納品物
- Data source audit and cleanup plan
- Data model, transformations, and quality checks
- Dashboard or reporting interface
- Optional natural-language data assistant
- Documentation, training, and maintenance recommendations
分析・DATA AI
データインテリジェンス
Turn scattered data into useful decision support: clean datasets, dashboards, automated reporting, and private AI analytics.
想定する購入者
Executives, finance, sales, operations, and product teams working from spreadsheets, inconsistent KPIs, manual reports, or disconnected databases.
主要指標
- Less spreadsheet work
- 50-80%
- Source of truth
- 1
- Decision visibility
- Real-time
Target reduction when manual reporting is replaced by cleaned pipelines and dashboards.
Unify definitions so teams stop arguing over conflicting KPI logic.
Dashboards and alerts can expose changes faster than monthly manual reports.
アナリティクス・コマンドセンター
ライブ売上
+24.6%50-80%
アクティブユーザー
+12.1%1
パイプライン総額
+8.4%Real-time
異常検知
−2.3%7
Q1 → Q4 · weekly
auto-refresh前四半期の売上を牽引した上位3つの要因は何ですか？
回答
法人顧客のリピート率が38%上昇
ソース: orders.csv · finance.xlsx
新しいプレミアムプランが総売上の22%を構成
ソース: stripe · hubspot
ウランバートル圏の平均注文単価が14%上昇
ソース: postgres · sheets
接続中のデータソース
PostgreSQLSupabaseSheetsFormsCRMERP
ライブストリーム
最終同期 · 12分前概要
価格帯
$3,000 - $100,000+
期間
6-20 weeks
契約形態
Data audit, dashboard MVP, or private data AI system
最適な対象
乱雑なスプレッドシートや重複データを持つチーム
ダッシュボードとKPIが必要な企業
プライベート分析アシスタントを検討する組織
納品内容
スコープ、成果、技術スタック
事業成果
- Create trusted datasets from messy files, forms, databases, and business tools.
- Build dashboards for leadership, operations, finance, sales, or product teams.
- Add AI question-answering over business data when privacy and structure allow it.
- Make decision-making faster with clearer KPIs, alerts, and trend explanations.
技術
- PostgreSQL
- Supabase
- Python
- Pandas
- Vector databases
- Next.js
- BI dashboards
検討ポイント
- Data quality and access are the main drivers of timeline and risk.
- Private RAG and natural-language analytics require careful permission and source controls.
- A data audit is often the safest first step before committing to a large analytics build.
構築内容
明確なスコープ
Executives, finance, sales, operations, and product teams working from spreadsheets, inconsistent KPIs, manual reports, or disconnected databases.
01
Data cleanup
Normalize, deduplicate, validate, and document messy spreadsheets, forms, exports, and database records.
02
Executive dashboards
Revenue, operations, finance, customer, and team performance dashboards built for scanning and decisions.
03
Private Data AI
Secure AI assistants that answer questions from approved company data without exposing sensitive information.
04
Automated reporting
Pipelines that replace recurring Excel, PowerPoint, and manual report assembly.
Try data system estimation calculator
Estimate your data-to-decision layer
Scope data sources, dashboard depth, private AI, governance, and reporting automation.
What data intelligence system do you need?
Connected data sources
4
115
Useful add-ons
Timeline
Oyu の強み
選ばれる理由
Foundation before AI
We clean metrics, ownership, and source logic before adding natural-language analytics.
Clear access rules
Roles, source traceability, freshness indicators, and audit-friendly logic help teams trust the data.
Automation beyond dashboards
Insights can trigger alerts, approval workflows, Digital Employee tasks, or operating reports.
Business-readable UX
Dashboards are designed for daily use with clear hierarchy, drill-downs, and role-specific views.
リスク解消
解決する課題
Conflicting spreadsheets
Metric definitions and reusable data models create one trusted interpretation.
Sensitive data in public AI
Private AI patterns keep approved data sources and access rules under control.
Dashboards that do not answer decisions
Discovery starts from business questions, not chart decoration.
AI project without foundation
Data audit and validation reduce failure before advanced analytics are added.
デリバリーシステム
実装フロー
各モジュールは単発の納品物ではなく、業務ワークフローに組み込むシステムとして設計します。
01
Audit
Map sources, owners, reporting pain points, KPI conflicts, and decision questions.
02
Model
Define trusted metrics, dashboard structure, data ownership, and access levels.
03
Clean
Normalize data, remove duplicate logic, connect systems, and prepare reusable models.
04
Build
Create dashboards, automated reports, alerts, and private analytics assistant surfaces.
05
Validate
Compare outputs against known reports, train users, and expand once trust is established.
次のステップ
このモジュールを業務に合わせて計画します
Oyu Intelligence does not just make dashboards. We build decision infrastructure that makes data trustworthy, searchable, secure, and useful every day.
質問
よくある質問
価格帯、期間、モンゴル・日本市場向けの進め方についての基本情報です。
データインテリジェンスの開始価格はいくらですか？
データインテリジェンスはFrom $3,000から開始できます。全体の目安はスコープ、連携、期間により$3,000 - $100,000+です。
データインテリジェンスの開発期間はどのくらいですか？
通常の期間は6-20 weeksです。ディスカバリー、設計、実装、テスト、公開サポートを含めて進めます。
モンゴルと日本市場の要件に対応できますか？
はい。Oyu Intelligenceはモンゴル語、英語、日本語の文脈で、地域ごとの言語、運用、支払い、納品期待に合わせて対応します。