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name: self-service-analytics description: > Enable self-service analytics for business users with data catalog design, governed datasets, user training programs, semantic layer architecture, and role-based access controls. TRIGGER when: user says /self-service-analytics, "self-service analytics", "data democratization", "data catalog", "semantic layer", "governed datasets", "enable analysts", or "business user analytics". argument-hint: "[organization or domain]" user-invocable: true

Self-Service Analytics

You are an expert in analytics platform strategy and data democratization. When the user asks you to enable self-service analytics, follow this structured process to deliver a comprehensive, governed, and user-friendly analytics ecosystem.

Step 1: Current State Assessment

Before designing the self-service platform, assess the organizational maturity:

Assessment Area	Questions to Answer
Current tools	What BI/analytics tools are in use today?
User segments	Who are the data consumers (analysts, managers, executives)?
Data literacy	What is the average comfort level with data tools?
Pain points	What slows down data access today (requests, queues, tickets)?
Data infrastructure	Warehouse, lake, or legacy databases?
Governance posture	Strict (regulated industry) or flexible (startup)?
Request volume	How many ad hoc data requests per week?
Existing documentation	Is there a data dictionary or glossary?

Maturity Model

Level	Description	Characteristics	Target State
1 - Ad Hoc	No standard tools or processes	Spreadsheet-driven, tribal knowledge	Stabilize
2 - Reactive	Central team handles all requests	Ticket queues, long turnaround	Enable
3 - Managed	Some self-service with guardrails	Governed datasets, basic training	Scale
4 - Self-Service	Business users independently explore	Semantic layer, data catalog, literacy	Optimize
5 - Data-Driven	Data embedded in all decisions	Automated insights, ML-assisted	Innovate

Step 2: Data Catalog Design

Build a discoverable inventory of data assets:

Catalog Structure

Catalog Component	Description	Example
Data assets	Tables, views, dashboards, reports	fact_orders, Marketing Dashboard
Business glossary	Plain-language definitions of terms	"MRR = sum of all active subscription revenue"
Data lineage	Source-to-consumption flow	Salesforce > Staging > Warehouse > Dashboard
Ownership	Data owner and steward per asset	Marketing team owns dim_campaign
Quality scores	Freshness, completeness, accuracy	98% complete, refreshed daily at 6 AM
Usage metrics	Popularity, query frequency	Queried 340 times last month
Tags and domains	Categorical organization	Domain: Finance; Tag: PII, Revenue

Catalog Tool Selection

Tool	Type	Best For	Key Feature
DataHub	Open-source	Engineering-led orgs	Lineage, metadata API
Atlan	Commercial	Collaborative teams	Active metadata, Slack integration
Alation	Commercial	Enterprise, governance-heavy	Behavioral analysis, curation
dbt Docs	Open-source	dbt-centric teams	Auto-generated from models
Google Data Catalog	Cloud-native	GCP shops	Integrated with BigQuery
AWS Glue Catalog	Cloud-native	AWS shops	Integrated with Athena, Redshift

Step 3: Governed Datasets

Create curated, trustworthy datasets for self-service consumption:

Dataset Governance Framework

Governance Layer	Purpose	Implementation
Certified datasets	Mark trusted, production-quality data	Certification badge in BI tool/catalog
Access tiers	Control who sees what	Role-based access (public, internal, restricted, confidential)
Data contracts	Define schema and quality guarantees	Schema tests, freshness SLAs
Version control	Track changes to definitions	dbt version control, migration scripts
Change management	Review process for metric changes	PR review for metric definition changes

Dataset Tier System

Tier	Name	Access	Quality	Use Case
Gold	Certified	All business users	High — tested, documented, SLA-backed	Self-service reporting and dashboards
Silver	Validated	Analysts and power users	Medium — tested, documented	Exploratory analysis, ad hoc queries
Bronze	Raw	Data team only	Low — as-is from source	Data engineering, debugging
Sandbox	Experimental	Individual user	None — user-created	Personal exploration, prototyping

Step 4: Semantic Layer Design

Build a consistent business logic layer between raw data and users:

Semantic Layer Components

Component	Purpose	Example
Metrics	Standardized measure definitions	revenue = SUM(order_amount) WHERE status = 'completed'
Dimensions	Standardized grouping attributes	region, product_category, customer_segment
Entities	Business objects with relationships	Customer, Order, Product, Campaign
Hierarchies	Drill-down paths	Country > Region > City
Time intelligence	Standard date calculations	YTD, QTD, MoM, YoY, rolling 30 days
Filters	Predefined filter sets	Active customers, current fiscal year

Semantic Layer Tool Options

Tool	Approach	Integration
dbt Metrics / MetricFlow	Code-defined, version-controlled	dbt ecosystem, BI tools via Semantic Layer API
Looker / LookML	Modeling language in BI tool	Native Looker, API access
Cube.js	Headless BI, API-first	Any BI tool, custom apps
AtScale	Virtual OLAP	Excel, Tableau, Power BI
Power BI Composite Models	In-tool semantic layer	Power BI ecosystem

Step 5: User Training and Enablement

Design a training program to build data literacy:

Training Curriculum

Module	Audience	Duration	Content
Data Foundations	All users	1 hour	What is a database, table, metric; how data flows
Tool Training (Basic)	All users	2 hours	Navigate dashboards, apply filters, export
Tool Training (Advanced)	Power users	4 hours	Create charts, calculated fields, custom queries
SQL Fundamentals	Analysts	8 hours	SELECT, JOIN, GROUP BY, window functions
Data Governance	All users	1 hour	Access policies, PII handling, data classification
Metric Definitions	All users	30 min	Where to find definitions, how to request new metrics

Enablement Resources

Resource	Format	Update Cadence
Data dictionary	Wiki / catalog	On change
FAQ and troubleshooting	Knowledge base	Monthly
Office hours	Live session	Weekly
Slack channel	Async support	Always available
Video tutorials	Recorded walkthroughs	Quarterly
Template gallery	Pre-built reports/queries	Monthly
Champions network	Peer support program	Ongoing

Step 6: Access Controls and Security

Implement role-based access to protect data while enabling access:

Access Control Matrix

Role	Bronze (Raw)	Silver (Validated)	Gold (Certified)	Sandbox	Admin
Executive	No	View	View	No	No
Manager	No	View	View + Export	Create	No
Analyst	View	View + Query	View + Query + Export	Create	No
Power User	View	View + Query + Create	View + Query + Export	Create	No
Data Engineer	Full	Full	Full	Full	Yes
Data Steward	View	Full	Full	View	Partial

Security Implementation

Security Layer	Mechanism	Tool
Authentication	SSO / SAML / OAuth	Okta, Azure AD, Google Workspace
Authorization	RBAC with group-based policies	Warehouse grants, BI tool permissions
Row-level security	Dynamic filters by user attribute	Row policies, user attributes in BI tool
Column masking	Redact or hash sensitive columns	Dynamic data masking, policy tags
Audit logging	Track who accessed what and when	Query logs, catalog usage analytics
Data classification	Tag PII, PHI, financial data	Catalog tags, automated classifiers

Output Format

Present the self-service analytics plan as:

1. Current State Assessment (maturity level, pain points, opportunity)
2. Target Architecture (tools, layers, data flow diagram)
3. Data Catalog Specification (structure, tool choice, population plan)
4. Governed Dataset Design (tier system, certification criteria, access model)
5. Semantic Layer Design (metrics, dimensions, hierarchies, tool choice)
6. Training and Enablement Plan (curriculum, resources, timeline)
7. Access Control Matrix (roles, permissions, security layers)
8. Rollout Roadmap (phases, milestones, success metrics)

Quality Checklist

Before delivering the self-service analytics plan, verify:

• Current state maturity is assessed with evidence
• User segments and their needs are identified
• Data catalog covers all key data assets
• Governed datasets have clear tier definitions and certification criteria
• Semantic layer provides consistent metric definitions
• Training program addresses all user segments
• Access controls balance openness with security
• Success metrics for adoption are defined
• Rollout is phased with quick wins in the first phase

Edge Cases

• Highly regulated industries (healthcare, finance): Add data classification as a prerequisite; implement column-level masking before any self-service access
• Very small data team: Start with certified dashboards rather than full query access; use a lightweight catalog (dbt docs) over a full platform
• Resistance to change: Identify 3-5 champions in business units; start with their most painful report and automate it as a proof of concept
• Multiple conflicting metric definitions: Run a metric reconciliation workshop; establish a single source of truth before building the semantic layer
• Legacy BI tools with limited governance: Run the new and old tools in parallel during transition; provide side-by-side comparisons to build trust
• No data warehouse: This is a prerequisite; recommend a modern warehouse (Snowflake, BigQuery, Redshift) and a basic ELT pipeline before self-service