Data and Analytics Business Architecture Package: AI Transformation Blueprint

The Data and Analytics Business Architecture Package takes a data function from a ranked list of capabilities to a designed AI program. It includes the full Capability Matrix and adds 12 value streams with 60 stages shown today and AI-native, a Business Information Model of 115 entities, 43 agent specification starters, 64 use cases, 45 controls, an operating model and a three-wave roadmap. Data offices at banks, insurers, asset and wealth managers and payments firms use it to decide how agents will work on governed data, under whose authority and in what order. The content is cross-industry.

What it is

Inside the Data and Analytics Business Architecture Package

The package begins where the Capability Matrix ends. The matrix ranks where AI fits in data work. The package designs the work that follows, and every item it adds carries the ID of the capability it serves.

Its Value Stream Catalog holds 12 data and analytics value streams split into 60 stages, plus 13 standard value streams. For each stage it shows the work as it runs now, the work as an AI-native design would run it, and the point where a named person takes the decision.

The Business Information Model defines 16 subject areas, 115 entities, 743 attributes and 388 relationships. Each entity carries sensitivity and data-quality requirements, so a designer can see what an agent would read before writing its permissions. Agent starters, use cases, controls, the operating model and the roadmap are all built on these two layers.

The deliverables

What the Download Contains

The package contains the whole Capability Matrix, with these design layers added:

The model

• The complete AI-Ready Capability Model edition: 8 L1, 48 L2 and 192 L3 data and analytics capabilities, with definitions to L3 and 193 KPIs at L2, an AI profile for every L2, AI ratings for every L3, prioritization, the AI heatmap and a 40-question readiness assessment
• 12 data and analytics value streams across 60 stages, with each stage's steps shown today and AI-native and its human checkpoint, and 13 standard value streams as a reference
• An AI-ready business data model of 16 subject areas, 115 entities, 743 attributes and 388 relationships, each with its sensitivity and data-quality requirements

AI and governance

• 43 agent specification starters, each setting tool permissions, guardrails and escalation paths
• 64 use cases written as structured business cases
• 45 controls and responsible AI principles, mapped to risk tiers
• An AI operating model covering governance bodies, a RACI, an agent lifecycle, role impacts and new roles
• A roadmap in three waves

Files and terms

• To begin, a Welcome document; then a leadership presentation template to adapt, the capability map deck, standalone documents for the capability definitions, KPIs, data model, agent specifications, use cases and AI governance, a value streams deck, the Excel workbook, the implementation guide
• Bonus files included: a data and analytics glossary, data and analytics roles and how AI changes them, data and analytics operating model archetypes, a data and analytics transformation overview and framework, A Practical Guide to Business Architecture, a capability modeling overview, an AI-ready capability profile template, capability relationship mapping templates, a framework-to-enablement deck, a capabilities to microservices and AI agents mapping example, and a business architecture leader role guide
• 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

Redesigning quality issue handling. Follow a data quality issue through its value stream, from detection to correction, and compare the current stages with the AI-native ones. An agent logs and merges issues and proposes a severity and an owner. The Data Owner still approves each correction of data of record.

Writing data access rules for agents. Use the sensitivity requirements in the Business Information Model to set which entities each planned agent may read or write. Access is decided at design time, before a tool is configured.

Reviewing agent designs at the architecture board. Ask each team to present its agent against the matching specification starter. The board compares tool permissions, guardrails and escalation paths with a standard instead of judging a demo.

Assembling the first wave. Select from the 64 use cases those that serve your top-ranked capabilities and place them in the three-wave roadmap. Each use case already names its capabilities and risk tier, so dependencies show early.

Assigning accountability for agents and datasets. Adapt the governance bodies, RACI and agent lifecycle from the operating model. Each agent gets an owner, and each change to it has a named approver.

Securing funding from the executive committee. Fill in the leadership presentation template with your chosen use cases and roadmap. It shows what each wave delivers, what it depends on and which decisions remain with people.

The Problem Behind It

A ranked list tells a data office where to begin and leaves the design open. Someone still has to decide which steps an agent takes on a quality issue, which tables it may query, who signs off and which control is tested. When each project team answers those points for itself, the answers drift apart as the number of agents grows.

Data agents need a shared design more than most. A finance report, a risk model and a customer record can all draw on the same governed entity, so an agent that edits that entity affects all three. The package fixes the rules once, in one information model, one set of controls by risk tier, one RACI and one agent lifecycle, and each new data agent follows them.

Who It Is For

Chief data officer. Owns the roadmap and the funding case and decides who is accountable for each data agent.

Data and information architects. Adapt the Business Information Model to the firm's data and link it to the value stream stages and agents.

Data engineering leads. Build agents from the specification starters, with permissions and data access settled before any code is written.

Model risk and AI oversight reviewers. Check each agent design against the controls for its risk tier and confirm where a person keeps the decision.

Data program managers. Run delivery wave by wave and track what each wave changes in the value streams and their KPIs.

Getting Started

A data office can work through the package in this order:

1. Read the Welcome document and the implementation guide, then rank the capabilities with the prioritization model.
2. Open the value streams behind the top-ranked capabilities and review the AI-native stages with the people who run them.
3. Compare the Business Information Model with your data and note the entities the first agents will need.
4. Adapt the matching agent starters and use cases to your systems.
5. Adopt the controls by risk tier, set up the governance bodies and RACI, and take the roadmap and leadership presentation to the executive committee.

The economics

The Alternatives

Designing an AI program for the data function means building several linked layers, and keeping them linked is most of the effort.

• Building it from a blank slate. The team maps value streams, models the information, writes agent designs, drafts use cases, defines controls and agrees an operating model, each through its own reviews. Every change in one layer has to be carried into the others by hand.
• Hiring consultants to build it. Advisers can staff each layer in parallel, but they still draft the generic structure first. The links between their layers are as reliable as their last consistency check.
• Starting from this model. All layers arrive drafted and linked by ID, and the links are validated before release. Workshops begin by choosing what to change, so the first complete design exists from the start.

People who know the firm are needed to fit the operating model to its structure, set risk tiers to its appetite and choose which use cases to fund. Drafting a generic data entity or agent design does not need that judgment.

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

Customers who bought the AI-Ready Capability Model edition within 90 days receive its full price as credit against this package. 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 can outline a data value stream or suggest agents for quality work in a minute, and that helps you get oriented. What it produces does not hold together as a design. Each answer is new, its agents have no permissions or escalation unless you write them, and nothing ties an agent to the entity it reads or the control that governs it. The package gives you stable IDs across capabilities, value stream stages, data entities, agents, use cases and controls, every cross-reference checked before release, rated AI profiles, risk tiers, 45 controls and a scoring model you weight. Those are what a risk committee or audit team will ask to see.