Post on 06-Apr-2018
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Best Practices for Data Quality
Salesforce.com Customer SuccessMarch 2009
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Agenda
Business Driver
Best Practices Overview
Importance of Data Quality
Data Quality Management Data Culture, Analyze, Plan, Standardize, Clean & Enrich,
Integrate & Automate, Maintain
Tools and Resources
Additional Information: Data Considerations
De-duping, Merging, Migration, Integrations & Mapping,
Reporting, IDs
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Business Driver
All organizations buy a CRM tool to derive clear
quantitative metrics on their business. Having bad datacauses user frustration, poor adoption, and may lead to
bad decisions due to inaccurate reports/metrics. The
drive to have accurate data for an organization is critical
since it can provide better and accurate visibility to
increase revenue, reduce costs, increase customer
profitability, and usage. It is important to understand Data
Quality Management best practices using Salesforce.
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Best Practices Overview
Every successful implementation of Salesforce shouldhave accurate data quality as a CRM goal. This is the
key in generating the right metrics and truly
understanding your customer. This presentation
touches on all of the aspects of creating andmaintaining good data quality.
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Importance of Data QualityPitfalls of Bad Data
Inaccurate report metrics
Bad information wastes users time and effort
Marketing wastes money and effort pursuing bad prospects
Understanding your customer is impossible
IT wastes time sifting through information and trying to make
sense of it
Operations has difficulty reconciling data against financial and
other backend information User get frustrated, you lose valuable buy-in and adoption
Analysts rate bad data as one of the top 3 reasons for CRM failure
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75% of commercial businessesbelieve that they are losing as muchas 73% of revenue due to poor dataquality
Experian - QASU.S. Business Losing Revenue Through
Poorly Managed Customer Data
Importance of Data QualityThe Cost of Bad Data
75% ofrespondents
41% ofrespondents
Poor data quality costs U.S. businesses more than$600 billion annually
Data Warehousing Institute.
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Data Quality ManagementBest Practices
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Data Quality Management Best Practices
Data Culture Analyze
Plan
Standardize, Clean & Enrich
Integrate & Automate
Maintain
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Installing a Culture of Data Quality
IntroductionAnything goes, adoptionbefore data integrity
AdaptationRecognize usage trends,Adapt standards to reality
StandardizationTrain to common best practices
Reward / RepressionReinforce best practices,with a carrot AND a stick
IntegrationBuild tools to help multidepartment tasks / processes
AutomationMake everybodys job easier,and make the company more efficient
1 2 3
456
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Analyze: Data Profiling
Understand your data sources
Where is everything coming from
Understand your datas weaknesses
Rate your data; consider completeness, accuracy, validity,
relevance, integrity, level of standardization and duplication
Pinpoint your problems and find ways of improving this
Understand your mapping and usage of data
Entity Level Mapping (Account, Opportunity, Contact)
Field Level Mapping (state, city etc)
Dont duplicate information between entities
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Data Quality Analysis Example: Phone Numbers
Not valid
Not standardized
Not complete
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Plan: Data Quality Management Strategy
Create your Data Quality Plan
Identify and Prioritize Goals
Define Reports and Dashboards
Find Sponsors and Owners
Establish Budget Select Tools (i.e. for De-Duplication)
Commit Resources
Create Communication Plan Provide Rewards and Disincentives
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Standardize, Clean & Enrich
Names CompanyName & Address
Identify,Match & Score
Load toSandbox
Find &Replace
1 2 4 5Standardize Cleanse Enrich (Optional) De-dupe Validate
US, U.S, U.S.A -> USAAcme-Widgets-453
Acme Inc HQAcme UK
J. Smith, John Smith 80%
Hot HighCold Low
DataTransformation
Hierarchy Data
Demographics Re-parentChild Records
acme incorp.-> Acme Inc
Account: Division,Opportunity, Contact
NamingConventionsAddresses Merge
Mergers, acquisitions,spin-offs
3
PostalStandards
J. Smith, John Smith ->John Smith
Archiving &Filtering
Validate&Modify
Load toProduction
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Standardize Create naming conventions and data standards and train all users
Enforce standards with validation rules and pick-lists Implement procedures to standardize data before mass-importing
Examples:
Accounts names: Inc vs. Incorp., INC, incorporated; Ltd vs LTD, Limited
Opportunity names: i.e. NameProduct: Acme 250 Tschotchkes
Country/State: use validation to standardize TX vs Texas, USA vs. U.S.
Postal Code: use validation rules for proper format in US/CAN: xxxxx-xxxx
Contact info: use pick lists for roles, titles, department: Marketing vd. Mktg
Look for useful validation rules in Help & Training!
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Cleanse
Cleanse your Data
Correct inaccuracies and inconsistencies
Find and replace bad or missing data
Remove or merge duplicates
Leverage all users to fix data (its their data)
Archive irrelevant and old data
Leverage automated routines/tools
Routinely reconcile Salesforce data against other data points/systems
Prioritize your data control process
Fix high visibility/usage information first (duplicates, addresses, emails)
Fix business specific information next (opportunity types, stages etc)
Remove duplicate fields (dont repeat account info on contact)
Remove irrelevant fields
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Enrich: Data Augmentation
Add missing information from 3rd party sources
Phone, emails, address info, executive contact information,
Company demographics, i.e. SIC, Industry, Revenue,
Employees, Company Overview, Competitors, Fiscal Year
Understand what data would provide additional value Poll your sales and marketing users and see what is needed
Add internally available account intelligence
Order history
Purchasing Pattern
Up-sell opportunity, i.e. products not yet owned
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Integrate
Understand your Masters
Account Master (Unique ID stored on all other systems)
Product Master
Avoid stale and bad information from spreading
Integrated solutions make it easier for users and more reliable for customers
Create links or integrated apps to avoid duplicates in many systems
Use and monitor review dates for key objects, i.e. account plans
Archive or flag old/irrelevant data, i.e. contacts not updated in last x months
Use workflow/approval processes before updating key fields
Create a true 360 view of your customer
Link order entry, fulfillment apps to Salesforce.com
Make some information read only
Use processes like case submission to update account master information
Product Pricing
SFA
IntegrationTools
Internet
Internet
Accounts
DataEnrichment
Internet
DataWarehouse Leads/Oppty
Catapult
IMI
Volume
ViewCentral
Quick Arrow
ViewCentral
Siebel
SAP
Oracle(Custom)
SAP
EAI/Middleware
Tibco, WebMethods (Alcatel)
BizTalk
ETL
Assorted
???
Standardsbased Integration
SOA/WebServices
XML
AcctMasterbasedonlifecycle
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: Five paths to integration success
SalesforceAppExchange
Native
DesktopConnectors
IntegrationPartners DeveloperToolkitsNative ERPConnectors
1 2 3 4 5
A comprehensive family of technologies built on top of the Force.com Web Services API
http://www.eurescom.de/summit2005/logos/sap_logo.jpghttp://www.avcom.com/partners/sun.shtmlhttp://images.google.com/imgres?imgurl=http://www.tiflolibros.com.ar/images/Microsoft%20.NET%20logo%20white.png&imgrefurl=http://www.tiflolibros.com.ar/&h=448&w=698&sz=28&hl=en&start=2&tbnid=GVJUGyzmLyVNiM:&tbnh=89&tbnw=139&prev=/images%3Fq%3D.net%2Blogo%26svnum%3D10%26hl%3Den%26lr%3D%26safe%3Doff8/3/2019 Best Practices for Data Quality
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Automate Salesforce.com partners can help!
Leverage 3rd parties such as D&B, Hoovers and others to periodically import andautomatically update account records
Inside Scoop or other partners to augment and cleanse information
Workflow can help!
Emails requesting missing information automatically sent to owner when a record isincomplete
Force.com can help! Generate your own alerts through the API
Script adds missing information
Script updates erroneous information
Create integration points Account Master/Product Master/Address Masters
Address Cleansing
Keep Relationships automated
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Low Complexity MediumComplexity
High Complexity
Composite Apps Enterprise Mash-ups Rich user interface
ApplicationIntegrationReal-time integrationMulti-step integration Human workflow
Data Integration
Data migration Data replication Bulk Data Transfers
Data CleansingData de-duplication Data assessment
4
Scontrol
Data Management ApplicationsForce.com Appexchange app considerationslist not all encompassing
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Maintain your Data
Data quality decays rapidly & enterprises should follow a methodology thatincludes regular measurement of data quality with goals for improvement &deployment of process improvements & technology
Safeguard your cleansed data and prevent future deterioration
Train
User Training
Naming Conventions
Address Conventions
Dupe. Prevention Process
Data Importing Policies
Required Fields
Default Values
Data Validation Rules
Workflow Field Updates
Web-to-Lead Restrictions
DataQualityDashboards
DataQualityReassessment
AppExchangeTools
Enforce Monitor
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Maintain Data Quality: Enforce
Make sure Data Ownership and Sharing is accurate
Critical to keep data in the right peoples hands
Designate i.e. super user or geography lead to own regional data quality
Make sure your hierarchy, groups, teams etc are kept up to date
Proactively have meetings with management and stakeholders to understand
org changes
Define your CRUD rights on each profile
Give users access rights to only the information they should have
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Improvement Checklist
Do you understand what data you have in Salesforce?
Where is it coming from? What is wrong? What is the business impact?
Have you cleaned your data?
Identify data owners, ensure permissions are up to date (CRUD)
Remove duplicates (manually and through tools or partners)
Have you integrated and automated your data?
Do your applications tie together?
Are you using workflow for notifications? Are validation rules in place?
Have you augmented your data?
Have you added information to help your sales users?
Do you monitor your data? Get the reports, dashboards and automation in place to monitor the health of your data
Do you have a good data quality culture?
Is everyone trained and contributing to your data quality? Do users trust the data?
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Additional Information
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Data Considerations Addressing duplicate records
There will most likely be overlapping/duplicate data
De-dupe either before or after you import the data from one system into the other Prior to importing into master account
Export both data sets, merge into one and identify duplicates
Merge/delete duplicates, import clean file
After importing into master account
Leverage de-dupe tools in salesforce.com
Leverage de-dupe tools from partners (www.salesforce.com/appexchange)
Use a custom field to flag each records source system
Establish controls and processes to minimize dupe creation and to remove dupes on anongoing basis
Consider existing integrations and system of record for your data
Develop rules for merging data
When there are two records for the same entity (i.e., Account), which one wins?
Newest record? Most complete record? Record from one of the databases? Most recentlyupdated?
Determine who will own the records if there are duplicates
Impacts sharing rules, reporting, etc.
Leverage for data cleansing that will ensue
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Data Considerations
Establish plan for migrating data
Determine when master system becomes live/system of record (i.e.,
stop entering data into other system)
Set date when you will extract all data from the system being merged
How long will the merge take? How will you deal with interim data? New
data blackout dates? Temporary data ID? How will you communicate to
users?
Ensure you have a complete copy of both data sets before attempting
any merging just in case!
Note if you have not done this type of work before, it is challenging.
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Data Considerations
Create mapping tables
Every record in Salesforce is assigned a unique 18-digit alpha-
numeric, case sensitive id by salesforce.com
Relationships between records are established based on these IDs
(i.e., Activity related to a Contact)
These IDs will change when you import data from one system to
another, as the system will assign it a new ID
In order to re-create the relationships between records (i.e., import
Activities and associate to the appropriate Contact), you need to
create a mapping table that will allow you to associate the OLDContact ID with the new one
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Data Considerations
Create Mapping Tables (cont.)
Create a temporary/mapping field on each object you will need to map forthe old id (i.e., OLD ACCOUNT ID, LEGACY ID)
Export all your data from the instance to be retired
You can do this via the Weekly Export service, reports, the API, Excel
Connector, AppExchange Data Loader or request a one-time full
extract from customer support
Dont forget about attachments and Documents!
Consider dumping these to a file server with a unique naming strategy and
use Custom Links from the salesforce.com objects to access
When importing the data into the master Account, map the Account Id to
the OLD ACCOUNT ID field
You will then be able to export the new Account Id, OLD ACCOUNT ID and
Account Name to act as your mapping table
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Data Considerations
What if data is inadvertently
Deleted Restore from the Recycle Bin (retained for 30 days)
Restore missing data from backups
Merged
There is no way to un-merge data
Clean up/work with merged records, OR
Delete and restore from back ups
Imported incorrectly
Mass transfer (if you can)
Delete and re-import into proper area
Consider tagging batches with a custom field indicating the load/batchnumber in case you need to reverse
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Advanced Data Quality