Ready your data for AI and whatever comes next
Your data is one of the most valuable assets in your business. ReadyMyData helps you define it, document it and prepare it properly so it can be used with dashboards, reports, AI tools and whatever comes next.
The real asset
Dashboards change. AI tools change. Your data is the asset.
Reports, dashboards, spreadsheets and AI tools are all ways of using your data. But the data underneath is what has lasting value. When your data is clearly defined, well-documented and trusted, you can use it in many places — and you are not locked into any single tool, vendor or consultant.
Define it once
Clear names, definitions, metric rules and ownership. When your data means the same thing to everyone, it works in every tool.
Use it anywhere
The same prepared data can power dashboards, reports, AI assistants, chatbots, copilots, custom apps and future systems you have not chosen yet.
Own it properly
When your data is documented and defined, your business owns the knowledge — not the consultant who built the report or the vendor who hosts the tool.
Why AI needs your help
Before you can talk to your data, your data needs to be ready
AI does not magically understand your business. It needs clear field names, plain-English definitions, examples and business rules written down. If your data is messy or unclear, AI tools will guess — and plausible-sounding wrong answers are worse than no answers at all.
ReadyMyData helps you prepare the foundation so that people and AI can understand your data in the same way. Build it once — then use it with dashboards, AI assistants, copilots, consultants and future tools.
Clear names
AI reads field names literally. "Monthly Customer Revenue" is useful. "FACT_cust_rev_MTD" is not. Naming is the foundation of reliable AI results.
Business context
AI does not know your rules, exceptions or business logic. That context needs to be written down before AI can give answers your team can trust.
Consistent definitions
If "Active Customer" means three different things in three different reports, AI will pick one — probably the wrong one. Agree the definition first.
The problem
Why most business data is not ready to use
ReadyMyData is designed to help you fix each of these.
Scattered data nobody owns
Data lives across systems, spreadsheets, reports, SaaS tools and people's heads — with no single agreed source of truth.
Undefined metrics and rules
Different teams use different definitions of the same KPI. Nobody agrees on what the number actually means until after the report is built.
Names AI cannot understand
Field names like FACT_cust_rev_MTD confuse business users and cause AI tools to guess — often incorrectly.
Locked into one tool or vendor
When you switch dashboards, add an AI tool or change consultants, you restart the scoping process from scratch because nothing is documented.
No clear brief for builders
Data teams, developers and consultants receive vague requests with no agreed metrics, no data source map and no success criteria.
Repeated work every project
The same naming standards, requirements docs and definitions are recreated from scratch on every project, every time.
The tools
Three tools. One readiness workflow.
AI Readiness Scanner
Check whether your data asset names, measures, descriptions and structure are clear enough for people and AI to understand. Starting with Power BI-style metadata — designed to expand.
- Readiness score across four dimensions
- Specific rename suggestions with reasons
- AI-ready field descriptions and synonyms
- Prioritised action plan
Requirements Gathering
A guided process for capturing what your business needs to understand from its data — turning vague requests into a structured, AI-ready data asset specification. Helps business users explain what they need and gives data teams a clearer brief to build from.
- Structured stakeholder question flow
- KPI definition capture and validation
- Data source and audience mapping
- Sign-off ready requirements output
AI-Ready Delivery Pack
Generate the complete set of artefacts needed to deliver and future-proof a data project — business brief, metric definitions, data source checklist, semantic model guidance, build tasks, UAT criteria and sign-off documentation.
- Business brief and questions the data should answer
- KPI definitions and data source checklist
- Semantic model guidance and dashboard plan
- UAT checklist and sign-off document
What you get
The AI-Ready Data Delivery Pack
A practical pack that explains what your business needs, what questions the data should answer, what the key metrics mean, where the data comes from, what needs to be built, how it should be tested and how to make it ready for AI.
Prepare it once. Use the same definitions and data context across dashboards, reports, AI tools, consultants and future systems.
Who it's for
For everyone involved in data project delivery
Business owners and managers
Explain what you need from your data — without needing to know how it is built.
Finance and operations teams
Define the metrics and reports that matter before asking IT or a data team to build them.
BI and data consultants
Deliver to clients with a repeatable, professional process and clear documentation every time.
BI developers and analysts
Build from clear specifications and stop working from vague briefs or rebuilding artefacts from scratch.
Data and analytics teams
Get data assets that are clearly defined, consistently named and ready for AI tools and business users alike.
Project managers
Commission and sign off data projects with confidence — clear scope, defined outputs, agreed criteria.
Expert help
Need hands-on support?
The tools automate what can be automated. When you need expert judgment on preparing your data properly, reviewing a data model, turning business goals into a build-ready brief, or facilitating a requirements workshop — the manual services are the right next step.
AI-Ready Data Project Review
Expert review of your data project requirements, semantic model or data asset with a written improvement plan.
Requirements discovery workshop
Facilitated session to turn business goals and questions into clear KPIs, data sources and a structured project brief.
Dashboard and metric audit
Review of existing dashboards, metric definitions and data asset quality with clear recommendations.
Semantic model review and cleanup
Field naming, descriptions, synonyms and display folder structure applied directly to your data model.
Ready to prepare your data?
Start by checking if your existing data assets are AI-ready, or begin turning your business questions into a signed-off delivery pack. No sign-up required for either.