Governance
Agreed definitions and owners, so the AI calculates "revenue" or "active customer" the way the business does.
Data Management
Faster, more accurate decisions start with well-managed data
AI can answer business questions in minutes, if the data behind it is accurate and well defined. We make your data ready for that.
Common Signs
When data management needs attention
- Reports disagree across teams for the same metric.
- Definitions vary depending on who you ask.
- Analysts spend more time preparing data than analyzing it.
- AI answers can't be trusted without a manual recheck.
63%
of organizations lack, or aren't sure they have, the right data management practices for AI.
Source: Gartner, February 2025Data Management and AI
AI answers are only as good as the data behind them
AI assistants and analytics tools let anyone ask a question in plain language and get a chart or answer back in seconds. Whether that answer is right depends on things the AI can't work out on its own: which metric definition to use, which tables are trusted, whether the numbers are current and complete, and who is allowed to see what. Data management supplies exactly that context.
AI on unmanaged data
- A leader asks for last quarter's revenue by region
- An analyst asks an AI assistant to write the query
- The AI picks a plausible table and its own definition of revenue
- The analyst compares the result with finance's report and finds a gap
- Several rounds of re-prompting and manual reconciliation follow
Turnaround: days, and the answer is still questioned in the meeting
AI on managed data
- A leader asks for last quarter's revenue by region
- The AI assistant uses the certified revenue definition and trusted tables
- The answer comes back with its source and calculation
- The analyst spot-checks it instead of rebuilding it
- It matches finance's numbers because both use the same definition
Turnaround: minutes to hours, and the discussion moves to what to do about it
+17–23pts
In an April 2026 benchmark of 100 business questions, adding a short semantic layer (documented metric definitions and table relationships) raised answer accuracy for Claude Opus 4.7, Claude Sonnet 4.6 and GPT-5.4 from 46–51% to about 68%. The authors found that having the semantic layer mattered more than which model was used.
Source: Cube, Semantic Layer Benchmark, April 2026. A separate dbt Labs benchmark the same month found similar gains: dbt Labs, April 2026.What each part of data management gives your AI
Data quality
Accurate, complete and current inputs, so answers can be acted on without a manual recheck.
Metadata & catalog
Descriptions and lineage that help the AI choose the right tables and show where each number came from.
Certified metrics
One shared set of metric calculations used by dashboards, spreadsheets and AI tools alike.
Access & security
Permissions that carry through, so each person's AI assistant only sees data they're allowed to see.
Ownership & issue resolution
A clear path to fix problems at the source, so the same error doesn't resurface in every answer.
Services
Where we can help
Most clients start with the assessment.
01
Assessment & Roadmap
Know what to fix first, and why.
- Review of your data, systems and key reports
- Readiness check for your priority AI and reporting use cases
- A prioritized 12-month roadmap
2–4 weeksAsk about this
02
Data Governance
One agreed definition for every key metric, with a named owner.
- Data owners and stewards for each area
- Glossary of key metrics and business terms
- A simple process for approving data changes
8–12 weeksAsk about this
03
Data Quality
Catch errors before they reach reports or AI answers.
- Quality rules for your most important data
- Automated monitoring and alerts
- A process for assigning and fixing issues at the source
6–10 weeksAsk about this
04
Data Catalog & Semantic Layer
Give people and AI the context to use the right data.
- Catalog of priority data with definitions and lineage
- Certified metric definitions shared by BI and AI tools
- Optional: an AI assistant connected to your governed data
8–12 weeksAsk about this
Approach
How an engagement runs
Weeks 1–4
Assess
Goals, critical data and priorities.
Weeks 3–8
Design
Roles, standards and processes sized to you.
Weeks 6–16
Implement
One priority area first, then expand.
Ongoing
Support
Coach your team until they run it.
Deliverables
What you receive
Depending on the services you choose, typical deliverables include:
- Current-state assessment and 12-month roadmap
- Governance charter and RACI (who is responsible, accountable, consulted, informed)
- Named data owners and stewards with defined responsibilities
- Business glossary for your key metrics and terms
- Certified metric definitions that BI and AI tools can use directly
- Data policies and standards
- Data quality rules, thresholds and a monitoring dashboard
- Data catalog and semantic layer for priority data sets
- Training materials and runbooks for your team
Engagement Models
Ways to work with us
Project-based
Fixed scope and deliverables.
Retainer
Ongoing support, set days each month.
Fractional data lead
A part-time data lead on your team.
Questions
Common questions
Do we need to buy a data governance tool first?
No. We work with the platforms you already have and help you decide if you need more.
How soon will we see results?
Usually within 6 to 8 weeks, starting with one priority data set.
Can you help with master data or data architecture?
Yes, as follow-on work when the assessment shows they're a priority.
Can you help us use AI on top of our data?
Yes. We prepare the data for your priority questions, then connect an AI assistant through our AI services.
Related
AI Automation & Implementation
From AI use case to daily use.
Get Started
Start with a 30-minute conversation
Tell us what isn't working. You'll leave with clear next steps.