best practices for data quality

8/3/2019 Best Practices for Data Quality

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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%3Doff


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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
http://ringlead.com/http://www.trilliumsoftware.com/home/index.aspxhttp://webmethods.com/


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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
http://www.salesforce.com/appexchange/http://www.salesforce.com/appexchange/


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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