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Best Practices for Data Quality


... Analyze, Plan, Standardize, Clean & Enrich, Integrate & Automate, Maintain ... Data Data Data: Start your Spring Cleaning Now. Salesforce Professional Services ... – PowerPoint PPT presentation

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Title: Best Practices for Data Quality

Best Practices for Data Quality
  • Customer Success
  • March 2009

  • 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

Business Driver
  • All organizations buy a CRM tool to derive clear
    quantitative metrics on their business. Having
    bad data causes 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.

Best Practices Overview
  • Every successful implementation of Salesforce
    should have 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 and maintaining good data quality.

Importance of Data QualityPitfalls of Bad Data
  • Inaccurate report metrics
  • Bad information wastes users time and effort
  • Marketing wastes money and effort pursuing bad
  • 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
  • Analysts rate bad data as one of the top 3
    reasons for CRM failure

Importance of Data QualityThe Cost of Bad Data

75 of commercial businesses believe that they
are losing as much as 73 of revenue due to poor
data quality
75 of respondents

Experian - QAS U.S. Business Losing Revenue
Through Poorly Managed Customer Data

41 of respondents
  • Poor data quality costs U.S. businesses more than
    600 billion annually

Data Warehousing Institute.
Data Quality Management Best Practices
Data Quality Management Best Practices
  • Data Culture
  • Analyze
  • Plan
  • Standardize, Clean Enrich
  • Integrate Automate
  • Maintain

Installing a Culture of Data Quality
Introduction Anything goes, adoption before data
Adaptation Recognize usage trends, Adapt
standards to reality
Standardization Train to common  best
Automation Make everybodys job easier, and make
the company more efficient
Reward / Repression Reinforce best
practices, with a carrot AND a stick
Integration Build tools to help multi department
tasks / processes
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
  • Understand your mapping and usage of data
  • Entity Level Mapping (Account, Opportunity,
  • Field Level Mapping (state, city etc)
  • Dont duplicate information between entities

Data Quality Analysis Example Phone Numbers
Not valid
Not complete
Not standardized
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

Standardize, Clean Enrich
Enrich (Optional)
Company Name Address
Identify, Match Score
Load to Sandbox
Find Replace
acme incorp.- Acme Inc
J. Smith, John Smith 80
Hot ? HighCold ? Low
Hierarchy Data
Naming Conventions
Validate Modify
Acme Inc HQ Acme UK
J. Smith, John Smith - John Smith
US, U.S, U.S.A - USA
Data Transformation
Postal Standards
Re-parent Child Records
Load to Production
Mergers, acquisitions, spin-offs
Account Division, Opportunity, Contact
Archiving Filtering
  • Create naming conventions and data standards and
    train all users
  • Enforce standards with validation rules and
  • Implement procedures to standardize data before
  • Examples
  • Accounts names Inc vs. Incorp., INC,
    incorporated Ltd vs LTD, Limited
  • Opportunity names i.e. Name Product 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
  • 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

Enrich Data Augmentation
  • Add missing information from 3rd party sources
  • Phone, emails, address info, executive contact
  • Company demographics, i.e. SIC, Industry,
    Revenue, Employees, Company Overview,
    Competitors, Fiscal Year
  • Understand what data would provide additional
  • 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

  • Understand your Masters
  • Account Master (Unique ID stored on all other
  • 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
  • Make some information read only
  • Use processes like case submission to update
    account master information

Five paths to integration success
A comprehensive family of technologies built on
top of the Web Services API
Integration Partners
Salesforce AppExchange
Developer Toolkits
  • partners can help!
  • Leverage 3rd parties such as DB, Hoovers and
    others to periodically import and automatically
    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 is
  • 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

Data Management
Appexchange app considerations list not all
Data Quality Management Best PracticesNative
tools for managing data quality
Excel Connector
Data Loader
Analyze and cleanse data
Leverage tools to prevent duplicates before
passing to Salesforce real-time
Import data from various file sources
Data Quality Analytics
Use reports and dashboards to measure data quality
Use Validation Rules and Workflow
Maintain your Data
Safeguard your cleansed data and prevent future
  • 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
  • Data Quality Dashboards
  • Data Quality Reassessment
  • AppExchange Tools

Data quality decays rapidly enterprises should
follow a methodology that includes regular
measurement of data quality with goals for
improvement deployment of process improvements

Maintain Data Quality Train and Communicate
  • Users are trained that data integrity is a
    collective responsibility
  • Users are trained on how data will be used
    (establish reasons for why data needs to be clean
    and accurate)
  • Communicate data quality goals and progress
  • Communicate policies and procedures
  • Data is always changing so Data Quality processes
    are on-ongoing!

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

Maintain Data Quality Monitor
  • Use Reports and Dashboards to monitor and
    identify erroneous/missing data
  • Data Quality owners spot check and monitor data
    on a regular basis
  • Create Alerts and workflow to monitor data
  • Define centralized processes for mass loads
  • Implement Procedures and Policies
  • Enlist everyone and hold them accountable
  • Exception reports run monthly to find incomplete
    records or records with incorrect pick list

Improvement Checklist
  • Do you understand what data you have in
  • 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
  • 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
  • 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?

Tools Resources
  • AppExchange - Data Quality tools and offerings
  • Data Quality Analysis Dashboards
  • Integration Data Management
  • Data Cleansing
  • De-duplication Tools - Search term Data
  • Data Tools
  • Apex Data Loader and Excel Connector
  • Dreamforce Data Quality Sessions
  • Data, Data Everywhere
  • No More Bad Data
  • Wrangle Data Pump up the Configuration
  • Turning Around your Data Quality Dilemma,
  • Data Data Data Start your Spring Cleaning Now
  • Salesforce Professional Services
  • Data Quality Assessment and Cleansing Solutions

Thank You
Additional Information
Data Considerations
  • Addressing duplicate records
  • There will most likely be overlapping/duplicate
  • 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
  • Leverage de-dupe tools from partners
  • Use a custom field to flag each records source
  • Establish controls and processes to minimize dupe
    creation and to remove dupes on an ongoing 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 recently updated?
  • Determine who will own the records if there are
  • Impacts sharing rules, reporting, etc.
  • Leverage for data cleansing that will ensue

Data Considerations
  • Establish plan for migrating data
  • Determine when master system becomes live/system
    of record (i.e., stop entering data into other
  • 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
  • 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.

Data Considerations
  • Create mapping tables
  • Every record in Salesforce is assigned a unique
    18-digit alpha-numeric, case sensitive id by
  • Relationships between records are established
    based on these IDs (i.e., Activity related to a
  • 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 OLD Contact ID with the new one

Data Considerations
  • Create Mapping Tables (cont.)
  • Create a temporary/mapping field on each object
    you will need to map for the old id (i.e., OLD
  • Export all your data from the instance to be
  • 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 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

Data Considerations
  • Created Dates
  • All records imported/migrated will have a Created
    Date to when the import occurs
  • To retain original dates, create a custom field
    to import into (i.e., Original Create Date)
  • If you are updating via the API, the new 7.0
    version will allow you to set the Created and
    Last Modified Dates http//
  • Note You must contact Salesforce support to
    enable this feature.
  • History Tables
  • Stage History for Opportunities / Case History
    for Cases
  • Data cannot be migrated into these tables, this
    information must be stored elsewhere if you bring
    it over (Note field is not Reportable, so
    custom field is recommended)
  • Unique Ids (system generated)
  • Record Ids are unique and cannot be imported
  • Imported records are assigned new Id, it is a
    good idea to import the old Id into a custom
    field for mapping purposes
  • Features that reference (i.e., Custom Links)
    unique ids of other objects (i.e., a report) must
    also be updated

Data Considerations
  • Reports
  • When reporting on migrated data, date filters
    must take into account standard and custom date
    fields (i.e., Create Date and Original Create
  • Other filters on existing reports must be
    reviewed to ensure they are still relevant/apply
    to all data
  • Record Types (EE/UE only)
  • If one of the instances leverages
    record types, all records added from the other
    instance must be assigned a Record Type
  • Record Types can be updated through the API, not
    through the import wizard
  • Record Type assignment must also be aligned with
    user Profiles

Data Considerations
  • What if data is inadvertently
  • Deleted
  • Restore from the Recycle Bin (retained for 30
  • 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/batch number in case you need
    to reverse

Advanced Data Quality
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