7 Pillars Of Data Strategy - High Five 2018

METHOD SAVVY - ALL RIGHTS RESERVED
H I G H F I V E 2 0 1 8
G E T T I N G T H E M O S T O U T O F
Y O U R D A T A : 7 P I L L A R S O F D A T A
S T R A T E G Y

A G E N D A
• Data’s relationship to modern marketing
• Data and Marketing Technology (MarTech)
• Data strategy
• Conducting useful analysis
@EvanLevy

Who is this guy?
@EvanLevy

W H O I S T H I S G U Y ?
Evan Levy
Director of Advertising
Method Savvy
@EvanLevy
@EvanLevy
www.evan-levy.com

The Data
Economy
@EvanLevy

“More data is created every two days than existed from 2003 to the
dawn of human existence.”
#High5Co
nf
@EvanLe
vy

D A T A I S N O W U B I Q U I T O U S
• Proper management and utilization of this resource will differentiate in the
short term.
• This will be assumed and mandatory to stay competitive in years to come.
@EvanLevy

D A T A & A N A L Y T I C S S K I L L S C R I T I C A L T O
S U C C E S S
• To be successful in 2020:
the most important
skill will be the
ability to work with
data & analytics.

V A L U E F R O M D A T A = H O W I T ’ S U S E D
Like oil – raw data is worthless.

Data must be organized, “refined” and put to use to be valuable.
Expensive analytics software with no strategy is the modern “throw
money at a problem and hope it goes away.”
#High5Co
nf
@EvanLe
vy

How can marketers get the most out of their data?
@EvanLevy

MarTech
Landscape
@EvanLevy

T H A T ’ S A L O T T O K E E P T R A C K O F !
@EvanLevy

+39% over
2016
32x 2011!

Humans are still the most important part of data and analysis work.
@EvanLevy

“We’re not that much smarter than we used to be, even though we
have much more information—and that means the real skill now is
learning how to pick out the useful information from all this noise.”
Nate Silver
American statistician and writer
Editor in Chief, FiveThirtyEight
#High5Co
nf
@EvanLe
vy

What’s the ROI of this expensive new software?
What’s the ROI of hiring an analyst?
@EvanLevy

Data Strategy
@EvanLevy

Start with resources & infrastructure.
End with ways to make your organization more money.

Easy right?

G R E A T P L A N T O M A K E M Y C O M P A N Y M O R E
$ $ $ $ $
• Phase 1: Amazing new (software/agile method/team structure etc.)
• Phase 2: ????
• Phase 3: Profit!!!!!

What practical benefits can I get out of this now?

Analytics helps you understand where your time would be most
valuably spent, and where it is most urgently required.

Proper use of analytics empowers you to make more (and better!)
decisions in the same amount of time, without feeling exhausted by
the end.
#High5Co
nf
@EvanLe
vy

The best outcomes require careful planning & strategy
before
data capture begins.

Pillars Of The
Strategy

W H A T T O C O N S I D E R W I T H Y O U R D A T A
S T R A T E G Y
1. The Questions
2. Technical Implementation
3. The Users
4. Data Storage & Structure
5. Data Security
6. Personally Identifiable Information (PII)
7. Visualization & Analysis Needs
@EvanLevy

T H E Q U E S T I O N S ( A K A P R O B L E M S )1
@EvanLevy
• The more valuable your question, the more valuable analytics is to the
company
• Walk before you run – start with questions that produce focus for decision
makers (including yourself!)
• Data maturity
• What happened?
• What’s happening?
• What’s going to happen?

A S K S P E C I F I C Q U E S T I O N S O F Y O U R D A T A
• Data absent context is rarely helpful.
• Push yourself (and your org) to conduct action-oriented analysis that is
focused on the business problem.
• Will this report help me understand what to do next?
• Automate tasks that divert time away from high value analysis as much as
possible.
@EvanLevy

2
T E C H N I C A L I M P L E M E N T A T I O N
@EvanLevy
• The most valuable data sets require high levels of customization
to the code sending data to your analytics platform
• Understand the technical limitations of your developer resources
• You don’t have to know how to code it, but you need to be able
to translate the VP’s business goal > marketing KPIs > technical
needs to create the data set
• HBR has recently dubbed this role the “analytics translator”

3
@EvanLevy
T H E U S E R S
• Who needs to access the data for analysis and who should be
allowed to make changes to the platform or data source?
• Always make sure there is a company-owned admin user profile
that is not attached to a specific individual.
• Please. Always have a company-owned Admin user that is not
attached to a specific individual.

4
@EvanLevy
D A T A S T O R A G E
• How much data will you be storing/analyzing?
• A few hundred leads?
• Millions of site visits?
• How long will you need to keep it?
• Do you need to combine or clean data before it’s ready for your
visualization platform?

D A T A S T O R A G E N E E D S

5
@EvanLevy
D A T A S E C U R I T Y
• Are you following appropriate user access controls?
• Do your software integrations create other risk points?
• Are there government regulations to be aware of (HIPAA, etc)?

6
@EvanLevy
P E R S O N A L L Y I D E N T I F I A B L E
I N F O R M A T I O N ( P I I )
• Are you capturing PII as a dimension (email, phone #, etc)?
• Hint: PII can slip by in URLs, too
• Are you passing that information into an analytics platform like
Google Analytics? – that can get your account banned!
• Use off-line keys to match user data, or anonymize & aggregate
(best customers, new customers, etc).

P I I D A T A P O L I C I E S

7
@EvanLevy
V I S U A L I Z A T I O N & A N A L Y S I S
• Evaluate data source flexibility with software
• Who is the audience for your analysis and reporting?
• What level of context do they have?
• What are the top 3 things they’re interested in?
• How frequently does the view need to be refreshed?

Other Tips For
Better #dataviz

T I P S F O R S U C C E S S F U L D A T A
V I S U A L I Z A T I O N S
1. Again, consider the audience
2. Make sure your labels are clear
3. Minimize styling while creating emphasis --
@EvanLevy

M I N I M I Z E S T Y L I N G & C R E A T I N G E M P H A S I S

3. Minimize styling while creating emphasis -
4. Avoid pie charts if you have more than a handful of segments
@EvanLevy

T H I S I S G R O S S

T H I S I S B E T T E R

3. Minimize styling while creating emphasis -
4. Avoid pie charts if you have more than a handful of segments
5. Make sure your data visualization accurately represents the data
- (I’m looking at you truncated Y axis)
@EvanLevy

N U M B E R S D O N ’ T L I E , R I G H T ?

T O R E V I E W – D A T A S T R A T E G Y
C O N S I D E R A T I O N S
1. The Questions
2. Technical Implementation
3. The Users
4. Data Storage & Structure
5. Data Security
6. Personally Identifiable Information (PII)
7. Visualization & Analysis Needs
@EvanLevy

B O N U S ! S O M E O F M Y F A V O R I T E
R E S O U R C E S
• Analytics Strategy by Avinash Kaushik: https://www.kaushik.net/avinash/
• Advanced Google Tag Manager: https://www.simoahava.com/
• Advanced Google Sheets training: https://codingisforlosers.com/
• Time saving reporting tool: https://supermetrics.com/
• Google Analytics setup: https://www.lunametrics.com/blog/

That’s all folks!
@EvanLevy

P A R T I N G T H O U G H T S – G O K I C K S O M E A S S
@EvanLevy

T H A N K S
www.evan-levy.com
Evan@MethodSavvy.com

7 Pillars Of Data Strategy - High Five 2018

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