Will Pate discusses what a data-driven culture looks like at a company. A data-driven culture empowers everyone in the company to make decisions based on data, with a focus on optimizing key metrics. Everyone should have the capacity to use data, not just a small group of data scientists. Good metrics are specific to a company's business and stakeholders. The document also provides tips on establishing infrastructure to measure important metrics, prioritizing metrics, and building a data-driven culture from the top-down.

1. @WillPate
Will pate
VP Digital
Case Study
“Making Sense of Data at Any Size”
m2

2. Will Pate, VP Digital Strategy, m2
April 8, 2014
@willpate
@m2canada
Making Sense
of Dataat Any Size

3. Data Big Data
How Most Companies Use Data

4. Actionable
insights
for informed
decision making
Unstructured
Insights
Visualized
Data
Structured
Data
Unstructured
Data
There is a Hierarchy of Value From Data
Opinions

5. What Does a
Data Driven Culture
Look Like?

6. What Does a Data Driven Culture Look Like?
Not Data
Driven
• Executive knows best
• Without art
• Selective about access
• Passion without reason
• Reliant on a small set of people
• Obfuscates purpose so that data is simply
numbers without meaning
• Stuck in decision cycles too slow to act upon
new insights
• Focus on technology over people
X

7. What Does a Data Driven Culture Look Like?
• Executive led
• Empowers everyone
• Builds everyone’s capacity to make
better decisions
• Passion driven by data
• Makes clear to everyone what we’re
optimizing towards
• Agile, adaptive to learning
• Technology serves the people’s needs
Data
Driven

8. Who is Responsible
for Data Science?

9. Who is Responsible for Data Science?
• Rapid building of organizational
capability for data driven decision
making
• Need time to learn operational
mechanics of business
• Organization is reliant on a small group
and therefore fragile to staff changes
• Spiky distribution of improvement based
on political power of groups requesting
resources
Data
Scientists

10. Who is Responsible for Data Science?
• Capacity for better decision making
across the organization
• Alignment between operational
understanding and insights
• Better aggregate organizational
capacity for data driven
decision making
• Organization is resilient to staff changes
• More even improvement
across organization
Everyone

11. Create a Hierarchy of KPIs
KPI What question it answers
Lifetime Customer Value by Channel
What channels drive the most valuable
customers?
Sales of widget by channel
What channels drive the most
customers?
Months to recover Cost of Acquisition
by channel
How long before customers from
a channel become profitable?
Cost per acquisition by channel
Where the cheapest customers
come from?
Conversion rate by channel
Where the visitors most likely to buy
come from?
Cost of visitor by channel
Where the cheapest prospects
come from?

12. Measure What
you Measure

13. What Does a Data Driven Culture Look Like?
• Usually easy to measure
• Don’t require any specific
understanding of your business
• Are platform-specific
• Don’t matter to your stakeholders
• Don’t help you optimize to the KPI
up the hierarchy
Poor KPIs

14. What Does a Data Driven Culture Look Like?
• Usually hard to measure
• Require an understanding
of your business
• Are platform agnostic
• Matter to your stakeholders
• Help you optimize to the next
important KPI
Good KPIs

15. Identify and Fill The Gaps With Infrastructure Investments
KPI Status Requirements
Lifetime Customer Value by Channel 1 year 1 year of sales data in data warehouse
Sales of widget by channel 6 Weeks 90 days of sales data in data warehouse
Months to recover Cost of Acquisition by
channel
3 Months 90 days of sales data in data warehouse
Cost per acquisition by channel 6 Weeks Connect sales system into data warehouse
Conversion rate by channel Done Pulled spend and web analytics into data
warehouse
Cost of visitor by channel Done Pulled spend and web analytics into data
warehouse

16. Summing It Up
Commit to a repeatable model for actionable insights
Start with culture, and start from the top
Hire data scientists, but make their mandate capability building
Prioritize your KPIs
Measure what you measure
Identify and fill the infrastructure gaps
Happy to continue the conversation on Twitter @willpate
and be sure to tell @m2canada what you think!
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