DENCYU Inc. Implements 'Organizational Data Lake Feature' in DataBuddy AI

## DENCYU Inc. Launches 'Organizational Data Lake Feature' for DataBuddy

DataBuddy Slogan: Data becomes an asset with just a single conversation.

DENCYU Inc. (Headquarters: Fukuoka City, Fukuoka Prefecture; CEO: Takumi Tanaka) has launched the 'Organizational Data Lake Feature' for its conversational data utilization AI agent, 'DataBuddy', providing a shared data area for organizations. This feature consolidates business files previously scattered across departments such as sales, manufacturing, and administration, allowing for cross-departmental analysis through dialogue with AI.

### Background

In many workplaces, data is stored in individual folders or devices by department and staff, making it difficult to share and analyze across the company. Attempting to collect, analyze, and aggregate this data hits the following barriers:

- Organizational data is fragmented by department: Requiring applications to IT departments or transfers via email/Slack, with no means prepared for fields to bundle data across the entire organization.
- Data accessible to AI remains only in the user's history: Requiring re-uploading of raw data with every interaction, past analysis results cannot be handed over to other users or utilized across the organization.

To solve these issues, DataBuddy developed the 'Organizational Data Lake Feature' that allows data to be aggregated within a tenant.

### Overview

By simply uploading business files such as CSV or JSON to the tenant's shared area, the entire team can reference and analyze the same data via chat. File transfers between departments and re-uploading raw data for every interaction are unnecessary.

### Key Features

1. Tenant-wide Sharing: Files accessible by the whole organization
Files that were previously closed per task can be consolidated into a data area shareable within the tenant. With a simple chat instruction like 'Save to the data lake,' anyone can draw out the same data cross-sectionally. Re-uploading files every time becomes unnecessary.

2. Organized by Folder + Tag: Structured by department, fiscal year, and usage
Folders can be created by simple instructions like 'Create a folder for 2026 reports,' and items can be organized by department, fiscal year, and usage using tags. The field can create a data sharing environment as-is without complex operations.

3. Completion from storage to analysis through dialogue: AI directly accesses the data lake
Because the AI agent directly refers to the data lake, it responds on the spot to cross-sectional requests like 'Compare last month's orders with this month's inventory.' Storage, retrieval, and aggregation are completed within the chat.

### Customer Feedback

'We have multiple factories domestically, each managing production results in different Excel formats. Consequently, it was difficult to aggregate data in a usable form for cross-factory decision-making, and we had to request data individually from each factory whenever creating materials for management meetings. Since we started using DataBuddy to deposit files for each factory, we can instantly compare the results of multiple factories through dialogue with AI, significantly reducing the time spent on monthly aggregation. Reports that were previously manually compiled by staff at each location can now be created just by talking on DataBuddy, and management has praised it, saying, 'The necessary figures come out immediately.'' - Manager Y, Corporate Planning Department, Company O.

### Future Development

Moving forward, the company expects to strengthen integration with various external services such as Microsoft SharePoint and Google Drive, making information stored outside of DataBuddy also referenceable.

### What is DataBuddy?

DataBuddy is an AI agent that advances data utilization through Japanese-language dialogue.

1. Easy: Operations completed through chat only.
Users only need to 'ask.' No difficult setup or mastery of tool operations is required, enabling frontline staff to use it immediately. With almost zero learning cost, it realizes a culture of data utilization throughout the company.
2. Ready to use: Utilize existing data environments as they are.
Connects directly to existing databases, Excel, and CSVs. It can link with almost all corporate core and business systems that support ODBC, allowing existing data to be used as analysis assets immediately without building a new data foundation.
3. Extensive: Explore internal data across the board.
Since it can explore connected databases and files comprehensively, there is no need to remember 'where which information is.' DataBuddy supports the utilization of siloed business knowledge.