1.
Thank you for joining!
The live broadcast will begin shortly to
accommodate last minute attendees. 1

2.
Before We Begin
● Don’t be shy!
● Got questions? Use the questions
module!
○ We’ll address them throughout
the presentation.
○ If we have time at the end, we’ll
run through a few more.
● Webinar replay will be sent to all
registrants within 24 hours.
2

3.
3
Brad is the CEO and co-founder of
Sonar, a change management platform
designed to make things easier by
improving process, communication,
and documentation for
cross-functional teams. Brad is also
the founder of Wizards of Ops (or
WizOps for short), a community Slack
channel where ops people can share
tips, ask for advice, and make
operations magic.
GET EXCITED
Featured Speakers
Tim has spent his career
building and scaling software
products from the ground up.
The last decade he has
worked primarily on data
startups in a number of
different verticals.
Brad Smith
CEO, Sonar
Tim Liu
Head of Product, Hull

4.
4
How is everyone today?
POLL TIME!

5.
What’s the biggest
obstacle keeping you
from clean data?
5
POLL

6.
WhatWe’llCover
6
What do we mean
by “bad data”?
The operational and financial
impact of bad data
Prevention and
mitigation strategies
1
2
3

7.
What bad data scenarios
have you faced?
7
POLL

8.
Incomplete contact
and account profiles
Unstandardized data
Duplicate data records
Typos
What Do We Mean by
“Bad Data?”
8
Legacy fields (that have not
been properly sunsetted)
Data flows gone wrong
Merging records incorrectly

9.
Downstream Impact of
Bad Data
9
Inaccurate or
suboptimal targeting
Missed sales opportunities
Loss of customers
Reduced productivity
Poor customer experience
Inaccurate performance
reporting
“Death by a thousand cuts”

10.
21%of marketing budgets
are wasted as a result of bad data.
— Forrester and Marketing Evolution, 2019
10

11.
When bad data strikes,
what do you feel is the
biggest misalignment?
11
POLL

12.
● Merging or expanding departments
○ e.g. M&A, organizational restructuring
● Executing too quickly on growth initiatives
○ e.g. with new funding rounds
● Onboarding new tech and data sources
● Status quo isn’t working anymore
When Bad Data
Strikes
12
Usually due to a
misalignment of:
PEOPLE
PROCESSES
TECH

13.
Proven Strategies
13
1
Begin with
the end in
mind
2 Gain
visibility
3 Architect &execute
4
Measure &
maintain

14.
Begin with the end in mind.
1
14

15.
● Don’t try to boil the ocean; start small
● Determine your ideal outcomes
● Beware of scope creep
● Acknowledge unknown complexity and
try to minimize risks upfront
Recommendation #1:
Plan ahead to mitigate risk.
15

16.
Gain visibility.
2
16

17.
● Customer data platforms are designed to unify and centralize
● Identify duplicate data
● Identify gaps in the data (i.e. What fields are missing data?)
● Where is the “best” data? Which sources do you trust more?
Recommendation #1:
Put all of your data into one place.
17

18.
18
● Understand how your metadata is being mapped
● Which system is updating which field?
● How do you know who owns what?
Recommendation #2:
Create a blueprint.

19.
Architect and execute.
3
19

20.
● What is identity resolution?
○ The process of taking the data points that define an entity from different online or
offline systems and merging them to create a single, consolidated, and consistent
record of that individual or company.
● How will you define your entities (e.g. individuals and companies)?
● Several strategies:
○ Natural keys, universal IDs, multiple natural keys
Consideration #1:
The identity resolution strategy.
20

21.
Consideration #2:
The data model.
● The data model defines how entities relate to one another
○ i.e. Leads and Contacts associated with Accounts
● What is the “source of truth” for these relationships?
○ How are these relationships created?
○ How are these relationships updated?
○ What are the controls to surface issues and fix them?
21

22.
● Data transformation tools, like the Hull
Processor, can help
○ e.g. update all instances of ‘USA’ to
‘United States’
● What is the Hull Processor?
○ A real-time editing environment for
your customer data, the Hull
Processor lets you compute new
attributes from existing data and
update customer records on the fly.
Consideration #3:
Data cleansing and standardization.
22

23.
● Training and user enablement
● Documentation is key
● Defining and sticking to your naming conventions
Consideration #4:
Promote consistency.
23

24.
● They’re inevitable!
● Backup your data, if possible
○ Especially if you’re doing large scale cleaning operations
○ Any way to “Undo” is your friend
● Always provide an escape hatch for manual correction
○ Engineers love to automate, but if there’s no possibility to manually
intervene, you’ll have to rely on that engineer to fix issues
Consideration #5:
Edge cases.
24

25.
Measure and maintain.
4
25

26.
● Reminder: Begin with the end in mind
○ What goals did we set at the beginning to make sure our project was a
success?
○ Define what success looks like for your company, paying attention to holistic
revenue goals.
○ Be mindful about setting aside vanity metrics.
○ Know what data points you need to be collecting in order to gauge
momentum and progress towards your goals.
Begin with the end in mind
Measuring Success
26

27.
● Nothing is ever static
● Tactical things to put in place:
○ Alerts and notifications
○ Audit trails and commit messages
○ Project owners
○ Sandbox organizations
Managing a Changing
Data Landscape
27

28.
28

29.
THANK YOU!
Visit
hull.io
>>>
Join
WizOps.org
>>>
Visit
Seesonar.com
>>>
29