Post on 08-Jul-2015
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