Data Data Everywhere!

Matthew Evans, Tribune Media Services

Erica Stowe, R. L. Polk

Deborah Sanford, salesforce.com

Kevin Smith, salesforce.com

Admin I: Getting Started

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Data Data Everywhere

Why is Data Quality important?

How to assess CRM Data Quality

Standardizing and Cleansing Data

Improving and Protecting Data

How to Get Started

Q&A

Erica Stowe

CRM Process Manager

erica_stowe@polk.com

We gather automotive data, compile it, analyze it and provide intelligence to the automotive market.

• INDUSTRY: Automotive

• EMPLOYEES: 1,300

• GEOGRAPHY: Global

• # USERS: 415

• Login 85%

• PRODUCT(S) USED: Salesforce SFA & Service & Support, 16 Custom

Objects, 4 Custom Apps, 4 S-Controls, 2 AppExchange Applications

Why is data quality important?

Poor Data Quality Turns Into:

Inaccurate Reporting

Time Wasted

Lost Money Pursuing Bad Information

Kills Users Adoption

Why is Data Quality Important?

R. L. Polk did not have a data quality strategy and decided to run one direct mail campaign using the data in their CRM tool. Here was the impact to R. L. Polk’s bottom line…

The total cost of poor data quality to any marketing program cannot be fully quantified but can easily run 25% or more!

4,000 Fulfillment Packages $80,000

500 Bad Addresses $10,000

1,000 Duplicates Time spent

500 Pricing Errors Due to Account

Hierarchy Issues

$ 10,000

Minimum Cost of Poor Data $20,000

Data Quality – Key Challenges

People Challenges• All users had the ability to add data to every object• No standards or guidelines on how data was to be entered

Process Challenges No required fields No field dependencies No validation rules No address validation No account hierarchy verification

Technology Challenges• No integration between systems or processes

Data Quality – The Solution

How did we address these challenges?• Required Fields

• Field Dependencies

• Validation Rules

• Visibility into the data

R. L. Polk has over a million records in Salesforce. Here are some highlights:

200,000 - Opportunities 100,000 - Contacts

85,000 - Cases 60,000 - Accounts 25,000 - Leads 3,500 - CARS 1,000 - Forecasts

• External Data Validation

• CRMfusion Data Therapist

• Processes & Guidelines

• Training

Data Quality – Results

What were the results?

Cost of poor data dropped from 25% to 3% for most campaigns

Improved forecast turnaround time from 1 week to real-time

Increased sales pipeline visibility from 60% to 95%

Increased management visibility into competitive losses

• Gathering data earlier within our process reduced the need to

re-key data down stream

• Year over year customer satisfaction increase

• CAR Process

R. L. Polk’s Data Strategy Today

Our Forecast is distributed every Monday

Reports, Dashboards, Escalation Rules

De-Duplication Tools; Demand Tools by CRM

Fusion

Data is shared with every level of management

within our company

Data Data Everywhere

Why is Data Quality important?

How to assess CRM Data Quality

Standardizing and Cleansing Data

Improving and Protecting Data

How to Get Started

Q&A

How to Assess a CRM Implementation’s Data Quality

Reports and Dashboards

De-Duplication Tools

Survey Your Users

Profile the Data

Analyze the Results

Data Quality Reports and Dashboards

Understand the Important Fields of Every Entity

Create Exceptions Reports for Each Entity

Create Data Quality Dashboards by Role

Scoring the Data Quality of Each Record

Leverage custom formula fields to score each record for data completeness. Sample Formula:

“IF( ISPICKVAL(Industry,""), 0,20) + IF( ISPICKVAL(Rating,""), 0,20) + IF( LEN(BillingCity) = 0, 0,20) + IF(LEN(Phone) = 0, 0,20) +

IF( ISPICKVAL(Type,""), 0,20)”

Use the Data Quality Dashboard on the AppExchange as a starting point:

Data Quality Analysis Dashboards 1.0

Demo Data Quality Scoring and Dashboards

Data Data Everywhere

Why is Data Quality important?

How to assess CRM Data Quality

Standardizing and Cleansing Data

Improving and Protecting Data

How to Get Started

Q&A

Matthew Evans

CRM Project Manager

mdevans@tribune.com

The AdminExchange

www.adminexchange.wordpress.com

• INDUSTRY: Media

• EMPLOYEES: 610

• GEOGRAPHY: Global

• # USERS: 40 Currently, 200+ by 2007 year end

• Login 82%

• PRODUCT(S) USED: Salesforce SFA & Service & Support, 7 Custom

Objects, 2 downloaded AppExchange applications

Tribune Media Services, a subsidiary of Tribune Company, is a leading provider of information and entertainment products for print, electronic and on-air media.

Standardizing & Cleansing Data

Names Company Name & Address

Identify, Match & Score

Load to Sandbox

Find & Replace

1 2 4 5

StandardizeStandardize CleanseCleanse Enrich (Optional)Enrich (Optional) De-dupeDe-dupe ValidateValidate

US, U.S, U.S.A -> USA Acme-Widgets-453

Acme Inc HQAcme UK

J. Smith, John Smith – 80%

Hot HighCold Low

Data Transformation

Hierarchy Data

Demographics Re-parent Child Records

acme incorp.-> Acme Inc

Account: Division, Opportunity, Contact

Naming Conventions

Addresses Merge

Mergers, acquisitions, spin-offs

3

Postal Standards

J. Smith, John Smith -> John Smith

Archiving & Filtering

Validate & Modify

Load to Production

Tribune Problem – Bad Data!

Too many records

23,000 Accounts for client base of 3,000

Duplicates

Unused fields

Asking for redundant data

Fields never used

Bad naming of records

Multiple opportunities and accounts with the same name

No standardization of field inputs

Billing State: CA, Calif., California, Cal.

Pre-Define Data Management

Standardize and Cleanse Correct inaccuracies and inconsistencies in data

Make sure data ownership and sharing is accurate

Define your CRUD rights on each profile

Augment Add missing information from 3rd party DB’s

Understand what data would provide additional value

Add behavioral specific data

Integrate Understand your masters

Avoid stale information, misinformation from spreading

Create a true “360” view of your customer

Make some information read only

Tribune Game Plan Cleanse

Delete old/unused Accounts

Use Demand Tools to clean duplicates

Survey Users to remove bad fields

Standardize and Enforce Create naming standards for Accounts and Opportunities

Enforce naming standards and field inputs with validation rules and workflow rules

Train Users on new standardizations

Monitor Use the Adoptions Dashboards from AppExchange to track

data quality

Tribune Results Reduced number of workable

accounts to 8,000 3,000 Clients; 5,000 Prospects

Eliminated duplicates 2,500 duplicate accounts 1,500 duplicate contacts

Removed 15 unused fields from 4 standard objects Trained users on cleaner Salesforce and introduced

naming standards Created 16 validation rules and 6 field update workflows to

standardize field inputs Customized data quality reports and dashboards to meet

our needs

Data Data Everywhere

Why is Data Quality important?

How to assess CRM Data Quality

Standardizing and Cleansing Data

Improving and Protecting Data

How to Get Started

Q&A

Data Protect

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

Safeguard your cleansed data and prevent future deterioration

TrainTrain

•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

EnforceEnforce MonitorMonitor

Data Data Everywhere

Why is Data Quality important?

How to assess CRM Data Quality

Standardizing and Cleansing Data

Improving and Protecting Data

How to Get Started

Q&A

How to get Started

How to apply what you’ve learned when you get home

• Get Data Quality Dashboards from AppExchange

• (and/or User Adoption Dashboards)

• Survey your users

• Review available Data Quality tools and offerings

• Create Data Quality project plan

• Review this presentation

• Utilize Salesforce Data Quality Assessment and Cleansing

Solutions

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Question and Answers