Data and Analytics Capability Matrix: AI-Ready Edition

The Data and Analytics Capability Matrix describes the work of a data function as 192 Level 3 capabilities, each defined, under 48 Level 2 capabilities and 8 Level 1 areas. Data offices at banks, insurers, asset and wealth managers and payments firms use it to agree on what their function does before they choose where AI goes. Each Level 2 capability has an AI profile with a target AI mode, an oversight level and a risk tier. A scoring model ranks all 48 on weights you set, and a 40-question assessment shows which gaps would slow the first AI projects. The content is cross-industry.

The Problem Behind It

Almost every team in a financial firm depends on the data office. Risk, finance, operations and customer teams each read governed data, and each tends to describe what it needs in the names of the systems it reads from. When budgets are set, their requests overlap and use different words for the same work, and a steering group has no common list to compare them against.

The matrix supplies that list, and the AI ratings make it a basis for decisions. Profiling, matching, lineage capture and querying in plain language now appear as AI features in many data products, and each feature asks how much the tool may do without review. With a profile on every Level 2 capability, the data office records that answer once per capability and applies it to each tool that touches the work.

What it is

Inside the Data and Analytics Capability Matrix

The matrix is a reference description of data and analytics work in business terms, organized in three levels. Governance, quality, integration, analytics and business intelligence sit in one hierarchy of 8 Level 1 areas, 48 Level 2 capabilities and 192 Level 3 capabilities. Each capability has a written definition, and the Level 2 capabilities carry 193 KPIs between them.

IDs stay fixed from one release to the next. Data quality issue resolution is DA-6.3, for example. A data office can tag its change requests, its budget lines and its stewardship assignments with these IDs and then read all three against one list.

Two levels carry the AI ratings. A Level 2 profile states where AI can take on part of the work, which decisions a person keeps, which agents and data are involved, what has to be in place first, what can go wrong and how roles change. A Level 3 entry rates work pattern, target AI mode, AI fit and risk tier. The sample scores DA-6.3 at 3 for AI fit, 5 for value, 3 for feasibility and 2 for data readiness, places it in Wave 2, and leaves every correction of data of record with the Data Owner.

The deliverables

What the Download Contains

The download holds the matrix, the AI ratings and the tools that turn them into a ranking:

The model

• 8 L1, 48 L2 and 192 L3 data and analytics capabilities, defined through L3, with 193 KPIs set at L2

AI and governance

• For each L2 capability, an AI profile covering opportunities, what stays human, agents, data, KPIs, AI measures, prerequisites, risks and workforce impact
• L3 capabilities rated for work pattern, target AI mode, AI fit and risk tier
• Risk-adjusted scoring and ranking of each L2 capability, driven by your own ratings and weights
• An AI heatmap across the entire model
• A weighted AI readiness assessment with 40 questions

Files and terms

• To begin, a Welcome document; then the capability map deck, standalone capability definitions and KPIs documents, the Excel workbook, the user guide
• Bonus files included: a data and analytics glossary, A Practical Guide to Business Architecture, a capability modeling overview, an AI-ready capability profile template and capability relationship mapping templates
• Corrections free of charge, and new versions free for 180 days after purchase

Your organization's name and license ID are printed in every file. Ratings reflect a typical large enterprise; treat them as a first draft of your own.

Putting it to work

Use Cases

Tracing a key report to its capabilities. Pick a report that goes to the executive committee and list the Level 3 capabilities it depends on, from data quality to reporting. Capabilities with no named owner or low readiness show where the report is exposed.

Ranking AI requests from several teams. Score the Level 2 capabilities with your own ratings and weights, then place each incoming AI request on the capability it serves. Requests are compared on the same scale, and the heatmap shows the result on one page.

Setting oversight before tools switch AI on. Read the target AI mode and oversight level for the capabilities a data product serves. Enable its AI features only where the profile and your own review agree, and record the decision against the capability ID.

Scoping a data product purchase. List the Level 3 capabilities a catalog, quality or integration product must support and the KPIs it should move. Shortlisted products are then scored against your definitions.

Comparing readiness across business lines. Run the 40-question assessment separately with each business line's data team. The answers show where data quality, skills or ownership differ, and which teams can start AI work first.

Combining data teams after an acquisition. Map both data organizations to the same capabilities. Duplicated capabilities show where teams can merge, and missing ones show work neither side covered.

Who It Is For

Chief data officer. Presents the function's plan and budget by capability and uses the ranking to choose the first AI investments.

Data governance and stewardship leads. Assign owners and stewards per capability and use the profiles to fix which approvals stay with a person.

Enterprise and data architects. Map applications and data products to capability IDs and link the matrix to the firm's wider architecture.

Heads of analytics and reporting. Show which foundations their reports depend on and argue for them in the same terms as the rest of the function.

Model risk and AI oversight teams. Use the risk tiers and oversight levels as the starting point for reviewing AI uses in data work.

The economics

The Alternatives

A data office can produce this matrix itself, commission it, or adapt a finished one. The choice decides where its architects spend their first months.

• Building it from a blank slate. The team collects terms from each data group, drafts definitions, settles where one capability ends and the next begins, and runs review rounds until the business agrees. The AI ratings, the scoring model and the assessment are separate pieces of work after that.
• Hiring consultants to build it. An outside team brings a method and extra hands, and it starts by drafting the same generic structure most data functions share. Your people still spend their time in elicitation workshops, and someone has to keep the model current after the engagement ends.
• Starting from this model. The definitions, KPIs, profiles, ratings, scoring model and assessment arrive checked against each other. Reviews start from a draft, and the discussion is about where your firm differs.

Tailoring the ratings to your data estate and setting the weights still needs people who know the firm, inside or outside. The ratings describe a typical large enterprise and are meant to be changed.

Getting Started

A data office can move from the download to a first ranked list in these steps:

1. Read the Welcome document and the user guide to learn how the files and IDs connect.
2. Review the capability map deck with the heads of data, analytics and governance, and mark capabilities to rename, merge or remove.
3. Change the AI ratings where your estate differs and enter your weights in the Excel workbook.
4. Run the readiness assessment with each data team and note the gaps that block AI work.
5. Take the top-ranked capabilities, read their profiles and write a short proposal for each with an owner and target KPIs.

Public sample

A Sample Profile

The sample is 0.82% of the package and traces one thread through it: data quality issue resolution. The fields below come from the Data Quality Issue Resolution (DA-6.3) AI profile:

• AI mode: Augment
• Oversight level: Human-in-the-loop
• Risk rating: High
• Wave: Wave 2
• Fit, value, feasibility and data readiness scores: 3, 5, 3, 2 on a 1–5 scale
• Patterns of work: Analysis & insight, Rules-based decision, Coordination & orchestration
• What AI could do: Log quality issues from checks, consumer reports and stewards in one record, merge duplicates and propose severity and an owner from the critical data elements and reports affected
• What people decide: The Data Owner approves every correction of data of record, at its source and in its copies, on the evidence the Data Steward prepares, after hearing the Data Quality Lead

Upgrading Later

Buy this edition now and, if you upgrade to the Data and Analytics Business Architecture Package: AI Transformation Blueprint within 90 days, its full price counts against the upgrade. Email your order number to support@finantrix.com for a one-time checkout code.

Read before you buy

Common Questions

Which edition fits my team?

Choose the AI-Ready Capability Model for the capability map, an AI profile for every L2 capability, prioritization, the heatmap and the readiness assessment. Choose the AI Transformation Blueprint for all of that plus value streams redesigned for AI, the business data model, agent specifications, use cases, responsible AI principles and controls, the operating model and the roadmap, a leadership presentation template, and bonus files on roles, operating model archetypes and transformation.

Can our internal AI tools use the content?

Yes, inside your organization. The Enterprise license covers internal tools, including assistants and agents you run yourselves. No license permits using the content to train, fine-tune or prompt AI systems on behalf of third parties.

Does it address AI laws and regulations?

No. The content is business architecture by intent: capabilities, value streams, data and the AI layer describe what data and analytics does and where AI fits, which holds under whatever laws apply to you. The risk tiers, principles and controls are lasting practices for your compliance team to map to the jurisdictions you operate in.

What happens when the 180 days end?

The license continues with no end date, and corrections stay free for as long as you use the files. New versions released within 180 days of purchase are free, and we email a download link for each. After that nothing renews, and new versions are offered at 25% off.

Can I move from the Capability Model to the full blueprint?

Yes. If you upgrade within 90 days, the full price paid for the Capability Model is credited. Email your order number to support@finantrix.com for a one-time checkout code.

Could an AI assistant write this instead?

An AI assistant will describe a data function in seconds, and for a first look that is enough. Ask again and the list changes, and none of its entries has been checked against the others. A data office that decides to let a tool propose corrections to data of record needs a record it can show a risk committee later. The matrix gives each of its 192 Level 3 capabilities a stable ID, validates its cross-references before release, rates every Level 2 capability for AI mode, oversight and risk tier, and ranks them with weights you control. Use the assistant to explore and the matrix to decide.