DataSelf AI+ | DataSelf Knowledge Base

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DataSelf MCP+ for Data Warehousing & Analytics

AI and Analytics Costs with DataSelf's Value Added

Introduction

AI is only as trustworthy as the data beneath it.

DataSelf AI+ combines DataSelf’s embedded AI capabilities with DataSelf MCP+ and leading large language models (LLMs). Together, they simplify and improve data preparation, reporting, and analysis for technical and business users while enabling new agentic AI possibilities.

DataSelf AI+ includes:

AI-assisted SQL and data modeling to help analysts address advanced reporting and analytics requirements
AI-assisted KPI creation to help executives and decision-makers define specialized business metrics
DataSelf MCP+ to connect curated, governed BI and data warehouse data with AI assistants, agents, and tools
Conversational analytics that enables business users to query data and generate insights using natural language
Agentic reporting and analytics that can monitor KPIs, identify anomalies, investigate contributing factors, generate recurring reports and summaries, recommend next steps, and initiate approved workflows

Why AI Needs Trusted Data

DataSelf AI+ combines conversational analytics with automated data warehousing, cleansing, optimization, and DFT+ (Dimension, Fact, and Time) modeling — enabling fast, governed, and curated data for accurate business insight.

AI hallucinations caused by fragmented transaction data such as from ERP, CRM, and payroll.
AI must sit on top of governed business data.
AI+ powers ETL+ and DFT+ to extract and model your data into consistent KPIs that feed insights.
By defining your SVOT (Single Version of the Truth) via SPOTs (Single Points of Truth) on your data warehouse, you guarantee curated, fast, low token-consumption data for BI and AI insights.

What Makes DataSelf AI+ Different

MCP platform supporting Claude, ChatGPT, Gemini, MS Copilot, Power BI, Tableau, and others.
No-code, low-code, and full-code customizations. You manage the business rules.

Capability	Generic AI	AI with DataSelf
Business Data Access	Reads disconnected documents, databases, and APIs	Unified, curated, governed data warehouse / data lake
Business Logic	Must infer business rules from prompts (potentially unreliable)	Standardized SVOT business rules powered by DFT+ and KPI+
Accuracy & Consistency	Responses may vary between prompts	Consistent, governed models, metrics and KPIs
Results foundation	Responses based on available context	Responses grounded in deterministic business rules
Business Context	Requires users to explain their business and underlying data	Built on ERP, CRM, accounting, and operational data models
Analytics Readiness	Requires prompt engineering and validation	Business-ready reports, KPIs, dashboards, and ad hoc analytics
Hallucination Risk	Higher	Lower
SMB focus	Weak	Strong
Performance	Depends on source systems and prompt complexity	Optimized analytical data warehouse for fast queries
AI Token Usage	Higher due to verbose prompts and schema discovery	Lower through curated models and business-friendly metadata
Security	Limited and time consuming	Enterprise-grade out-of-the-box
Scalability	Ad hoc and user dependent	Standardized, reusable analytics across the organization

Examples of AI-assisted Insights with DataSelf

Prompt 1: “Show sales by year from the DataSelf DW”

Prompt 2 (Click here to interact with the demo dashboard below):

“Create a dashboard with three coordinated visualizations from MCP+:
- Top-left chart: Sales by Year — Stacked Bar Chart. Display total sales by year, using one bar for each year. Segment each bar by Ship-To State and assign a consistent color to each state.
- Top-right chart: Sales by Ship-To State on a U.S. Map. Display total sales by Ship-To State on a U.S. map. Label each state with its state code and total sales. Use the same state colors assigned in the stacked bar chart.
- Bottom table/grid: Sales by Ship-To State by Year. Display Ship-To State on the rows and Year on the columns. Include totals for every row and column. Allow users to sort the table by clicking any column header.

Interactivity:
- Filter by state on the map: Clicking a state on the map filters the yearly bar chart to show only that state.
- Filter by year on the bar chart: Clicking a year’s bar filters the map to show sales for that year.
- Highlight active selections: Visually highlight the selected state or year on the visualizations.
- Filter by year dropdown: Add a multi-select year dropdown at the top of the dashboard.
- Download to Excel: Add a button to download the grid to Excel.”