121 CRM playbook · Data-quality review

Find and correct records that are incomplete, duplicated, stale or inconsistent without hiding uncertainty.

A fictional consultancy reviews duplicate contacts and missing ownership before a campaign. This playbook turns that kind of situation into an owned sequence with explicit stop points, exceptions and measures. Use only channels, data and capabilities approved for your setup.

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Customer Thread

Data-quality review: the working sequence

Illustrative example

Choose a bounded question
Review one risk such as duplicate email, missing owner or invalid status.
Set correction rules
Define what can be changed automatically and what needs a person.
Prevent recurrence
Fix forms, imports, definitions or training that created the issue.
Human review
A person checks the outcome and any exception.
Human decision

Illustrative playbook — names, organisations and activity are fictional.

Fictional workflow demonstration

Data-quality review, with controls and handovers visible

The coded interface shows an example sequence using fictional data and does not expose a live CRM account.

121 CRM · Migration mapping

Source fieldCRM fieldStatus

Company nameOrganisationMapped

Primary emailContact emailSampled

Free-text notesHistory notesReview

Legacy stageOpportunity stageDecision

12 mapped4 sampled3 decisions open

Illustrative exampleSample records and field names are fictional. A real migration needs source-specific reconciliation.

What supports the playbook

See what the CRM can coordinate — and where your team steps in.

The CRM can organise the sequence, but the business still sets the rules, checks consent and handles the moments that need human judgement.

Capability 01

Duplicate management

Uses account rules and review steps to reduce or resolve repeated contact records.

How it works: Stores or organises supported business data under configured fields, permissions and record rules.
Why use it: Keeps teams from following up with the same person through conflicting records.
Plan for: The platform does not make imported, duplicated or incomplete data accurate without review.
Your team owns: A data owner must define the record model and resolve quality, ownership and access exceptions.

Capability 02

Bulk actions

Applies supported updates or actions to a selected group of records rather than one at a time.

How it works: Stores or organises supported business data under configured fields, permissions and record rules.
Why use it: Saves administrative effort while retaining a review point before a large change.
Plan for: The platform does not make imported, duplicated or incomplete data accurate without review.
Your team owns: A data owner must define the record model and resolve quality, ownership and access exceptions.

Capability 03

Import and export

Moves supported records through structured files, with mapping and validation needed before relying on the result.

How it works: Stores or organises supported business data under configured fields, permissions and record rules.
Why use it: Supports controlled migration and operational extracts when the data is prepared and reconciled.
Plan for: The platform does not make imported, duplicated or incomplete data accurate without review. Bulk migration requires mapping and reconciliation; do not treat a completed import as proof of accuracy or reversibility.
Your team owns: A data owner must define the record model and resolve quality, ownership and access exceptions.

Capability 04

Custom fields

Adds structured fields to supported record types so a business can capture information it actually needs.

How it works: Stores or organises supported business data under configured fields, permissions and record rules.
Why use it: Replaces important notes and spreadsheet columns with searchable, reusable data.
Plan for: The platform does not make imported, duplicated or incomplete data accurate without review.
Your team owns: A data owner must define the record model and resolve quality, ownership and access exceptions.

Entry criteria

Begin with a valid trigger, enough context and one owner

A scheduled review or operational issue identifies a defined set of data-quality risks. Before any automated action, confirm that the event, customer record and communication basis are suitable for this playbook.

Trigger

A scheduled review or operational issue identifies a defined set of data-quality risks.

Practical scenario

A fictional consultancy reviews duplicate contacts and missing ownership before a campaign.

Human decision retained

Accountable owner

Name the person or queue that accepts the work, monitors exceptions and can stop the sequence.

The sequence

Run the data-quality review playbook step by step

Each step should leave evidence a colleague can understand. Timing and channels should follow the customer context rather than a generic high-pressure cadence.

1. Choose a bounded question

Review one risk such as duplicate email, missing owner or invalid status.

2. Profile the records

Count, sample and understand patterns before bulk changes.

3. Set correction rules

Define what can be changed automatically and what needs a person.

4. Apply and reconcile

Test a sample, complete the approved change and compare results.

Close the loop

5. Prevent recurrence

Fix forms, imports, definitions or training that created the issue.

Owner, timing and exception

Make the operational state easy to review

The supporting view keeps the next action and human checkpoint visible around the automated steps.

121 CRM · Connected operations

Book discovery

October · illustrative availability

MTWTFSS1234567891011121314

11:3014:00

Appointment sequence

Booked

Calendar entry created

1 day before

Reminder scheduled

Exception

Owner reviews a reply

Journey summary

New

Illustrative

Owned

Illustrative

Due

Illustrative

Website

Referral

Direct

Illustrative exampleAll dates, counts and workflow states are fictional. Availability and behaviour depend on the agreed setup.

Exception paths

Stop the normal route when the facts change

Exceptions are part of the process, not failures to be hidden. Route them to a person with the authority and context to decide what happens next.

Human review

Conflicting customer identities

Do not merge until a person can establish the relationship safely.

Human review

Legally required retention

Apply the approved policy rather than deleting for neatness.

Human review

Unknown value

Record unknown or leave blank; never infer a fact for completeness.

Operational review

Measure whether the work became safer and clearer

Use measures that reveal ownership, service quality and control. Counts of messages, tasks or workflow runs are supporting signals, not proof of customer value.

Records failing the defined rule

Track trend with a stable definition.

Corrections with reviewed evidence

Sample accuracy after changes.

Repeat source of errors

Prioritise prevention over repeated clean-up.

Questions about this page

Useful answers, without the sales fog.

1. When should the data-quality review playbook start?

A scheduled review or operational issue identifies a defined set of data-quality risks. Confirm the record is eligible and that a named owner or monitored queue is available before allowing customer-facing action.

2. Which channels should the data-quality review playbook use?

Use only configured channels that suit the purpose, customer preference and consent position. Give each channel a distinct role and check provider availability and usage charges before launch.

3. When should data-quality review automation stop?

Stop when the purpose is complete, the customer responds or opts out, the data conflicts, or an exception needs judgement. In this playbook, pay particular attention to conflicting customer identities, legally required retention, unknown value.

4. How should we judge whether data-quality review works?

Review records failing the defined rule, corrections with reviewed evidence, repeat source of errors. Combine the numbers with sampled records and customer context; do not treat activity volume as a business outcome.

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Continue the journey

Data import and export Custom fields Security and data Weekly CRM review

Delegate the work. Keep the human.

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