many Japanese companies are facing. Currently, the AI utilization efforts of many manufacturing companies are limited to the automation of routine tasks (Level 1.0). However, to build true competitive advantage, it is essential to transition to "Level 2.0" and beyond, where AI is integrated into core business activities such as decision-making and strategic planning. By steering towards proactive AI utilization, we will present specific steps to achieve sustainable business growth and revenue expansion. For more details on the maturity model of AI utilization that expands revenue, please download the materials and check them.

• Other information systems

Even if expensive SFA and CRM systems are implemented, we will unravel the fundamental reasons why they do not take root in the field and fail to deliver the expected results. The main factors behind the failure of traditional sales DX lie in three significant barriers: "input burden," "individualization," and "system fragmentation." The input tasks in the field become a burden, and the lack of data accumulation ultimately leads to a vicious cycle of relying on individual memory and spreadsheets. To achieve essential DX, it is necessary to redesign operations and systems in a way that does not create resistance in the field, even before selecting tools. Details on the "DX traps" that sales organizations are prone to and strategies to avoid them can be found in the downloadable materials.

• Other information systems

This introduction discusses the role of a "GTM Engineer," who integrates sales, marketing, and customer success through technology to design revenue systems. A GTM Engineer is not just a tool manager; they are a "revenue architect" who builds autonomous workflows using APIs and LLMs (large language models). The goal is to transform traditional labor-intensive sales styles into technology-driven approaches, achieving productivity equivalent to that of 100 people with just one individual. Elevating personalized sales processes into highly reproducible systems is key to accelerating growth in the manufacturing industry. The definition and role of GTM engineering aimed at maximizing revenue are explained in detail in the materials provided.

• Other information systems

We will explain an advanced design philosophy that derives optimal solutions by combining the best tools from each area without relying on a massive single system. In modern GTM engineering, CRM systems like Salesforce and HubSpot serve as data hubs, while the iPaaS "n8n" acts as the glue to connect various systems. For example, it is possible to implement autonomous workflows that automatically collect customer signals from external sources and generate personalized proposal emails using LLM. This composable design enables flexible scalability and rapid on-site support that adapts to changes in the business. Examples of automated workflows using advanced AI and API integrations can be found in the materials.

• Other information systems

In a specialized trading company in the chemical industry, we will detail a successful case of how AI was utilized to reduce the workload on-site and enhance sales efficiency. In this project, we first conducted a business assessment of all employees and quantified the reality that "non-core tasks," such as creating estimates and processing orders, accounted for approximately 80% of total work hours. By replacing these bottleneck administrative tasks with an AI agent, we created an environment where sales representatives could focus on deepening customer relationships and generating business opportunities. The key to success is to prioritize digital transformation (DX) in areas with high return on investment (ROI) based on numerical evidence rather than intuition. For detailed examples of specific AI applications that significantly reduced administrative burdens, please download the materials and take a look.

• Other information systems