NTP Product Lineup

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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."

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.

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.

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.

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.

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.

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.

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.