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31~60 item / All 111 items
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Invitation to a free assessment of the "Predictive Maintenance Roadmap" to be presented in 30 minutes.
How to fight with the assets we currently have? The first step to obtaining internal approval. Free explanatory materials provided.
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Trends in AI Utilization by Domestic Companies in 2024: Transition to Standard Infrastructure
[Free explanatory materials] About 50% are in the introduction or consideration phase. What are the guidelines to avoid falling behind now?
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The benefits of "on-site driven DX" that fosters a culture of voluntary improvement.
Create an organization that quickly reflects the voices of business users and actively engages in problem-solving.
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Data infrastructure construction: Databricks × Fivetran partnership
[Free Presentation of Explanatory Materials] Professionals Support the Latest Data Lakehouse Design and Implementation
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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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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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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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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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