Data and Analytics Capability Model: AI-Ready Edition

The Data and Analytics Capability Model: AI-Ready Edition is for CIOs, architects and transformation leads who have to decide where AI goes in the data function and in what order. It names what data and analytics does in business terms, 48 L2 capabilities in all, and records for each one where AI fits, how much a person stays in charge and how risky the use is. A scoring model ranks the capabilities on weights you set, and a 40-question readiness assessment shows what would hold the first projects back.

About the Data and Analytics Capability Model

The model describes the data and analytics function by what it does, not by the tools it runs or the teams that run it. Its 8 L1 areas break into 48 L2 capabilities and 192 L3 capabilities, each with a definition, and the L2 level carries 193 KPIs. Every capability has a stable ID, such as DA-6.3 for data quality issue resolution, so a roadmap, a heatmap and an investment case can point at the same thing and mean the same thing.

Data management and analytics sit in one structure rather than two. Business intelligence is part of the model, next to the governance, quality and integration work it depends on. That matters because most data problems cross team lines: a wrong figure in a report is often a quality issue, a missing owner and a lineage gap at the same time, and a model split by department hides the connection.

The AI layer is what separates this from a plain capability list. Each L2 capability has a profile that records the AI opportunity, what stays human, the agents involved, the data required, AI measures, prerequisites, risks and workforce impact, with a target AI mode, a human oversight level and a risk tier. Each L3 carries its own work pattern, AI mode, AI fit and risk tier. In the sample, data quality issue resolution is rated Augment, with a human in the loop and a High risk tier, and the profile states that the Data Owner approves every correction of data of record.

Why It Matters

Data functions are usually described in the vocabulary of their systems: the warehouse, the catalog, the quality tool, the reporting estate. That works for engineers and fails in a funding discussion, because two proposals that describe different slices of the same work in different terms cannot be compared. A capability model gives the function one business vocabulary that outlasts the next platform change.

AI adds a second problem. AI features now arrive in many of the tools data teams already own, from automated profiling to plain-language queries, and each one comes with its own case for adoption. Without a shared map, decisions about what to automate and who signs off are made tool by tool. The AI profiles and the prioritization model let the function make those decisions once, across all 48 L2 capabilities, with the oversight and risk written down.

What's Inside

• 8 L1, 48 L2 and 192 L3 data and analytics capabilities, with definitions to L3 and 193 KPIs at L2
• A full AI profile for every L2 capability: opportunities, what stays human, agents, data, KPIs, AI measures, prerequisites, risks and workforce impact
• AI ratings for every L3: work pattern, target AI mode, AI fit and risk tier
• A prioritization model with a risk-adjusted score and rank for every L2 capability, using your own ratings and weights
• An AI heatmap of the whole model
• A 40-question weighted AI readiness assessment
• A Welcome document, the capability map deck, standalone capability definitions and KPIs documents, the Excel workbook, the user guide
• Bonus files: 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
• Free corrections, and new versions free for 180 days

Every file names your organization and carries your license ID. Ratings describe a typical large enterprise and are a starting point for your own.

Where It Is Used

AI opportunity assessment. Use the risk-adjusted scores to find the capabilities where AI fit and value are high and the risk is manageable. The heatmap shows the result on one page, so a steering group can see why a capability ranks where it does and challenge a rating rather than a conclusion.

Data tool rationalization. Map catalog, quality, integration and reporting tools to the L2 capabilities they support. Capabilities served by several tools, or by none, show where to consolidate and where a gap is hiding behind a crowded tool list.

Vendor evaluation scoped by capability. Before a demo, list the L3 capabilities a purchase is meant to serve and the KPIs it should move. Vendors then answer against your definitions instead of their own feature list, and two products can be compared on the same terms.

Data ownership and stewardship design. Assign owners and stewards to capabilities rather than to systems. The entries on what stays human show which decisions need a named person, which helps you write decision rights before agents start preparing work for those people.

Readiness baseline. Run the readiness assessment with data, analytics and business stakeholders to see where data quality, skills or governance would hold back AI work. Running it again later shows whether the gaps are closing.

Capability-based roadmap. The prerequisites in each AI profile show which foundations have to be in place before an AI use can work. Ordering initiatives by those dependencies, rather than by the date a request arrived, keeps the roadmap from committing to results the data cannot yet support.

Who Works With It

Chief Data Officer or head of data. States the function's strategy and investment case in capability terms and uses the ranking to decide where AI goes first.

Enterprise and business architects. Connect data capabilities to the rest of the enterprise model, map applications to them and keep the IDs stable across planning documents.

Data governance lead. Uses the definitions and the entries on what stays human to set ownership, stewardship and approval rights.

Analytics and business intelligence leaders. Place reporting and insight work next to the foundations it depends on, and make the case for those foundations in the same vocabulary.

Transformation and portfolio managers. Tie projects to capabilities and KPIs so the portfolio shows what each program changes in the function.

A Blank Slate, Consultants, or a Reference Model

There are three ways to get a data and analytics capability model with an AI layer, and the difference between them is mostly where your people spend their time.

• Building it from a blank slate. Your architects draft the structure, run workshops with data, analytics and business teams to fill it, reconcile overlapping definitions and go through review cycles before anyone agrees on the vocabulary. Rating every capability for AI mode, oversight and risk, and building a scoring model, is a second project on top. The result fits closely, and the first usable draft arrives late.
• Hiring consultants to build it. A consulting team brings method and pace, and it still has to elicit the generic structure from your people before it can tailor anything. Much of the effort goes into drafting definitions that look similar in most large organizations, and the model can be hard to maintain once the team leaves.
• Starting from this model. You begin with defined capabilities, KPIs, AI profiles, ratings, a scoring model and a readiness assessment that have already been cross-checked. Workshops review and adjust instead of drafting, so the time goes to how your organization differs.

Consultants and internal architects are better used for tailoring than for drafting: changing the ratings to match your own data estate, setting the weights, and fitting ownership to your structure. The ratings in the package describe a typical large enterprise and are meant to be changed.

First Steps

1. Read the Welcome document and the user guide to see how the files and IDs fit together.
2. Walk the capability map deck with data and analytics leaders and mark capabilities to rename, merge or drop.
3. Adjust the AI ratings and set your weights in the Excel workbook so the ranking reflects your priorities.
4. Run the readiness assessment to find the gaps that would hold back the first AI uses.
5. Take a few of the top-ranked capabilities, read their AI profiles and turn each into a scoped proposal with an owner and KPIs.

Read the Public Sample

The sample follows one thread through the package, data quality issue resolution, and is 0.82% of the content. From the Data Quality Issue Resolution (DA-6.3) AI profile:

• Work patterns: Analysis & insight, Rules-based decision, Coordination & orchestration
• Target AI mode: Augment
• Human oversight: Human-in-the-loop
• AI fit / Value / Feasibility / Data readiness: 3 / 5 / 3 / 2 (1–5)
• Risk tier: High
• Roadmap wave: Wave 2
• AI opportunity: 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 stays human: 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

Upgrade Credit

Start here and move up later: the full price of this edition counts toward the AI Transformation Blueprint for Data and Analytics if you upgrade within 90 days. Email support@ciopages.com with your order number for a one-time checkout code.

Frequently Asked Questions

What is the difference between the two editions?

The AI-Ready Capability Model covers the capability map, an AI profile for every L2 capability, prioritization, the heatmap and the readiness assessment. The AI Transformation Blueprint adds 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 I upgrade from the Capability Model to the Blueprint later?

Yes. The full price you paid for the Capability Model counts toward the Blueprint if you upgrade within 90 days. Email support@ciopages.com with your order number for a one-time checkout code.

What happens after 180 days?

Your license to use the files does not end. Corrections are free for as long as you use the files. New versions released within 180 days of purchase are free: we email you a new download link when one is released. There is no renewal after that: you can buy a new version at 25% off its price.

Can I load the content into our own AI tools?

Yes, within the license. The Enterprise license allows internal tools, including your own assistants and agents. Neither license allows training, fine-tuning or prompting AI systems for third parties.

Does the package cover AI laws and regulations?

No, by design. It is business architecture: capabilities, value streams, data and the AI layer describe what data and analytics does and how AI fits, and they hold whichever laws apply where you operate. The risk tiers, principles and controls are durable practices your compliance team maps to your own jurisdictions.

Why not just ask ChatGPT or another AI assistant?

An AI assistant is a reasonable way to get oriented. Ask it what a data and analytics function does and you will have a plausible list in a minute. The list changes between runs, though, and nothing in it has been checked against anything else. This package is for decisions that have to stand up to review by a steering group, an architecture board or anyone who later asks why a capability was automated. It gives you stable IDs for 192 L3 capabilities, cross-references validated before release, an AI profile with a target mode and oversight level for every L2 capability, a risk tier for each, and a scoring model you weight yourself. Use an assistant to explore, and use the model as the record you decide against.