Are you fearful that trying to fix your company’s data quality issues will result in 7 years of bad luck? Too often, data quality superstitions lead to paralysis by analysis. You don't need a rabbit's foot to make progress with your data management strategy, you just have to separate fact from fable. Donato Diorio and Michael Farrington, two experts in CRM and marketing automation technology, dispel several common data superstitions providing tangible and actionable advice to ensure “good luck” for all who rely on your data.

1. How to Overcome Your
Data Quality Superstitions
Donato Diorio
Founder & CEO
Broadlook Technologies
www.broadlook.com
#DataBadLuck
Michael Farrington
Chief Product Officer
RingLead
www.ringlead.com

2. Key trends in CRM
Social
Cloud
Metrics/
Dashboards
Big data & sales
intelligence
#DataBadLuck
Mobile
Analytics
Collaborative
selling
Empowered
Users

3. The Foundation: Good CRM Data
Without enhancing
your existing data
you limit your
“data potential”
Clean
Limited
potential
Protect
Without
performing a
comprehensive
data cleanse, the
foundation is weak
#DataBadLuck
Without a
protection
Without…
strategy, your data
will continually
decay
Decaying
data
Poor
foundation
Enhance
Enhanc
e

4. MYTH
It’s ok to delete data
#DataBadLuck

5. Never delete CRM data - Score it Instead!
dhughes@broadlook.com
nbauman@broadlook.com
#DataBadLuck

6. Scoring Data: Focus on Good Data
dhughes@broadlook.com
ddiorio@broadlook.com
nbauman@broadlook.com
#DataBadLuck

7. What can we learn/derive from the
existing data?
•
•
•
•
•
•
•
•
•
Domain is broadlook.com
Email pattern in first-initial(.)last-name
April reports to Donato
On broadlook.com, there are 15 additional contacts
Notes on Donato are 1 month old
Notes on April are 8 months old
Natalie is no longer at the company
“The Doctor” is a fictional character
Natalie is now a VP at another company - and an
additional prospect!
#DataBadLuck

8. Result: A More Complete Picture
ddiorio@broadlook.com
abroom@broadlook.com
dhughes@broadlook.com
ipetrenko@broadlook.com
akazansky@broadlook.com
kschuetz@broadlook.com
New Prospect:
nbauman@xyzcorp.com
#DataBadLuck

9. MYTH
Using the stick works:
make all fields required!
#DataBadLuck

10. Using the stick works
• Determine carrot and/or stick on
field basis, not object
• Educate users on the importance
of everything you ask of them
(focus on selfish reasons)
• Don’t ask what they don’t know
#DataBadLuck

11. MYTH
Training works to
enforce data standards
#DataBadLuck

12. #DataBadLuck

13. Enforcing Data Standards is Optimal
#DataBadLuck

14. MYTH
My Data is Fairly
Complete
#DataBadLuck

15. My Data is Fairly Complete
• Superstition or fact? Find out!
#DataBadLuck

16. My Data is Fairly Complete
#DataBadLuck

17. MYTH
Buy as much data as
you can, all at once
(because it’s cheaper)
#DataBadLuck

18. Data decay happens
• Change in title, promotion
• Change of department
• Change in working location
• Change of area code
• Change of phone number
• Change of email format
• Add mobile phone number
• Merger or acquisition
#DataBadLuck

19. #DataBadLuck

20. MYTH
Bad Data is IT’s
Problem
#DataBadLuck

21. Bad Data is IT’s Problem
• He who reporteth upon it...
• Treat it like a project
• Choose Data Quality applications that
don’t require a PhD in Computer
Physiology
#DataBadLuck

22. MYTH
The company’s name
is more important than
the website address
#DataBadLuck

23. Company Based
Changes
Decade
Multi year
Year
Contact based Event & Activity Based
Quarter
Month
Day
Static
Data types
Acquisition
method
URL
Hour
Dynamic
Corp Name
Database
merging +
algorithm
Update
strategy
#DataBadLuck
City
State
Address
Zip
Phone
Competitors
Editorial &
Aggregation
Revenue
Employees
Products
Services
Financials
Editorial + SEC
spidering
Static, compiled and
online databases
Names
Titles
Emails address
Phone
Biographies
Social Network Links
Real time
content
spidering
News
Email content
Blogs
Net links
social networks
newsgroups
Tweet
Check-In’s
Proximity
Website visits
Email reads
Semantic
monitoring
services
Real time
API’s
Real time

24. MYTH
I know how to search
for duplicates
#DataBadLuck

25. I know how to search for duplicates
• It gets messy
• Users may not have access
• Even if you do, is that a good use of your
(user’s) time?
#DataBadLuck

26. MYTH
My vendor’s data is
better than mine
(they are the specialists right?)
#DataBadLuck

27. Data industry processes
•Buy data from multiple sources
•Refresh top companies with editors
•6 month cycle (top 10K companies)
•6-12 month (next 40K companies)
•24 month cycle on the next 2 million
•Nothing past the top 2 million
•Add social data (good for top 10%)
•Add news feeds (good for top 5%)
•Mob source
#DataBadLuck

28. Buying data...why, how and gotchas
How recent is the list as whole? How quickly was the list produced?
Different from record freshness. Contact data degrades 3% per month
(5% in a stressed economy). A list of 1000 records can be built over 60
days. In the case below, the first 500 records are 8 weeks old (5.68%
inaccurate) upon list delivery.
96.8%
#DataBadLuck

29. Buying data...why, how and gotchas
86.5%
#DataBadLuck
59.5%

30. Your data vs. your vendor’s
• Your data is less complete
• Your data has a better competitive
advantage
• Use their data to fill in your data
#DataBadLuck

31. MYTH
My data is awesome!
#DataBadLuck

32. CRM Data Quality
Points
4
3
2
1
Fresh
<30 days
<60 days
<90 days
<180 days
Accurate
95.00%
80% +
70% +
60% +
Factors
Basic
Basic + 2 social Basic + 1 social (email+phon
e)
Multi-venue
All
available
Built fast
<14 days
<60 days
<90 days
<180 days
Normalized
Enforced
Plan + culture
Has plan
no
Scored
Custom
rules
Accessible
rules
white box
scoring
black box
scoring
Total data quality score:
#DataBadLuck
Your
score

33. CRM Competitive Advantage
Points
4
3
2
1
target by self
description
hand built
keywords
SIC code
built on-demand
mashed from
many sources
pulled from
larger sample
Complete
95%+
80.0%
60.0%
40.0%
Exclusive
no competitors
limited access
anyone can buy
access
free
Sources
transparent
sources known
sources
available
By a person
Marketing
automation
email
Factors
Targeted
Custom
Transparent
Verified
Total competitive advantage score:
#DataBadLuck
Your
score

34. Where is your CRM data?
Competitive
Advantage
24
12
0
12
Data Quality
#DataBadLuck
24

35. What is your data potential?
Qualitative /
Event-Driven
Qualitative
Cyclic
Quantitative /
Cyclic
Competitive
Advantage
Quadrant Key
24
12
Quantitative/
commodity
Influence
Relationship
g
tin ion CRM+
ke at
ar m
90 days
m to
CRM+
u
a
180
CRM+
360
days
days
Cold Call
0
new
CRM
lead
Warm call
12
Data Quality
#DataBadLuck
24

36. MYTH
Preventing Duplicate
Records Based on
Email is Sufficient
#DataBadLuck

37. Preventing Duplicate Records
Based on Email is Sufficient
• No. Not even for sending emails.
• Email addresses are not social security
numbers
• True story: I had four email addresses at
one company
#DataBadLuck

38. MYTH
My sales team lets me know
what they need
#DataBadLuck

39. The Evolution of Sales Desire
I want...More data (lists)
I want... Better selection (databases)
I want... More contacts per company (zoom)
I want... Fresher contacts(Jigsaw)
I want... More information (LinkedIn)
I want... More knowledge
(many sources)
I want... More process (crm)
I want... Sustainable process
Data
Information
#DataBadLuck
Knowledge
Process

40. Questions?
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#DataBadLuck