1. Best Practices for Data
Governance and Stewardship
Inside Salesforce
Beth Fitzpatrick, Director Product Marketing, Data.com
Greg Malpass, Founder and CEO, Traction on Demand

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3. Who Do We Have Here Today?
Who Owns Data in Your Organization?
Sales Marketing IT
Support
Data
Operations
Sales
Operations

4. Governance and Stewardship
Common understanding
Rules/policies that are designed to
maintain data order.
Quality, management, policy, risk
management
Thresholds and
Measures
Rules and
Systems
Assignments/actions and personas
designed to uphold data governance
Obligations and
role responsibility
Motivation to
participate. Culture

5. Greg Malpass
Founder and CEO – Traction on Demand

6. • Downstream “Target”
Why do we care about data?
• Upstream “Source”
Where is it from?
Motive
Trust
Knowledge
Intent
Where is it consumed
Timeliness
Usage
Insight
Action

7. • Getting ahead with Salesforce.com
– Integration
– Analytics
– Stewardship/Governance
• Getting ahead with Data.com
– API
– Advanced use cases
– Building data from change
Why do you care about Data?
• Getting started with Salesforce
– Cleansing
– Migration
– Adoption
• Getting started with Data.com
– Record creation
– Record management
– Introduction

8. Let’s talk about data quality

9. What Challenges are You Facing Today?

10. What We Have Found With Customer Data
Name Phone
Bob Johnson 415-536-6000
Bob Johnson 650-205-1899
Rob Johnson 415-536-6100
Bob C. Johnson 408-209-7070
Bob Johnson 415-536-6000
Rob Johnson 650-205-5555
Bob T. Johnson 650-780-9090
Robert Johnson
(415) 536-2283
90%Incomplete
74%Need Updates
21%Dead
15+%Duplicate
20%
Useless

11. The Ever Changing World of Data
Source: D&B Sales & Marketing Research Institute
120 businesses change addresses
75 phone numbers change
30 new businesses are formed
20 CEO’s leave their job
1 company gets acquired or merged
In 30 minutes

12. Data Governance Drives Quality Data
So You Can Confidently …..
Whitespace Analysis /
Cross-sell & Upsell
Market Analysis &
Customer Segmentation
Territory Planning &
Alignment
Prospect & Target New
Accounts
Lead Scoring & Routing Revenue Roll-Ups

13. Data Governance is an Investment (vs. Expense)
Where you choose your investment goals, manage your risks
Source: DAMA DMBOK
Data Management Functions Environmental Elements
Data
Governance
Goals &
Principles

14. Moving from talking to doing

15. Assess
- Get a sense of the state of your current data
- Who are your users – reports/adoption
- What fields are being used - fieldtrip
- What do they do – integration/workflow/dependencies/docs/conga etc.
- How is the overall quality – 3rd party, self check
- What do your users “use” it for – ask them/stalk them
- What tools are dependent – Integrations/downstream
- What analytics are important – dashboards/reports/BI
Goal: get inventory and current state

16. Clean It Up
- Initiate some “level 1” cleansing
- Standardize outliers (normalize)
- Self append (inferred fixes)
- Baseline duplicate management (careful of dependencies/history considerations)
- Kill useless records – FHD – Flag,Hide,Delete
- 3rd party append (internal and external)
- Advanced duplicate management
Goal: get your baseline in order

17. Develop a strategy
- Two choices – distributed or managed
- What will work within your “culture” today
- What is sustainable looking forward
- Recommendation – develop a distributed data management model
Goal: get your baseline in order

18. Levers
• Forced business processes – contract generation/automated replies/dashboards
• Entitlement and ownership – labeling, ownership, naming
• SWAT team – call for help – tactical support team
• Gift of time
• Gift of focus and analytics
• Gift of assignment
X

19. Guiding Principles

20. Data Quality Guiding Principles
• Know where you’re going and make hard decisions on priorities.
• Ownership: Clear ownership of core data.
• Definitions: Widely understood definitions of account, customer etc.
• Objectives: Agree on areas of focus and how it will be used.
1. Agree on a Clear Vision and Ownership
• Highlight focus areas for data quality in the system.
• Flag governance status and quality score clearly. Use icons.
• Leverage validation rules, record types, profiles and dependent
pick lists.
• The “Give” (and take).
2. Articulate Priorities

21. Data Quality Guiding Principles
• Give users the tools to be successful.
• Search before create. Warn if duplicate.
• A common key adds power: D-U-N-S
• Easy enrichment: MDM, Data.com, Address Validate.
• Empower reps: social stewardship.
3. Ensure Usability at Point of Entry
• Governance and Stewardship teams support quality.
• Monitoring and approval of key information : Several approaches
• Management of bulk-loads.
• SME/ Gatekeeper for integrations.
4. Have Experts Support the Process

22. Data Quality Guiding Principles
• Get rid of the noise.
• Develop and apply an archiving policy
(ie both at account and overarching level).
• Regular de-duplication cycles based on pre-agreed scenarios
(eg CRM Fusion demandtools initially then dupeblocker).
• Conduct regular field audits (eg fieldtrip, Traction Field Audit Tool).
5. Conduct Regular Housekeeping
• Foster a culture of Data Stewardship. Celebrate success.
• Define measures and score – automatically.
• Report and stress single KPI – by org, BU, User.
• Measure improvement over time.
6. Measure . . . And Hold Accountable

23. Tactical Examples

24. Getting Tactical
Moving from talking to doing:
• 9 declarative elements in SFDC that are excellent
governance/stewardship enablers
Check the www.tractionondemand.com blog for additional details

25. Data Quality
Security
What:
Leverage SFDC field level
security to restrict access to
certain data validation fields.
IE approval status, record
condition.
Why:
Allocate responsibility in
determining what is “trusted” to
a certain group of people. Hide
fields to enable usability.
How:
• Set up custom profiles for ALL – catalogue access
• Manage Field Access
• Then create Permission Sets
Hide/Restrict access to certain fields that are
strategic in nature

26. Data Quality
Validation Rules/Dependencies
What:
Block the ability for users to
enter misaligned values via
validation rules. Leverage
rules to create gentle blocks
and encourage correct
process.
Why:
If you give people
workarounds, they’ll use them.
Typically workarounds = bad
data and no governance
How:
• Conditional Validation statements using mixed
AND/OR
• English: if the record type is Prospect and the
state/prov is empty require it.
• Give GREAT explanations and embed brand

27. Data Quality
Record Types/Layouts/ Visual Indicators
What:
Use record types to segment
an object based on status to
ensure only relevant
information is presented based
on stage in process.
Why:
Don’t show users information
that is meaningless within the
context they are operating.
- RT/Layouts by status
- RT/Layouts by type
How:
• Establish your profiles
• Establish your types of records (account type)
• Establish your status/progress by type
• Use icons to clearly indicate stage/ quality
• Determine what is relevant by type/status
• Develop custom page layouts for each
• Create WF to auto move RT based on defined
actions

28. Data Quality
Dependent Picklist Fields
What:
Only show relevant values on a
particular record. Don’t give
users incorrect choices
Why:
Noise. Makes your system look
poorly thought through. Easy
logical fix
How:
Set up profiles
Set up record types
Create fields, assign values by RT
Create additional dependent fields, follow same
path
Use Excel to map your matrix out.

29. Data Quality
Approval Workflows
What:
Prior to record lock, or pass
over to integration leverage
approval workflow as final gate.
Why:
Not all data gets migrated
Apply expensive resources to
sample
Ensure data that is propagated is
good
How:
• Set up profiles
• Set up record types
• Set up page layouts
• Set approval workflow. Apply submit for
approval button to specific layouts. Block
progress without approval via validation.

30. Data Quality
System / User Fields
What:
Create custom fields to allow
users to enter basic information
without disturbing sync data.
Leverage formula fields to
differentiate
Why:
Battle user frustration
Open up usability without losing
DQ
Small step in managing biz
expectation
How:
Save standard fields for native synchronizations
and leverage custom fields for variable data.

31. Data Quality
Add a Data Quality Score
What:
Establish a basic point scoring
formula to provide data quality
ratings on records
Why:
Expose your “trust” in a record and
detach the typical link between data
quality and adoption.
Set user expectations on records
Create positive motivation to
improve
How:
Create a single formula field to score
completeness from priority fields
Conditional statement that evaluates:
- Consistency
- Recency – last changed, last activity
- Completeness
- No duplicates
- 3rd party validation
- Represent point ranges with a graphic – one
score
- Use Analytic Snapshots to measure over time
- Report by Rep for accountability

32. Data Quality
Kill Suspects
What:
Simply put, most systems have
2x the data they need. Clean
house!
Why:
Eliminate noise
Give ownership to users
Invest resources in high profiles
prospects
How:
Never delete first
1. Isolate suspects
2. Flag for elimination and color code
3. Hide with security
4. Wait
5. Backup
6. Delete
!! Warning. This record has been flagged for deletion. Please
update details with complete information by #formula to prevent
removal.

33. Data Quality
De-dupe
What:
Follow a consistent method/
process when de-duping and
NEVER deter
Why:
Duplicates are easy to eliminate,
and very expensive to restore
should you have made a mistake
How:
Main Order
1. Accounts vs Accounts
2. Contacts within Accounts
3. Contacts between Accounts
4. Accounts vs Accounts
5. Leads
6. Leads to Contacts
Search before create
Address correction

34. Data Quality
Make it Easy
What:
Consider how record
generation be easy and
convenient.
Why:
If data entry is easy and there is
value in entering details,
supports workflow, people will do
it.
How:
Search before create – DDC API applications
Address tools
Clicktools forms to flatten SFDC record
generation
Experian QAS/ Postcode Anywhere
Workflow to infer values
Social search