Case Study: "Making Sense of Data at Any Size"

  • 159 views
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
159
On Slideshare
0
From Embeds
0
Number of Embeds
2

Actions

Shares
Downloads
2
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 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! 1 2 3 4 5 6