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31～60 item / All 73 items
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The true nature of "PoC death" that hinders DX promotion: Free explanatory materials provided.
Why do about 70% of AI projects fail to reach the field and come to a standstill?
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IT Strategy for Building Competitive Advantage in the AI Era: Free Presentation Material Offered
After 2026, the success or failure of a business will be determined by the "speed of value delivery."
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Data utilization consultation desk: Leading to solutions for challenges in manufacturing industry DX.
[Free explanatory materials] From inventorying existing data to AI implementation, feel free to consult with us first.
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Integrated Architecture of Azure Environment: Monitor & Govern
[Free Presentation of Explanation Materials] A Consistent Flow from Azure Data Factory to Power BI
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Three Steps to Full-scale Implementation of Generative AI: From CoE to Identifying Use Cases
It won't change overnight. Support for a continuous approach as an organization.
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Example: Introduction of Azure Databricks in the manufacturing industry
[Free Distribution of Explanatory Materials] Automating Failure Prediction and Condition-Based Maintenance Using IoT Data
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Example: Formulation of a Grand Design for IT Strategy for the Utilization of Communication AI
IT strategy formulation centered on the utilization of generative AI, from issue identification to roadmap creation.
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Data infrastructure construction: Databricks × Fivetran partnership
[Free Presentation of Explanatory Materials] Professionals Support the Latest Data Lakehouse Design and Implementation
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AI READY Consulting: Extracting and Eliminating Non-AI-Ready Factors
Eliminate personalization and data fragmentation, and establish an ideal state for optimal AI utilization.
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Representative Kyo Ueno discusses IT strategies for the manufacturing industry from a global perspective.
[Free Presentation of Explanatory Materials] How to Create Winning IT Based on Achievements at IBM and Yamaha Motor Co.
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A professional group in manufacturing industry DX: "Frontline" design by NTP Corporation.
Eliminate "translation loss" between strategy and implementation. Committed to business growth through collaborative engineering.
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The first step of AI transformation: Inventory of existing data infrastructure and understanding the current situation.
[Free explanatory materials] The source of competitiveness lies not in the number of AI implementations, but in the "quality of data."
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Integration of Structured and Unstructured Data: Collaborative Design Key to AI Accuracy
PDFs, emails, and images are also assets. How to advance the organization of accurate and unified data.
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Conditions for the next-generation data infrastructure: real-time capability and semantic connectivity.
[Free Presentation of Explanatory Materials] Enables interactive collaboration with AI, achieving high-accuracy output.
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Establishment of Data Governance Basic Policy: The Foundation for Safe AI Implementation
From access control to log management. Design guidelines to minimize information leakage risks.
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Conversational Interface: Intuitive Data Exploration through Natural Language
[Free Presentation of Explanatory Materials] Execute queries in chat format. Encourage active use by non-engineers.
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Semantic Layer: Preventing discrepancies in data definitions between departments.
No need for specialized SQL. Building an environment where you can directly access data using business terminology.
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Selection of Metadata Management Tools: From Databricks to AWS
[Free explanatory materials] Prevent the obsolescence of manual management and automatically eliminate information silos.
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Meta-learning and Knowledge Repository: Maximizing the Outcomes of AI Utilization
Systematize internal knowledge and integrate it as a "common language" that AI can reference instantly.
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Differentiating between business metadata and technical metadata.
[Free Presentation of Explanatory Materials] Clearly define the purpose and structure. Support the consistency between systems with technology.
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Metadata Management: The Core of Information that Unleashes the True Value of Data Utilization
Organize 'data about data' to ensure reliability and correctness of interpretation.
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Benefits of using ETL tools: Data integration and reduction of operational burden.
Free presentation of explanatory materials: Integrating data from multiple systems with different formats consistently.
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The Importance of Data Modeling: Guidelines to Prevent Inconsistencies in Advance
Not just ending with mere "accumulation." Building a foundation for advanced utilization directly linked to business.
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Rapid evolution of data infrastructure through agile development.
[Free Presentation of Explanatory Materials] Repeated improvements in a short cycle, responding promptly to changes in needs.
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Resolution of User vs. Administrator Conflict: A Management System that Meets Both Parties' Needs
Bridging the gap between the field that wants to analyze freely and the information system that wants centralized management.
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Limits of Manual Operations: Complexity of Data Structures and Maintenance Burden
[Free Provision of Explanatory Materials] Transition to automatic updates using external tools in response to the vast amount of data.
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Four Steps to Data Quality Improvement: Definition, Measurement, Improvement, Application
Transforming data "garbage" into value: Quality management techniques using the PDCA cycle.
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Accelerating shortage of data talent: A serious concern for 57.5% of companies.
[Free explanatory materials] "Securing talent" is the biggest bottleneck. The next step you should take immediately.
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Two major factors hindering data integration: unstructured data and undeveloped rules.
Graduating from 'Having data but not being able to use it': How to advance strategic collaboration design.
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The Trap of Data Silos: Disadvantages Faced by Field-Centric DX
[Free Distribution of Explanatory Materials] The Necessity of a "Common Foundation" to Prevent the Proliferation of Systems and Increase in Management Costs
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