Technology, data and AI. Chief Data Officer
AI for the Data Office
Every AI plan starts with one question. Is the data ready?
The answer is usually no. It’s incomplete, scattered across systems, locked in documents or defined three different ways. Your team spends its time firefighting while the business waits. We map the Data Office the way your team actually works, then put agents where they make data trusted, findable and ready for AI.
The short answer
It takes on the volume work that keeps data usable. Agents check and fix data quality, extract facts from documents, describe and tag every data set, handle access requests within policy and catch broken pipelines before the business notices.
Your team keeps the architecture, the definitions and the ownership. The agents do the checking and the documenting, so data becomes ready for AI where it matters first.
The mandate
Make data trusted, findable and ready to use. By people and by agents.
What the board measures you on
Data quality
For the data that matters most
Time to data
From request to usable
Data products in use
Not just built
AI readiness
Data ready for priority use cases
Data incidents
Breaks, leaks and errors
Compliance
GDPR, the EU Data Act and sector rules
The canvas of the Data Office
Most data AI is a smarter dashboard. The bigger value sits in the quality, documentation and access work that decides whether AI can use the data at all.
Canvas 1
What you run
The data the company depends on.
Data platform
Lakes, warehouses and pipelines, with the CIO.
Data products
Shared data sets the business can trust and reuse.
Data quality
Checks, fixes and ownership for critical data.
Catalogue and metadata
What data exists, what it means and where it lives.
Unstructured data
Documents, emails and files that hold the company’s knowledge.
Canvas 2
What you answer for
The things the board, regulators and every other office hold you to.
Data governance
Ownership, definitions, lineage and standards.
Privacy and access
Who can see what, under GDPR and your own policies.
One version of the truth
Shared definitions for revenue, customer and every key metric.
Data for AI
Training, grounding and evaluation data that meets the rules.
Where value leaks
Each one quietly blocks a report, an AI use case or a decision somewhere else in the company.
01
Data not ready for AI
The use case is approved. The data is incomplete or wrong.
Quality agents. Profile data every day, fix issues at source where they can and route the rest to an owner.
Data quality score
Use cases unblocked
02
Knowledge locked in documents
Contracts, reports and emails that no system can read.
Extraction agents. Read documents, pull out the facts and store them as structured data linked to your systems.
Documents usable by agents
Hours saved per search
03
Nobody can find the data
Analysts ask around, then build their own copy.
Catalogue agents. Describe, tag and link every data set so people and agents can find it.
Catalogue coverage
Time to find data
04
Access that takes weeks
Every request waits in a queue for a manual check.
Access agents. Check each request against policy, grant what the rules allow and route the rest for approval.
Time to access
Requests over SLA
05
Many versions of the truth
Revenue means one thing in finance and another in sales.
Definition agents. Compare how key metrics are calculated across reports and flag the conflicts to data owners.
Conflicting metrics
Certified reports
06
Pipelines that break silently
The dashboard looks fine. The data behind it stopped updating.
Observability agents. Watch freshness, volume and schema changes and alert the owner before the business notices.
Data incidents
Time to detect
Process lifecycles
We start from the platforms and tools you already run. The orange steps are where an agent takes on the work, so your team spends its time on design and ownership.
Data lifecycle
Every data source
Source
Ingest
Documents included
Agent
Quality
Checked every day
Agent
Catalogue
Described and tagged
Agent
Serve
Typical systems. Databricks, Snowflake, Microsoft Fabric, dbt, Informatica
Data product
Every shared data set
Request
Design
Build
Document
Drafted from the build
Agent
Publish
Measure use
Who uses it and how
Agent
Typical systems. Data platform, Collibra, Alation, BI tools
Access request
Every request
Request
Policy check
Purpose and sensitivity
Agent
Approve
Provision
Within the rules
Agent
Review
Unused access removed
Agent
Typical systems. Immuta, identity tools, ITSM, catalogue
Data incident
When something breaks
Detect
Freshness, volume, schema
Agent
Assess
Reports and agents affected
Agent
Fix
Root cause
Patterns across incidents
Agent
Notify
Typical systems. Monte Carlo, data observability tools, ITSM
Agent intervention point
The Data control tower
Not a data quality report that nobody reads. A live view of what’s ready, what’s broken and what’s blocking the next AI use case.
Illustrative
Data control tower
Sample data to show the view. Not client results.
Data quality
87%
▲ 6 pts
Time to data
3 days
▼ from 19
Products in use
34
▲ 8 this quarter
Docs usable
61%
▲ from 12%
Open incidents
5
1 affects finance
Over SLA
12
Access requests
This week’s data brief
Blocked
Two AI use cases waiting on customer address quality
18% of records incomplete. Fixes proposed at source
Broken
Sales pipeline stopped updating 2 days ago
Three dashboards and one agent affected. Owner alerted
Conflict
Net revenue calculated four ways across eleven reports
Definition comparison sent to the CFO Office
Unlocked
12,400 supplier contracts now searchable as data
Extraction agent. Ready for the Procurement Office
Data quality by domain
Finance
94%
Customer
82%
Product
88%
Supplier
72%
Supplier data lags. Two planned agents in the Procurement Office depend on it.
Illustrative example. Figures are invented to show how the view works. In discovery we build it on your own data and KPIs.
Your industry changes the agenda
The canvas stays the same. Where the data lives and what it’s needed for does not. Here’s the question we hear most from data leaders in each industry, and where agents help first.
CPG
How do we use retailer and distributor data that arrives in a hundred formats?
Partner data extraction and quality
FMCG
How do we get one product master across many markets?
Product data matching and quality
Retail
How do we build one view of the customer across store and online?
Customer data matching and access rules
D2C
How do we keep data clean as the SaaS stack grows?
Pipeline observability and catalogue
Automotive
How do we use vehicle, dealer and warranty data together?
Data products across business units
Healthcare
How do we use clinical and admin data within strict privacy rules?
Access agents and data classification
Energy
How do we bring asset, sensor and customer data together?
Data quality across IT and OT sources
Manufacturing
How do we use decades of specs, reports and drawings?
Document extraction and search
Logistics
How do we trust shipment data from hundreds of partners?
Partner data quality and observability
BFSI
How do we meet data lineage rules and use data for AI at the same time?
Lineage, definitions and access control
B2B technology
How do we turn product usage data into something every team can use?
Data products and catalogue
How the Data Office connects
Every AI use case starts with data, and every office depends on it. The agents that serve the Data Office share the same registry and controls as the offices that use the data, so quality problems get fixed once, at source.
How we’d start
Pilot 1
Data ready for your top three AI use cases
↳
The data behind three priority use cases checked for quality, freshness and access
↳
Fixes made at source where possible, the rest routed to owners
↳
Every data set documented and added to the catalogue
Your decision
Whether quality agents run on every critical data domain.
Pilot 2
One document archive, unlocked
↳
Contracts, reports or forms from one archive read by extraction agents
↳
Key facts stored as structured, searchable data
↳
Accuracy checked against a sample reviewed by your team
Your decision
Which archives to unlock next.
Both run under our Validation First Framework. They’re tested on your own data before you commit a larger budget, and if the numbers don’t hold, we tell you.
Your intervention map
Bring your data map, your quality scores and the AI use case that’s waiting on data. We bring the Data Office canvas. In one session we map it to your platforms and owners, and mark where agents would pay back first.
01
Your Data Office canvas
What you run and answer for, checked against how your data teams, owners and platforms really work.
02
Your value chain
The canvas placed on your industry and company, showing which data each part of the business depends on.
03
Your intervention points
Each leak with an agent design, its controls and the KPI it moves. Delivered as a mind map, an architecture view or an interactive dashboard.
AI for the Data Office, answered
What can AI do for the Data Office?
Agentic AI takes on the volume work that keeps data usable. Agents check and fix data quality, extract facts from documents, describe and tag every data set, handle access requests within policy and catch broken pipelines early. Data teams spend less time firefighting and more time building data products the business uses.
How do you make data ready for AI?
Start from the use case, not the whole estate. For each priority AI use case, agents check the data it needs for completeness, accuracy, freshness and access, fix what they can at source and flag the rest with an owner. Data becomes ready where it matters first.
Can AI improve data quality?
Yes. Quality agents profile data continuously, spot duplicates, gaps and odd values, suggest fixes and route the rest to data owners. Because they run every day rather than in a yearly clean up, quality improves and stays improved.
How can we use unstructured data like documents and emails?
Extraction agents read contracts, reports, forms and emails, pull out the facts that matter and store them as structured, searchable data linked to your systems. Much of a company’s knowledge lives in documents, so this is often where AI finds the most value.
Do we need a new data platform for AI?
Usually not. Agents work with Databricks, Snowflake, Microsoft Fabric, your catalogue and your existing pipelines. The gaps are rarely the platform. They’re quality, documentation, access and ownership, which agents help close.
Where should a Chief Data Officer start with agentic AI?
With the data behind your top three AI use cases. Check it, fix it and document it with agents, so those use cases can reach production. Then add document extraction on one archive that holds knowledge the business needs.
