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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Who Do We Have Here Today?

Who Owns Data in Your Organization?

Sales Marketing IT

Support Data Operations

Sales Operations

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

Greg Malpass Founder and CEO – Traction on Demand

• 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

• 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

Let’s talk about data quality

What Challenges are You Facing Today?

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

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90% Incomplete

74% Need Updates

21% Dead

15+% Duplicate

20% Useless

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

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

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

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

Guiding Principles

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

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

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

Tactical Examples

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