Data Quality Survey Trends & Perceptions

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Data Quality Survey Trends & Perceptions

Wednesday, September 22, 2010

Teleconference:

Dial-in: 1-866-237-3252

Passcode: 900499

© Experian Limited 2008. All rights reserved.

Confidential and proprietary. 2

Welcome!Introductions and Overview of Today’s Session

Experian QAS reviews August 2010 data quality survey

Research findings

Key trends and perceptions

Tips for improving data quality in your organization

Today’s speaker:

Courtney Fulton

Marketing Programs Manager, Experian QAS



Why did we do this survey?

To answer questions like . . .

How do organizations prioritize data quality among other strategic initiatives?

Are there common database issues that all organizations face? Do these common issues vary by industry?

How do most organizations measure data quality?

Are businesses assessing the impact of data quality on marketing initiatives, and therefore, marketing budget?

Who typically holds responsibility for improving and ensuring data?



What was the research methodology?

August 2010

300 respondents from the United States

Produced by pureprofile, an online marketing research firm

3 industries surveyed:

Banking

Insurance

Retail

Company size from 50 employees to 1,000+ employees

Titles included CEOs, CIOs, directors and managers connected with data management



Data quality is top of mind for finance and insurance industries

84% of all respondents said that they plan to invest or should plan to invest in data quality initiatives over the next 12 months

Banks are almost unanimous on this point – 90% of bank respondents chose this option

Insurers are planning a high level of investment in data quality initiatives as well, at 87%



Documented strategy essential for banks

Banks are more likely than insurers to document contact data management strategies

83% of bank respondents have or are currently working on this type of strategy

67% of insurance organizations have or are currently working on a documented contact data management strategy

50

60

70

80

90

Perc

en

tag

e

Banks Insurers Retailers



Why is contact data quality important?

Overall, survey respondents identified 4 key reasons for maintaining the quality of contact data:

1. Enhance customer satisfaction

2. Increase efficiency

3. Save costs

4. Capitalize on market opportunities through customer profiling



Looking at responses from banks and insurers, the reasons for maintaining contact data quality were prioritized differently

Why is contact data quality important for banks and insurers?

Banks/ Insurers

1. Save costs



Overall



3. Save costs



What are the marketing impacts of bad contact data?

Organizations waste large amounts of budget on incorrect and inaccurate contact data

Overall, 63% of respondents said that 5% - 30% of their marketing budget is wasted as a result of bad data

62% of insurers fell into this category

67% of banks fell into this category



How prevalent are contact data errors?

65% of banks and 60% of insurance organizations say that 6% or more of their database contains missing or inaccurate contact data

Top contact data errors are consistent across industries:

Incomplete or missing data

Outdated information

Incorrect data



Where do contact data errors originate?

Both bank and insurance respondents agreed upon which department typically creates contact data errors: Customer Service

The second most likely area of origination was different:

Bank respondents said that Marketing contributes a large number of data errors

Insurance respondents identified Sales as a main contributor of contact data errors

Both industries find that multiple departments are responsible for these types of errors



Who is responsible for clean contact data?

Overwhelming response – the IT department is most often responsible for cleansing contact data

Surprisingly, less than 25% of respondents said that data quality responsibility was shared by multiple departments

Remember – respondents said that multiple departments contribute data errors, and 28% of respondents said that ALL departments contribute data errors

A variety of departments use contact data

Why not share responsibility among several stakeholders?



Are there common barriers to maintaining accurate contact data?

In a word – yes

Budget

Staff errors

Awareness of changes to data

Budget Senior Management Support

Large Volume of Changes Knowledge or Awareness of Changes

Staff Errors Time and Internal Resource

None



How do organizations measure data quality?

Respondents had three main ways of measuring data quality:

Analysis of response rates

Software tools

Manual processes

Only a very small percentage of survey respondents have no current system for measuring data quality

0

20

40

60

Perc

en

tag

e

Manual Processes

Outsource to agency

Analysis of response rates

Software Tools

We don't currently measure accuracy



How do organizations maintain and improve contact data?

0 20 40 60

Percentage

We Have No Solutions in Place

Clean Data Prior to Each Communication

Data Cleansing Tools at All Customer Touchpoints

Software Tools

Outsource to Call Center

Outsourcing of Clean Data

Staff are Measured on Data Quality

Training of Staff

0 20 40 60

Percentage



Data Cleansing Tools at All Customer

TouchpointsSoftware Tools




Training of Staff

0 20 40 60

Percentage



Data Cleansing Tools at All Customer Touchpoints

Software Tools




Training of Staff

BANKS INSURERS



What types of data quality tools are being used?

0%

20%

40%

60%

Point-of-

Capture

Address

Verification

Batch Address

Verification

De-

Duplification

Email

Verification

Banks

Insurers



Overall trends

Data quality is a priority for businesses

Customer satisfaction key reason for data quality

Large amount of resources wasted on inaccurate data

Customer Service, Sales and Marketing cited as error-prone departments

One department is responsible for data quality

Budget still ranked as key barrier to clean data

Companies still use manual processes to clean data



Tips to clean data

1. Understand your database

2. Clean existing data

3. Remove duplicate records

4. Verify data during all capture processes

5. Enhance and update data



QASProducts & services

Real-time verification Clean & enhance

Clean

QAS Batch (PC Based)

QAS Bulk Processing (Web Based)

Phone & Email Batch(Service)

Enhance

QAS Unify (PC Based)

NCOALink® (Service)

Address

QAS Pro (PC Based)

QAS Pro On Demand (Software as a Service)

QAS Pro Web (Web Based)

QAS Pro API (Integration Toolkit)

Phone and Email

QAS Phone (Service)

QAS Email (Service)



Please visit www.qas.comfor more information.

http://www.qas.com/?tid=2326

http://www.qas.com/?tid=2326

Data Quality Survey Trends & Perceptions

Technology

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