Minimum Operable Data (MOD): Your CX & SOC Foundation
- James F. Kenefick
- 2 days ago
- 6 min read
Most leadership teams now accept that Agentic AI is here to stay. You can rent high-quality models from the cloud, plug them into your CX and SOC stack, and prototype in days.
Yet production value still stalls.
Executives hear the same complaints from every direction:
CX teams see agents hallucinate or make inconsistent refund decisions.
SOC teams drown in noisy alerts because agents can’t see the right context.
Data teams say they spend most of their time hunting and cleaning data for each new use case.
The pattern is simple: the data layer was built for dashboards, not autonomy. It’s full of historical tables, half-governed lakes, and ad-hoc extracts—fine for analysts, toxic for agents that must act in real time.
That’s where Minimum Operable Data (MOD) comes in. MOD is not “all your data, cleaned.” It’s the smallest, governed data slice agents need to do real work in Customer.
Experience (CX) and Security Operations (SOC), with:
Clear canonical entities
Explicit lineage and ownership
Aggressive masking and minimization
Shared feature/embedding hubs
A durable evidence store
Get MOD right, and your AI roadmap stops being “another experiment.” It becomes a real operating capability.
Executive brief: what MOD changes for CX & SOC
For CDOs, CIOs, and heads of CX/SOC, MOD is a shift in how you think about Data Modernization:
You design for agents first, not reports. What data must be correct, fresh, and governed so an agent can safely act in a workflow?
You focus on canonical entities, not lakes: customer, order/entitlement, device/endpoint, identity, and policy/playbook.
You add feature/embedding hubs as the standard way agents consume context—no more one-off joins inside every project.
You create an evidence store as a first-class asset, so every agentic decision can be explained to auditors, incident responders, and your board.
You treat MOD as a product with SLOs and ROI, not a one-time integration project.
For it support companies in Chicago, IL and similar markets, MOD becomes the backbone that lets them deliver agentic services reliably—across CX, SOC, and core Data Modernization engagements.
Step 1: Canonical entities, lineage, and masking
Anchor on a short list of canonical entities
Instead of starting with “all CX data” or “all security logs,” MOD starts with a short, critical list of entities for CX and SOC:
Customer / Account – identifiers, consent flags, value/risk segment, communication preferences.
Order / Entitlement / Case – what was promised (product, warranty, SLA), what happened (deliveries, disputes), and how it was resolved.
Device / Endpoint / Asset – owner, criticality, patch and AV status, recent incidents, location.
Identity / Access – user, roles, authentication events, anomalies, and key privileges.
Policy / Playbook – codified rules for refunds, discounts, containment, escalation, and approvals.
For each entity, define:
The system of record
The minimal field set agents truly need
A stable key that lets you join across systems (customer ID, asset ID, identity ID, policy ID)
If MOD had a slogan, it would be: “Fewer entities, fewer fields, more trust.”
Make lineage explicit and owned
Next, you define lineage in a way your architects and auditors can both understand:
Show how each canonical entity is constructed from source systems (CRM, billing, ticketing, EDR, IdP, etc.).
Capture transformations—joins, filters, enrichments, masking—as code in your pipelines.
Assign an entity owner who is accountable for quality, freshness, and schema stability.
Lineage is not just a diagram; it’s a contract: when a CX or SOC agent uses Customer.core.V2, everyone knows what that means, what data it includes, and who is on the hook if something breaks.
Mask and minimize by design
MOD is also how you reduce risk.
For each entity and use case, define:
Which fields are strictly necessary for the agent to act.
Which can be masked, tokenized, or dropped entirely for that use case.
Where raw PII/PHI can never appear (logs, feature stores, ad-hoc exports).
You end up with statements like:
“Refund agents only see masked email and last four digits of card; full values are only used in the payment gateway.”
“SOC triage agents never log full IP + username + device serial in the same evidence record; we store a hashed link instead.”
That’s how you align MOD with your privacy program, your security architecture, and your auditors in one move.
Step 2: Feature/embedding hubs and an evidence store
Once MOD entities are defined, agents need a consistent way to consume them—and you need a consistent way to observe what agents did.
Feature & embedding hubs: fuel for agents
Instead of letting every project engineer their own “golden dataset,” you build:
A feature hub for structured features
An embedding hub for unstructured content
In practice:
For CX, a refund or service agent might call a feature view that returns:
Customer value and churn risk
Entitlement status and SLA tier
Refund/chargeback history
Current open cases or escalations
Plus embeddings from tickets, notes, and knowledge articles for retrieval-augmented reasoning.
For SOC, an alert triage agent might call a feature view with:
Endpoint risk score
Last patch date and AV status
Recent anomalous logins
Prior incidents involving this device or identity
Plus embeddings of prior incident reports and playbooks.
The point: every agent pulls from the same governed stores, not bespoke SQL in a forgotten notebook.
Evidence store: a single place to prove what happened
Every agentic decision should write an evidence record to a dedicated store. Think of it as a specialized log for:
Your NIST CSF 2.0 Govern/Respond obligations
Your ISO 27001 incident handling and audit needs
Your own root-cause and quality analyses
Each record should capture:
Which entities/features were used (with versions)
The agent/model and policy versions
The suggested action, final action, and confidence score
Any human approval or override
Timestamps and correlation IDs back into CX or SOC systems
Later, when someone asks, “Why did the CX agent approve that refund?” or “Why did the SOC agent isolate that device?”, you don’t hunt through five systems. You pull the evidence record.
For partners—like it support companies Chicago IL that manage CX and SOC stacks for clients—this evidence store becomes the backbone for quarterly reviews, incident post-mortems, and compliance reporting.
Step 3: MOD SLOs and ROI measures
MOD is not a static model; it’s a service. You need to measure how well that service supports your CX and SOC outcomes.
MOD SLOs: data reliability as a product
Define service-level objectives for your MOD layer, just as you would for an API:
Freshness
CX example: “For active customers, 95% of customers. core records are updated within 5 minutes of a material event (order, refund, complaint).”
SOC example: “For monitored endpoints, 99% of endpoints. core records reflect the latest EDR status within 60 seconds.”
Completeness
“For refund decisions, 95% of flows have populated customer, order, entitlement, and fraud features.”
“For high-severity alerts, 98% of flows have both identity and endpoint features.”
Correctness/quality
Regular reconciliation tests against source systems.
Data quality checks embedded in pipelines, with visible error budgets.
When MOD misses its SLOs, that’s not “just a data issue”—it’s a service risk you can discuss
with your CX and SOC leaders in operational terms.
ROI: how MOD shows up in CX, SOC, and engineering
You can then show MOD’s value in three dimensions:
CX performance
Higher self-resolution rates in digital and agentic channels.
Fewer “we can’t see your order” or “we need to escalate” moments.
Improved CSAT/NPS on high-friction journeys (refunds, disputes, onboarding).
SOC efficacy and risk reduction
Lower MTTC (mean time to contain) because agents see the right context immediately.
Fewer false positives, fewer dead-end investigations.
Cleaner evidentiary trails for incidents and compliance reviews.
Engineering leverage
Reduced time to spin up new AI or analytics use cases because teams reuse MOD entities and features.
Lower duplication of pipelines and one-off data marts.
Higher utilization of the same data assets across CX, SOC, finance, and product.
That’s how you walk into an executive meeting and say, “Our investment in MOD reduced downtime, improved customer retention, and cut build time for new automations”—not “We finished the data catalog.”
A practical path to MOD: journey-first, not platform-first
The trap is thinking you need a massive platform re-architecture to “do” MOD. You don’t.
What works in practice:
Start from a journey, not a warehouse. Pick one CX journey and one SOC journey that matter financially and operationally.
Work backward to entities and evidence. Ask, “What must an agent know to act safely here?” and “What evidence will we need to defend this action?”
Define MOD for that slice. Canonical entities, lineage, masking, features, evidence schema.
Expand deliberately. Once MOD works for two journeys, extend those entities and features into adjacent journeys, rather than starting from scratch.
Whether you operate in-house or partner with it support companies Chicago IL and similar providers, you’re giving everyone the same foundation to build on: a small, high-trust data fabric that’s fit for agents.
Initiate the MOD Definition Workbook
If your AI roadmap still assumes “we’ll fix the data later,” you’re betting against reality. Models are getting better every quarter; your agents will only be as good as the data you feed them.
The next move is not a giant platform program. It’s a shared blueprint:
Initiate the MOD Definition Workbook You’ll capture:
Canonical entity lists for your first CX and SOC journeys
Clear lineage maps with ownership and update expectations
Masking and minimization rules by entity and use case
A reusable data contract template for agents and consuming services
Use it to bring your CDO, CIO, CX, SOC, security, and compliance leaders into the same conversation and answer a concrete question:
“What is the smallest, governed data slice our agents must rely on this year—and how will we make it real?”
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