Go Live in 14 Days with CluedIn - A Step‑by‑Step Playbook

Go Live in 14 Days with CluedIn: A Step‑by‑Step Playbook

Audience: Data engineers, analytics engineers, solutions architects
Goal: Get a working, production‑minded slice of your use case live in 14 days—with real data flowing in via Ingestion Endpoints and out via Export Targets, while building the habits that make CluedIn successful over time.

Why this works

This plan embraces ELT and iteration:

  • Push data as‑is (raw) into CluedIn. Don’t pre‑clean. Let CluedIn’s schema inference, lineage, mapping, and cleaning projects do the heavy lifting.
  • Start small (one incoming asset), ship data out early, and improve mapping and quality as you learn.
  • Automate ingestion (streaming or scheduled batches) so you never have to re‑upload files manually.
  • Lean on AI Agents to spot quality issues, suggest validations, and surface duplicates.
  • Measure progress with data quality metrics. Improve frequently in small increments.
  • Practice mapping changes: try, test, revert. Lower the cost of change.

14‑Day Overview (at a glance)

Day Theme Outcome
1 Business slice & target destinations A crisp “thin slice” of the use case and where data will be exported
2 Ingestion Endpoints (stream/batch) Endpoints created for one single source
3 First raw data in One asset flowing into CluedIn “as‑is”
4 Minimal mapping v0 Just enough mapping to recognize entities and relationships
5 Export Targets v0 First export wired to BI / ops surface
6 End‑to‑end smoke test Data reliably moving in → out on a schedule
7 Retrospective & plan Add tasks for Week 2 improvements
8 AI Agents for QA Findings list: missingness, invalids, duplicates
9 Cleaning projects First incremental fixes running on a cadence
10 Data quality metrics Baselines + alerts; choose KPIs and thresholds
11 Iterative mapping Practice change/test/rollback; improve coverage
12 Dedup projects Deterministic rules + auto‑approve high‑confidence
13 Second asset Expand model; regression check exports
14 Go‑live checklist Runbook, ownership, on‑call and next 30/60/90

Scope guardrails: One source → one export in week 1. In week 2, add fixes, dedup, and one more source. That’s enough to go live credibly and safely.

Prerequisites

  • CluedIn workspace access and a service account (API key) with Ingestion and Export permissions.
  • A target Export destination (e.g., data warehouse table, analytics lake, CRM, reverse‑ETL).
  • One initial source you can stream or batch on a schedule (e.g., Kafka topic, webhook producer, S3/Blob folder, database CDC).
  • A lightweight success metric (e.g., “Daily pipeline success + record count within ±5%”, “<2% invalid emails”, “<1% duplicates”).

Core principles you’ll follow

  1. Push to CluedIn via Ingestion Endpoints. Prefer live streams (Kafka/webhooks) or automated batches (S3/Blob schedulers). Avoid manual uploads after day 1.
  2. Drop data “as‑is.” Don’t pre‑clean or reshape. CluedIn is ELT: land first, then transform/clean in place.
  3. Don’t perfect mapping up front. Get data in and out first. Iteratively refine mapping as you learn.
  4. Start small—one asset. Don’t model the world. Bring data in, then let requirements inform the model.
  5. Lean on AI Agents. Use them to find issues, propose validations, and surface duplicate candidates.
  6. Cleaning projects > one‑off fixes. Ship frequent, small improvements on a schedule.
  7. Measure constantly. Set DQ baselines now; raise the bar gradually.
  8. Make mapping change cheap. Practice modify → test → revert. Embrace versioning.
  9. Deduplicate safely. Start deterministic; auto‑approve only high‑confidence matches.

Day‑by‑Day Guide

Day 1 — Define the thin slice and “data out” first

Objectives

  • Pick one concrete question you want to answer or workflow to power.
  • Define the Export Target and format: table, topic, API, or file layout.
  • Identify one source (asset) that supports the outcome, even if imperfect.

Artifacts

  • Use‑case brief (1 page): purpose, users, downstream surface, SLAs.
  • Data contract (minimal) for the export: the final fields and semantics.
  • Runbook skeleton (who owns what, escalation, observability).

Tip: If the export is a table, decide primary key, update strategy (upsert vs. append), and schema evolution policy now.

Day 2 — Create Ingestion Endpoints (stream or batch)

Objectives

  • Create an Ingestion Endpoint for one asset, using a streaming or scheduled path:
    • Streaming: Kafka topic / webhook that produces JSON lines
    • Batch: S3/Blob folder with daily/hourly drops (CSV/JSON/Parquet)

Example: HTTP ingestion (webhook)

curl -X POST \
  -H "Authorization: Bearer <CLUEDIN_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"source":"crm-contacts","payload":{"id":"c_123","email":"a@example.com","first_name":"Ada","last_name":"Lovelace","updated_at":"2025-08-22T12:00:00Z"}}' \
  https://<YOUR_INGESTION_ENDPOINT_URL>/ingest

Example: S3/Blob batch registration (pseudo‑JSON)

{
  "name": "s3-sales-orders",
  "type": "s3-bucket-watch",
  "options": {
    "bucket": "acme-prod-orders",
    "prefix": "daily/",
    "file_types": ["json","csv","parquet"],
    "schedule": "cron(0 * * * ? *)"  // hourly
  }
}

Checklist

  • Endpoint created for a single asset
  • Producer (or object drop) automated
  • Authentication/authorization confirmed
  • Observability: ingestion success counter + dead‑letter path

Rule: From here on, no manual uploads. Everything arrives via the endpoint.

Day 3 — Land the first raw data “as‑is”

Objectives

  • Send a small but real sample through the endpoint.
  • Verify CluedIn can parse and persist the records.
  • Capture lineage and metadata (source system, timestamps, version).

Validation

  • Ingestion dashboard shows new records
  • Sample record previewed (raw fields visible)
  • No pre‑cleaning done upstream

Anti‑pattern: Don’t pause to cleanse/massage data before landing it. That’s what cleaning projects are for later this week.

Day 4 — Minimal mapping v0 (don’t perfect it)

Objectives

  • Create just‑enough entity mapping to recognize core entities (e.g., Person, Organization, Order) and a few relationships.
  • Avoid exotic field logic; capture IDs, names, keys, timestamps.

Tactics

  • Start with 5–10 high‑value fields only.
  • Mark known primary keys and foreign keys.
  • Add a default namespace for unmapped attributes.

Success looks like

  • Records appear as proper entities
  • Core joins/relationships visible
  • No blocking errors preventing export

Remember: You will change this mapping many times. That’s normal.

Day 5 — Wire up Export Targets v0

Objectives

  • Create the first export to your chosen destination (warehouse table, topic, API, file). Aim for a stable daily/hourly schedule.

Example: table export config (pseudo‑JSON)

{
  "name": "warehouse-contacts-v0",
  "type": "sql-table",
  "options": {
    "connection": "analytics-warehouse",
    "schema": "mdm",
    "table": "contacts_v0",
    "mode": "upsert",
    "primary_key": ["contact_id"]
  },
  "mapping": {
    "contact_id": "Person.id",
    "email": "Person.email",
    "first_name": "Person.first_name",
    "last_name": "Person.last_name",
    "updated_at": "Person.updated_at"
  },
  "schedule": "0 * * * *"  // hourly
}

Validation

  • Export task succeeds end‑to‑end
  • Downstream surface can query/use the output
  • Row counts within expected bounds

Day 6 — End‑to‑end smoke test on a schedule

Objectives

  • Run the pipeline in → through → out a few times on an automatic schedule.
  • Add alerts for failures and volume anomalies (±X% vs baseline).

Checklist

  • End‑to‑end schedule in place
  • Alerting/notifications configured
  • Runbook updated with failure modes and triage steps

Day 7 — Retrospective & Week‑2 plan

Discuss (30–45 min)

  • What slowed us down?
  • What parts of mapping are confusing downstream?
  • Which fields are most error‑prone?
  • What small fixes would materially improve trust?

Plan

  • Pick 3–5 fixes for Week 2 (AI Agents, cleaning, metrics, dedup).
  • Nominate owners and define “done” for each.

Day 8 — Use AI Agents to analyze data quality

Objectives

  • Run AI Agents to scan entities for:
    • Missingness, invalid formats, out‑of‑range values
    • Suggested validations (e.g., regex, domain lists, referential checks)
    • Duplicate candidates (e.g., person/org dedup)

Workflow

  1. Run an analysis prompt or playbook against your entity set.
  2. Review the findings list with counts and examples.
  3. Accept or tailor proposed validation rules and dedup hints.
  4. Open issues/tasks directly from findings.

Deliverables

  • Findings doc with top 5 issues
  • Proposed validation rules drafted
  • Candidate dedup keys identified

Day 9 — Create cleaning projects (incremental fixes)

Objectives

  • Build cleaning projects that remediate the top issues. Examples:
    • Standardize phone/email formats
    • Trim/normalize names
    • Resolve country/state codes
    • Impute defaults (only where safe)
    • Enforce referential integrity

Practices

  • Small, re-runnable steps
  • Idempotent logic (safe to run multiple times)
  • Versioned transformations; review & rollback friendly
  • Scheduled execution (hourly/daily)

Success

  • Cleaning pipeline runs on a cadence
  • Changes land back in CluedIn entities
  • No breaking changes to the export schema

Day 10 — Establish data quality metrics & alerts

Objectives

  • Define and baseline key metrics for your use case:
    • Completeness (non‑null %, required fields)
    • Validity (regex/domain compliance)
    • Uniqueness (duplicate rate)
    • Consistency (cross‑field rules)
    • Timeliness (SLA latency from source → export)

Actions

  • Capture baselines per entity/table.
  • Configure thresholds and alerts.
  • Add a dashboard panel to your downstream surface.

Rule of thumb

  • Improve by small increments frequently (e.g., +0.5–1.0% per day).

Day 11 — Iterate on mapping (practice change/revert)

Objectives

  • Evolve mapping to add fields demanded by downstream users.
  • Practice safe change:
    1. Branch/version your mapping
    2. Apply change to a staging export
    3. Compare outputs (row count, nulls, schema drift)
    4. Promote or revert

Checklist

  • Version history for mapping changes
  • Staging export used for trials
  • Rollback tested at least once

Day 12 — Set up deduplication projects

Objectives

  • Create dedup projects for key entities (Person/Organization) with a deterministic first pass.
  • Auto‑approve only high‑confidence matches; queue the rest for review.

Example deterministic rules (pseudo)

rules:
  - name: exact_email
    when: lower(email) matches lower(other.email)
    confidence: 0.98
  - name: phone_e164
    when: e164(phone) == e164(other.phone)
    confidence: 0.95
  - name: org_name_address
    when: norm(name)==norm(other.name) and norm(addr)==norm(other.addr)
    confidence: 0.92
auto_approve_threshold: 0.97
queue_threshold: 0.85

Success

  • High‑confidence duplicates auto‑merged or flagged for auto‑action
  • Reviewer queue created for the “gray area”
  • Export logic updated to respect master records

Day 13 — Add a second asset, adapt the model

Objectives

  • Onboard the second source that enriches the same entities or introduces a related one.
  • Validate that mapping, cleaning, and dedup logic still holds.

Regression checks

  • Row counts within expected bounds
  • DQ metrics didn’t regress
  • Export schema unchanged (or managed evolution with version bump)

Day 14 — Go‑live checklist & handover

Go‑live gates

  • End‑to‑end schedule stable for 3 consecutive runs
  • Alerts firing to the right channel with owners
  • DQ metrics tracked and improving
  • Dedup auto‑approve threshold validated
  • Runbook complete (how to pause/retry/backfill; RTO/RPO notes)
  • Ownership clear: source, mapping, cleaning, export

Handover packet

  • Use‑case brief, data contract, runbook, dashboard link, “what changed” log, next 30/60/90 roadmap.

How‑To Details & Templates

A. Minimal export data contract (template) 

name: contacts_v0
primary_key: contact_id
delivery:
  type: sql-table
  schedule: hourly
fields:
  - name: contact_id
    type: string
    semantics: stable synthetic id from source
    required: true
  - name: email
    type: string
    constraints:
      - regex: "^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$"
  - name: first_name
    type: string
  - name: last_name
    type: string
  - name: updated_at
    type: timestamp
    semantics: last update from any contributing source

B. AI Agent prompt starter

“Analyze the Person entity for missingness, invalid formats, and duplicates. Suggest concrete validation rules with examples and propose deterministic dedup keys. Prioritize fixes that unlock downstream usage of the contacts_v0 export.”

C. Cleaning project sketch 

project: normalize_contacts
schedule: "0 * * * *"  # hourly
steps:
  - name: trim_names
    action: set
    field: first_name
    value: trim(first_name)
  - name: normalize_email
    action: set
    field: email
    value: lower(trim(email))
  - name: e164_phone
    when: phone is not null
    action: set
    field: phone
    value: to_e164(phone, default_country="US")
  - name: drop_impossible_dates
    when: birthdate < "1900-01-01" or birthdate > now()
    action: set
    field: birthdate
    value: null

D. Data quality metric tracker (example) 

entity: Person
metrics:
  completeness:
    email_non_null: pct_non_null(email)
    name_non_null: pct_non_null(first_name) & pct_non_null(last_name)
  validity:
    email_regex_ok: pct_match(email, "^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$")
  uniqueness:
    email_unique: 1 - duplicate_rate(email)
  timeliness:
    export_latency_minutes: p95(now() - updated_at)
alerts:
  - metric: email_regex_ok
    threshold: ">= 0.98"
    action: "notify #data-quality"

E. Mapping iteration checklist

  • Create mapping branch/version
  • Add 1–3 fields only
  • Run staging export & compare
  • Roll forward or revert
  • Update runbook & data contract

F. Dedup reviewer guide

  • Approve only when you’d be comfortable auto‑approving next time.
  • Leave notes; encode your decision as a new rule where possible.
  • Track precision (false merge rate) and recall (missed dupes).

Troubleshooting

Ingestion fails intermittently

  • Check auth tokens, rate limits, and payload size.
  • Use a dead‑letter queue/folder and replay tool.

Mapping produces nulls

  • Verify source field names and paths (case/array/indexing).
  • Add defaults in cleaning; avoid lossy transforms early.

Export breaks schema

  • Switch to staging target and diff schemas.
  • Use versioned exports (_v1, _v2) and deprecate gracefully.

Duplicate cascades

  • Tighten deterministic rules and raise auto‑approve threshold.
  • Add secondary keys (email+phone, name+DOB, org name+address).

Operating Rhythm (after go‑live)

  • Daily: Pipeline run checks + key DQ metrics glance (2 mins).
  • Weekly: Review AI Agent findings; ship 2–3 cleaning tweaks.
  • Bi‑weekly: Add/adjust a mapping field; test rollback.
  • Monthly: Revisit dedup thresholds; expand export consumers.

Summary

In two weeks you can have a resilient, automated in→through→out slice in CluedIn, built on repeatable habits:

  • Automated ingestion (no manual uploads)
  • As‑is landing (ELT)
  • Exports first, mapping evolves with needs
  • AI‑assisted quality and dedup
  • Incremental cleaning projects
  • Metrics‑driven improvement
  • Cheap mapping changes you can revert anytime

Ship small, ship often—and keep the pipeline warm.