Customer Feedback Forum:
Data Governance and
Stewardship Best Practices
Beth Fitzpatrick, Director Product Marketing, Data.com
David Hughan, VP Professional Services, Data.com
Eric Kasserman, Solution Engagement Manager, Data.com
Christina Belding, Solution Engagement Manager, Data.com

Driving Growth and Success through Business Strategy
Company
Strategy
Profitable growth by leveraging
existing customers and
products and expanding
internationally
Data Strategy
Have the right accurate data at the
right moment to drive growth,
optimize business process, and
scale

Getting Started with Data
Governance

There is SO much to do, where do we even start?
Keep it simple, there is no magic bullet and things WILL change as you go through this
journey
Step 1-
Assess Your Entire Portfolio
Catalog YourAssets
1 2 3
Step 2-
Understand Your Options
Not every application should be run
in Salesforce.com
Step 3-
Plan Your Approach
Create Your Team, Create Your
Plan, Validate ItAnd Socialize

Ok, so a little more than just 3 steps…
It’s really not hard to do, once you know where to start
1- Catalog all portfolio and data assets
2- Identify the 6-18 month out treatment plan for each
3- Identify current state “health and wellness” of data
4- Identify future state “health and wellness” of data
5- Identify ancillary modules or capabilities that will be used
6- Identify data volumes and paths (new records, updated records)
7- Identify business stakeholders, IS stakeholders, organizational stakeholders
1
2
3

Data Assets – They Are YOUR Assets
You own them, manage them, care and pay for them, but they grow and need your guidance
For each data asset, determine the following Considerations
What kind of data is it? Public, proprietary, private?
Master record, transactional record (dated), structured, unstructured,
etc.?
Who is the business audience that consumes the data? What level of importance is the asset to them – functionally speaking?
Who owns the data (authorship) Does this data allow you to be actionable- if so how?
Where does new data come from (primarily)? Is the data fed in from other systems?
Where do updates come from-primarily? New record updates, data enrichment from 3rd parties, etc?
How much data do you have, how fast does it change, what different
varieties exist….the answer is the trustworthiness of the data.
Data volume, velocity, variety= veracity (trustworthy)

Data- Lifecycle and Process
These areas reflect common touch points in the data lifecycle
Acquisition
Quality
Hygiene
Governance
&
Stewardship
Reporting &
Dashboards
•Document all sources- streamline manual and offline
record creation
(Manual, purchased lists, spreadsheets, W2L, ERP, etc.)
•Structured hierarchies using DUNS number
will help you see the full picture of an
organization and where you penetrate.
•Define ‘must have’ and ‘nice to have’
attributes that define your records.
•Matching and cleansing with trusted external
sources is paramount.
•On-going process that requires a strategy; data
goes bad over time – use automated cleansing
routines (off-line even if large data sets are involved)
•Work with stewardship team to divide and conquer
•Define roles for governance team
•Define the company’s data assets
•Determine integration points and
paths- make sure to identify which
systems are masters
•Identify if there are any legal,
compliance or security restrictive
actions
•Data should be reportable
•You should be able to
track inbound data
•It’s difficult to manage
what you cannot measure

Portfolio Assessment
• Catalog Applications First
– Identify core functionality –what is the value to the organization?
– Identify current state health and wellness
– Identify on-going financial costs (licensing, support, etc.)
• Identify Consumers, Users, Stakeholders
– What people use the application, how many are there?
– Who and how many support resources are needed?
– Who and how many development/IT resources are needed?
• Create Optimum Future State Direction
– Even if you can’t do it for a while, include it for consideration
What exactly does this mean- what to include?

General Functionality Data Support / Operational
Questions To Aide In Your Analysis
These critical areas should also be cataloged when evaluating your portfolio
• What area of your businessdoes the
application currentlyprovide valuefor?
• How long has it been since a significant
update was made to the application’s
capabilities?
• Is the applicationneeded for a regulatory,
legalor compliancepurpose?
• Does the data in the application provide
criticalinformation that stillmeets your
businessgrowth needs?
• How do new records get added, how are
records updated?
• Can you extract data easily?
• Does data from the applicationfeed into
another system – does it receive data from
another system?
• Wouldyou need to provide additionalsupport
to users if changeswere made?
• Is it currentlydifficult to obtainresourcesto
provide support?
• Is the applicationevolving and getting
updates, or is it staying still?
• Does the applicationcontain highlysensitive
or proprietary information?

Governance- DMBOK (Data Management Body
Of Knowledge)

Data Governance – At The Organizational Level
• Board
Direction
• Owners
• Enterprise Architecture
• Change Management
Governance
• Data Quality
• Master and Reference Data
• Reporting and Analytics
• Data Management/Architecture
• Models and Meta-Data
Management
• Working Groups (SMEs)
• Domain Working Groups (SMEs)
• Business Intelligence
• Repository / ETL Tools
• Enterprise Applications (Security, Compliance, Risk Management,
Lifecycle Management)
Teams
• Stewards- Quality Analysts
• Stewards- Custodians
• Business Analysts – Providers
• Architects
• Modelers & Analysts
Roles

Governance Structure – Processes and Activities
Governance Committee
Council & Organization
Terms and Definitions
Working Groups
Alignment Liaison
Roles and Responsibilities
Owners
Stewards
Custodians
Data Governance Office
Data Management
Policies and Processes
Principles
Policies
Standards
Processes
Program
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting and Assurance
Performance
Measurement
Continuous Improvement
Evidence Repository
Communications

Governance Structure – People (Organizational Areas)
Governance Committee
Sales
Customer Management
Lead to Opportunity
Quoting / Ordering
Sales Operations
Price Books /
Pricing Strategy
Pipeline Reporting
Territory Planning
Data Acquisition
Marketing
Lead
Generation
Content /
Publishing
Events
Branding
Marketing Ops
Data Acquisition
Event Planning
Event Reporting
Reporting/
Analytics
Customer Intel
Information Systems
Data Model
Data Quality
Analytics
Support
Application
Development
Executive
Strategic Vision
Product Direction
Services Direction
Organizational Growth
Funding

Meet Your Governance Team – Data Custodians
Qualities for Data Custodians
Understand the importance and criticality of the data assets in their purview. They are often super users or core SMEs and
can articulate details to others of ‘how things work’.
Social/Personal:
Work with average end users to correct data, and or make recommendations to prohibit recurrence of problems
Ability to converse with data stewards to effectively problem solve
Are collaborative- can work with others to ensure buy in and effective change
Technical:
Understand high level data model; can define and potentially write reports for data assets they understand, but they may
not be super technical (don’t expect them to be developers).
Not afraid to get their hands dirty (updating data when necessary)
YOU WILL NOT GET A CAPE, TROPHY, RAISE OR PUBLIC RECOGNITION
AS A DATA CUSTODIAN

Meet Your Governance Team – Data Stewards
Qualities for Data Stewards
Understand that Data Stewards are those business people within your company that can provide the knowledge behind
many of your applications. They often know the reasons ‘why’ things are done ‘that way’.
Social/Personal:
They work well with end users (correcting data, isolating problem areas) as well as with custodians and other stewards and
executives. Great data stewards are good communicators, but are even better facilitators.
Ability to converse with mid-line management on potential risk factors
Technical:
Usually you will not be getting your hands dirty, however may need to be involved in helping data custodians validate
changes and prioritize elements as determined by the governance board.
Data Stewards should be knowledgeable about basic report writing and understanding complex models in the organization.
YOU WILL NOT GET A CAPE, TROPHY, RAISE OR PUBLIC RECOGNITION
AS A DATA STEWARD

How Do The Teams Work Together?
Most conflicts are resolved at the operational level, however when additional guidance is
needed, it’s recommended to have the ‘council’ assist.

High Level Plan To Get You Started
Planning
• Identify Stakeholders
• Identify Goals,
Objectives, Vision &
Drivers
• Complete Application
Portfolio Catalog
• Create Business Case
for Change
Baseline & Target
• Define Data Policies,
Standards & Org
Structure & Roles
• Define Governance
Process
• Establish Current
Maturity Level & Target
Maturity Level
• Check Baseline Against
Target
• Review and Confirm
Data Architecture
Roadmap
• Identify and Prioritize
Projects & Activities
• Develop Data
Management Roadmap
• Conduct Gap Analysis
• Establish Rollout Plan
and Organizational
Readiness (Impact)
Rollout
• Fill Governance Roles
and Socialize
Structures
• Develop Policies and
Standards
• Train and
Communicate
Throughout The
Organization
• Monitor Performance
and Obtain Feedback
•Business Case For Change
•Data Strategy
•Data Principles
•Terms of Reference
•Role Definitions
•Data Management Roadmap
•Gap Analysis
•Organization Readiness
•Rollout Plan
•Feedback Processes
•Communication & Training Plan(s)
•Standards and Policies
•Data Governance Maturity
Assessment
•Conceptual Models
•Process Definitions
Deliverables for Your Governance Plan

Moving from Talking to Doing

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

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

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

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

Let’s Talk About Data Quality

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

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

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

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

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.

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.

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.

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

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.

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

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

Customer Feedback Forum
Best Practices

Data Quality Best Practices
Tips and Tricks for Improving Data Quality
Data Quality Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3
Duplicates from multiple systems (like
SAP)
Duplicates in the existing data bases:
Recommend an analysis and de-dupe
project to get to a baseline. Recommend
Demand Tools as product to assist in
ongoing
Duplicates from incoming systems
(ERP, etc..): Need to work with
these system owners to identify the
common key to use for prevention
Lead source creating duplicates:
Determine lead process and use of
the lead object in conjunction with a
baseline de-dupe
Need control points to evaluate data
accuracy and quality
Identify the specific fields for your
organization that are of value (by object);
write reports to ascertain where the data
is non-standard or missing, then have
validation rules created to maximize the
entry going forward.
Be a Formula Ninja to help with Data
Quality:
http://www.youtube.com/watch?v=r
1T767LzrZY#t=2377
Lack of data quality record delete
strategy
Archiving policy should be established and
communicated. It should consider all
existing records with/without transactions
and treament should be defined
accordingly based on business processes
Auto delete within 60 days if no
activity- record retention
No good definition of an account today
Organizations should standardize on the
definition of an account with a 3rd party
referential data source like D&B
Don’t try to recreate your ERP in
your CRM . ERP's handle many
complex relationships. Determine
what is required in CRM and port
data accordingly
Salesforce is a dumping ground for new
data
Leverage Lead Object to filter out the
garbage. Potentially do an offline cleanup
first and the build out a process to
manage ongoing

Compliance and Administration Best Practices
Tips and Tricks for Improving CRM Compliance, Security and Adherence
Compliance Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3
How to determine and manage
centralized and decentralized
processes
Use delegated administration
Salesforce functions to provide
support for users. Establish
governance team with input from
business units that cross
geographic boundaries to enable
adequate support in end user time
zones
Identify what areas of the business
should be managed and supported
via business specific teams, versus
the overall management of the org
Use Centralized support when
there are 3rd party system
integrations being used,
decentralized when users can be
managed 1 admin to every 400
users
Security best practices for
compliance and regulatory
adherence; profile updates
Structure your governance plan
that dictates the process that
needs to be followed when initially
assigning users to profiles; what
validation needs to be done when
profile updates are necessary, who
should sign off on it from a
functional side and technical side
Identify what data fields are
necessary to be maintained due to
regulatory requirements, or hidden
from certain profiles/users
Compliance of data acquisitions
and routing processes
For any data sets that cannot be
automatically updated within
Salesforce (like D&B information)
assign a data steward that can
periodically run updates and check
for inconsistencies
Always include a data source on
files that are assigned to users-
this will help you identify if source
data is problematic or if internal SF
actions have updated (i.e.
workflow field updates change
values). Turn on activity history
tracking only for critical fields

Compliance and Administration Best Practices
Tips and Tricks for Improving CRM Compliance, Security and Adherence
Compliance Issue Best Practice Tip #1 Best Practice Tip #2
Data acquisition purchases vs. user obtained
and organizational data
Put in place formal process for users to submit
alternate data sources to administrator for
uploading to Salesforce
Establish ROI on all purchased data sources
first- understand what your business benefits
are versus the overall maintenance, care and
feeding of additional data assets. Assign a
SME or data steward to each data asset and
work with them to establish proper
maintenance routines
Centralization issues- one group doing all
updates and data loads
Limiting the number of people who can upload
data is a best practice. Only certified admins
with a clear understanding of the system and
the data being considered should be permitted
to load data
All data loads should be tested and signed off
in a sandbox org prior to loading to production
Opportunities not created until deals close
This is a top down driven approach. Tie to
compensation. Need to do this to create a
pipeline visibility and forecast

Success Metrics Best Practices
Tips and Tricks for Driving Success and User Behavior
Success Metrics Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3
Identifying and defining fields and
metrics for accuracy
Identify where field population
enhances organizational reporting;
define what you need to report on
first, then go back and fill in
missing fields with information
Use consistent pick lists, run
reports for missing information
(eliminate 'unknown')
Create dashboards that can be
scheduled for refresh to show
progress each month
How to deploy a stewardship
model without dedicated resources
or funding
Turn your users into stewards.
Leverage field tracking to track
changes and owners to create an
audit trail for updates and changes
to records. Create field
requirements on all records to
ensure that proper data is entered
at point of creation to avoid
duplicates and shell records
Deploy a de-dupe tool and search
before create to build in data
quality tools that prevent user
abuse and limit the need for a
high level of effort by users
Create an archiving strategy to
assist with currency and quality of
data. Implement a flagging
strategy so that users can denote
records they want to keep. If the
record is not maintained or
updated it can move to a hidden
status prior to archive. Follow
similar steps as outlined in the
data quality section
How do we motivate the right
behavior from sales
In many cases sales and other
departments for that matter are
motivated by 1) Recognition 2)
Compensation 3) Impact to the
business. All three should be
considered when putting a data
governance plan together
Create a hero board that
highlights those that are driving
data improvements. Potentially
leverage a spiff to drive record
completeness and accuracy
Link opportunity ownership to data
accuracy and completeness. An
opportunity requires certain data
points for creation and without an
opportunity a contract cannot be
created nor can a rep be
compensated.

Data Management Best Practices
Tips and Tricks for Improved Data Management
Data Management Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3
Hierarchies- without restrictions
so you can view accounts with
open opptys
Establish what fields you wish to
have in a hierarchy- i.e. legal, by
brand (tradestyle), etc.
Establish and review account
sharing/access rules (i.e. read
only for all users); make sure
every user has a manager in SF
and the manager is active
Using permission sets and role
hierarchies, allow management
team members more visibility into
entire family trees
Blocking account creation for reps
Allow reps to create "shell" or
"propspect" records. Enfore rules
that require a minimum amount of
info for the account to become a
"customer" or allow opportunities
to be created/closed on the
account
Rep requests a record to be
created and request routes to
stewardship team. They create the
account once vetted or use dupe
blocker with DDC on insert
Use dupe blocker with Data.com
on insert to prevent dupes from
being created and drive user
behavior to import records from
Data.com rather than manual
creation
Segmentation: governments,
affiliates, hierarchies; non-
standard categorization; routing,
analysis and validation (territory
assignments)
Apply consistency across the
board with segmentation rules;
make fields required if they are
needed for segmentation purposes
If territory alignment changes
frequently consider using territory
management. This will allow you
the ability to move accounts with
related items to new users
including open opportunities

Data Management Best Practices
Tips and Tricks for Improved Data Management
Data Management Issue Best Practice Tip #1 Best Practice Tip #2
Data performance; data volume management
and handling of data; Eloqua activities (email
views being logged as activities)
Establish effective governance to identify and put in
place clean up policies- i.e. deletion routines when
there is no activity on an account or contact within a
certain period of time (flag, hide, delete); conduct
weekly (or for large orgs- monthly) data exports to
quickly identify records that should be removed. Flag
records with a potential removal date and status. Then
once that date has occurred, hide records (workflow
field update to change record type), and eventually
delete after org export has been done
For Eloqua and marketing system integrations, keep
nurturing and drip activities in those systems and
only pass qualified records back to Salesforce when
thresholds are met
Lead source and categories
Define and segregate leads by inbound sources; every
lead should have a source so you can track metrics on
results when converted
Use lead scoring to help with routing and
governance (i.e if you are missing critical fields,
update lead assignment rules to have sketchy leads
go to an inbound data steward queue first (then
supplement before sending to sales users)
Not using leads in Salesforce
Using leads allows your company to maintain two
separate lists - one for prospective customers and one
for existing customers. You can store your prospects as
leads, and then once a lead becomes qualified, you can
convert it to an account, contact, and, optionally, an
opportunity
Leads are especially useful if your company has two
separate teams - one that handles lead generation
and mass marketing and one that handles sales.
The lead generation team can concentrate their
work on the Leads tab, and the opportunity team
can use the Account, Contact, and Opportunity tabs

Data Management Best Practices
Tips and Tricks for Improved Data Management
Data Management Issue Best Practice Tip #1 Best Practice Tip #2
How to build out hierarchies
D&B upward linkage is a very effective way to
understand account family structures. This
understanding enables effective territory
management, cross sell/upsell opportunities as well
as roll up reporting to understand total
sales/exposure to a particular business
Define and determine the role of a legal hierarchy
vs. an internal company defined hierarchy. Some
customers need to understand both and reflect legal
ownership, as well as the unique view of the account
from the account management perspective.
Marketing automation integration
Marketing automation integration can be handled in
two main manners 1) Bring all records into SFDC,
Clean and enrich, then push applicable data back to
MA tool
2) If a subset of records is desired in Salesforce,
Data.com API's can be utilized to enrich data within
the MA tool. Note licensing restrictions may apply. In
both cases enriched data is critical for lead routing,
scoring and reporting
Need simplified view of customer; too many related
objects; inline dashboards might be good
Ongoing system governance and system audits to
prohibit and weed out unneeded configuration and
technical debt is key to a successful system
MDM team that owns the golden record- not visible
via Salesforce views today
Record visibility is determined via Salesforce sharing
and role hierarchy set-up. Any integration should
keep these rules in mind
Each platform owner owns data - 34 instances of
Salesforce
To enable cross platform visibility, a CDM/MDM
strategy should be considered. Salesforce to
Salesforce integrations as well as consolidation orgs
are considered for these needs. Clean, keyed data is
the foundation to enable such cross-org visibility