Join us for an interactive discussion with trailblazers to understand how simple concepts such as naming standards and validation rules play a critical role in driving quality as well as your return from data. We will also discuss best practices for using Salesforce functionality and tools that impact effective data management.

1. Become a Data Management Rockstar

2. Be a Salesforce Success Cloud
Trailblazer
Learn more at salesforce.com/successcloud

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4. Agenda
Why Data Management Matters
4-step Framework for Effective
Data Management
Interactive
Learning/Discussion
Recap & Wrap-Up

5. Game Plan
Share your successes
Ask questions
Succeed together

6. ​Data quality
problems
cause inefficiencies
Customer Data Gap Creates Real Costs for Organizations
​20
+
% user time
consumed doing
research
​#1 tech issue with
CRM is consolidating
customer data
​Data Quality Problems Cause Inefficiencies
Approximately
20% Useless,
90% Incomplete,
21% Dead,
15% Duplicate…
Need for more
people, delays
in closing sales,
service tickets
Reduces value
of investment,
potential for
future benefits

7. Why data quality matters
​1 in 10
companies rate their data
quality as excellent
​Business costs of poor
data quality may be
up to 25%
of organization’s revenue
​$3 Trillion
cost of poor data quality
for US economy each year
​Up to 50%
of typical IT budget
is spent on scrap and
rework
Source: Harvard Business Review,
Gartner, TDWI, Lemonly.com

8. What Brought You to This Circle Today?
​Common topic questions customers ask. What are yours?
What tools are available
for data management?
How should I
consolidate data from
multiple sources?
Do I need a data backup strategy?
How is poor data quality
impacting my business?
How do I maintain high quality
data to support my business?
How do I ensure
adherence to established
data standards & business rules?

9. Identify
the focus area
Understand data
management building
blocks
Identify and prioritize
areas of focus
Identify kpis & metrics
Four Steps for Effective Data Management
Evaluate your
data needs
Assess data & process
health, identify gaps
Establish a plan to
address gaps identified
Execute plan
Access, plan
and execute
Refine metrics and KPIs
Evaluate & enhance
dashboards & reports
used to for metrics
Continuously monitor and
take mitigation actions as
needed
Monitor
and maintain
Identify critical business
processes
Understand data needs
for each business process
Identify data sources and
owners

10. Data Management Framework - Major Building Blocks
• Align information strategy with business
priorities and goals
• Establish data metrics and KPIs early and
establish linkages to financial measures
• Enlist business users as data stewards
(tribal stewardship)
• Security considerations for data classification
are critical
• Have a conservative plan for data capacity to
address retention and archival requirements
Information
Governance
Enterprise
Metrics & KPIs
Information
Security
Master Data
Management
Metadata
Management
Transactional
Data
Management
Big Data
Management
Data Quality
Management
Data Integration
Information
Dissemination
(BI, Reporting,
Analytics)
Identity &
Access
Management
Data Retention
& Archival
Information Strategy

11. Legacy Systems, Disparate Data Sources
Legacy systems integration
Data dispersed across
business units
Inability to personalize
customer service
​Personalized Service
​Seamless Experience
​Efficient Support

12. Enabling Single View of Truth with Master Data Management
​Siloed context for business transactions Enterprise context for business transactions
MDM provides a consistent context for consolidating data
From a Departmental View …... To an Enterprise View
Operations Sales Manufacturing
Operations
Data
Sales Data Manufacturing
Data
Operations Sales Manufacturing
Operations
Data Sales Data
Manufacturing
Data
MDM
Customer
Product
Customer
Product
BOM
Product
Customer Product

13. Processes that can impact your data in Salesforce
Data
transformations
Data cleansing,
standardization,
normalization
Data retirement/
purging
Data ingestion
processes and events:
Data conversion
/migration
System consolidation/
retirement
Manual data entry
Batch jobs
Real-Time interfaces
Internal processes that may change original data
Processes or events
resulting in data decay:
Changes/versions
not captured
System upgrades
New use cases
Lack of
appropriate skills
Process automation
Complete
Timely
Accurate
Relevant

14. How do I Administer Data Quality?
A process framework to ensure clean data
Incoming Record/Standardized & Normalized
3220 South Adams St., Tallahassee, FL 32301-9998 USA
Analyze data and establish its
statistical signature (e.g. frequency
counts compared against
benchmarks, range checks, formats
etc.). Group attributes based on
established cleanliness thresholds.
Cleanse anomalies
identified in the profile
step. Inputs are data
attributes in the
suspect group.
Standardize and
normalize to optimize
matching results. May
involve structured,
semi-structured and
unstructured data.
Monitor data health through
well established governance
process and controls (data
stewardship). Data quality
dashboards, Data Steward
UIs and reports are some of
the tools used to monitor DQ
Identify duplicates that may
span multiple sources
through deterministic or
probabilistic techniques.
Create golden record
through merge process (data
survivorship)
Cleanse
Standardize
Match
and Merge
Profile
Monitor
Data
Quality

15. How do I Administer Data Quality?
A process framework to ensure clean data
Incoming Record/Standardized & Normalized
3220 South Adams St., Tallahassee, FL 32301-9998 USA
Analyze data and establish its
statistical signature (e.g. frequency
counts compared against
benchmarks, range checks, formats
etc.). Group attributes based on
established cleanliness thresholds.
Cleanse anomalies
identified in the profile
step. Inputs are data
attributes in the
suspect group.
Standardize and
normalize to optimize
matching results. May
involve structured,
semi-structured and
unstructured data.
Monitor data health through
well established governance
process and controls (data
stewardship). Data quality
dashboards, Data Steward
UIs and reports are some of
the tools used to monitor DQ
Identify duplicates that may
span multiple sources
through deterministic or
probabilistic techniques.
Create golden record
through merge process (data
survivorship)
Cleanse
Standardize
Match
and Merge
Profile
Monitor
Data Quality

16. Data Profiling: Key Considerations
Do not boil the ocean – align data domains with
consuming business processes
Identify relevant data sources
Identify representative data from source systems
• Volume
• Grain
• Scope (determined by
consuming business processes)
​
​Prevention is better than a cure
Extract
Source DB
Source DB Data
Staging
Data Profiling
Flat
Files
Data groups
based on initial
health assessment
Good Data
Bad Data
Group sample records according to their health
assessment (e.g. “good,” “bad” data groups)
Select technology enabler(s) for establishing
statistical signature of source data (profiling) as
well as discovery of relationships between data
elements within and across data sources
Integrate data profiling with data stewardship to
iteratively improve DQ controls at the point of entry

17. Data Cleansing: Key Considerations
Enforce rules established in profiling phase by addressing as many data inconsistencies
as possible and making updates to data sample groups (“good”, “bad”)
Focus on data anomalies related to formatting issues (e.g. date format), illegal values (e.g.
alphabet when numeric value is expected) etc.
Continue iteratively updating “good” and “bad” data files with corrections until “bad” record count is
in “acceptable” range (establish a Trust Score)
Data stewardship can play a major role in identifying business rules early to help with the initial
cleansing step
​Catch anomalies early
Extract
Source DB
Source DB Data
Staging
Data Profiling
Flat
Files
Good Data
Bad Data
Data
Cleansing
Good Data
Bad Data

18. Data Standardization: Typical Focus Areas
​Legal Form
Generally part of Account Name
Separate from actual Company Name
Examples: Ltd., LLC, LLP, Limited, Corp. etc.
Last word is extracted from end of Company name (e.g. DFC LTD)
to improve matching performance
​Country
Country field on Account, Contact and Lead
Translation to Country ISO Codes lookups
(e.g. United States, US, America -> US)
​Domain
Domain extracted from website address (e.g. www.businessinsights.co.uk)
Used to improve consistency during fuzzy matching
​What to normalize

19. Deterministic Matching
involves exact comparison of data
elements. Scores are assigned at
the field and record level
Probabilistic Matching
involves likelihood of occurrence,
phonetic encoding, leveraging
statistical theory. Scores are
assigned as percentages indicating
the probability
of match
Match & Merge: Key Considerations
Identify duplicates using deterministic or probabilistic matching
techniques
Determine which data elements to consider from duplicated
data (single or multiple sources) for consolidation
Build intelligent data survivorship rules to automate the merge
process by rule-based selection of winning data elements from
duplicate records
Ensure data dependencies are accounted for “re-parenting”
(during merge operation)
Rules should be comprehensive to support data stewardship
function
​Deciding who should survive the battle of duplicates

20. Data Governance Best Practices
Data standards are understandable, sensible and easily accessible
Embed Data Governance/Data Management professionals in the field (e.g. Development teams)
Educate developers in data management practices (What? Why?)
Implement data governance processes and policies in smaller pieces (e.g. focusing on a single
data domain such as Customer), learning from and adapting the approach with each segment
Data centric projects must be business
driven delivering tangible business value
Embed compliance activities in day-to-day processes to avoid expensive post process reviews
Extend data stewardship to include key business users who are consumers of outcomes from a
given process or a segment of the process chain (tribal stewardship)
Doing things right means doing the right things

21. Data Backup & Archiving: Key Considerations
Categorize data based on current storage license, levels of protection needed, frequency of
use, performance requirements, currency requirements and regulatory mandates.
Understand backup options available on platform and through SF partners
Evaluate tier 2 storage options if necessary to maintain a manageable data footprint on
platform that conforms to storage licensing and performance requirements
Evaluate opportunities for aggregating data to reduce data footprint on platform
Assess integration patterns for full backup, incremental backup and partial backup
​
Getting the most out of your Salesforce licenses

22. AppExchange Tools for Your Data Management Needs
Master Data Management
​Informatica C360
Consolidate data from multiple
sources, manage complex
hierarchies, enrich data using 3rd
party data providers
​DemandTools
Administrator productivity suite to
control, standardize, and
de-duplicate
Duplicate Management
Ringlead Unique Upload
Import duplicate-free lists to
complement existing tools
Cloudingo
Identify and remove duplicate
using a dashboard-based tool
Plauti B.V. – Duplicate Check
Mass de-duplication, fuzzy
matching, duplicate prevention
​Data Quality
Data.com Assessment App
Free app (does not require Data.com
Clean) to understand data health
Experian Data Quality Grader
Free app to quickly evaluate
and rate data quality
DQ Anaysis Dashboards
Free app to expose DQ health across
key dimensions (completeness,
accuracy, integrity etc.)
​Data loaders and mass edits
Thousands of additional FREE and Paid apps available. 3.5+ Million Installs.

23. Account: Based on industry,
rating, type, phone and
complete address details
Contact: Based on phone,
e-mail address, title, salutation
and complete address details
Opportunity: Based on type,
closed date, amount, lead
source and next steps
Expose average data quality
score by owners (account,
contact, opportunity)
Monitor Key Data Quality Metrics
Use dashboards and alerts to recognize problems & results, mitigate risk

24. We Help You Navigate the Wealth of Salesforce Resources
Adoption Webinars
Live interactive sessions with
adoption experts
Basic Tutorials
How-to videos
Circles of Success
Small group best practice sessions
with customers and Salesforce
experts
Accelerators
Deliver customer-defined business outcomes
Premier Community
Exclusive community content
On Demand Training Catalog
Self-paced learning for users and admins
Premier Success Plan Customers
Lifelong
Success
Plan
Extend
Prepare
Get
StartedAdopt
Getting Started
Resources
Videos, in-app walkthroughs, and
webinars to get you started right
Community
Collaboration with application
experts
Workbook
Step-by-step guide to plan your
implementation

25. Premier Success Drives Salesforce ROI
​Reported increase over Standard Success Plan customers
The ROI is based on a customer survey conducted by independent, third-party Market Tools.
All other metrics are based on Premier customer metadata.
130%
more process
automation
138%
more analytic
insights
80%
higher
ROI
52%
higher user
adoption
61%
faster
deployment
Enhanced Training
Success Resources
Enhanced Support

26. Target Resources to Help You
​Trying to get started or achieve more? We have resources for your success!
Journey Resources
Get the Basics
How to prepare, import and manage
your data
Go Further
Discover how to identify and
manage duplicate records.
Discuss
Get advice and answers from
Salesforce experts and customers.
For more best practices resources,
visit Improve Data Quality
Premier Resources
Online Training*
• Get the Most out of your
Data
• Enable the User
Experience with Data
Accelerators*
• Customer Data Master
Harmonization
• Salesforce Data Quality
Management

27. What’s Next? Book an Accelerator!
Get Expert Help With Data Management
Salesforce.com/Accelerators
Accelerators are available to customers with Premier/+ or Signature
Success Plans. Other terms may apply.
1-on-1 Consultation with a Certified Cloud Specialist.
Recommended:
● Customer Data Master Harmonization
● Salesforce Data Backup and Management Quickstart
● Salesforce Data Quality Management
● Prevent Duplicate Records
Contact Your AE or Success Manager

28. Wrap-Up

29. Welcome to the Trailblazer Community
Engage directly with Salesforce experts.
Hear from MVPs and other customers.
Access all you need to achieve success:
• Content and resources
• Circles of Success and webinars
​Join the conversation
Release
Readiness
Getting
Started
Lightning
Now
Premier
Central
Access the Trailblazer
Community today!
salesforce.com/success