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Grow Your Own:
How to Create a Data Culture at Your
Organization
Emperitas Webinar June 14th
2016
www.emperitas.com / 801.810.5869 / 4609 South 2300 East Suite 204, Holladay, UT 84117
Hi, I’m Luciano Wheatley Pesci…
Founder & Director, Utah Community Research Group (UTAHCRG), Univ. of Utah
• Teach microeconomics, statistics, applied research & data analytics, and American
economic development & history.
Co-Founder and CEO, EMPERITAS
• Team of analysts, data scientists, and economists who find actionable business
intelligence through marketing analytics and agile research, to help our clients beat their
competitors for the most profitable customers.
2
My Basic Argument Today…
• Entering The Data Age: If you’re not using data daily, then you’re
falling behind - personally and professionally.
• Types of Data Talent: Specialization is necessary, which means you
need to understand the types of talent that can solve your problems.
• Culture & Goals: Cooperation is the cornerstone of specialization,
which requires transparency, accountability and clear responsibilities.
• Think Small, Grow Big: Data doesn’t have to be big to be useful.
3
Entering The Data Age
Welcome to the Data Age!
• Era of human evolution where everyone is using massive
amounts of information to inform daily decision-making.
• Personal Life: Fitbit, mobile data, home automation.
• Professional Life: Forecasting sales, validating marketing
effectiveness, identifying profitable customer personas,
building competitive strategies.
5
Human + Machine = Success
• The Data Age was made possible through huge advances in
digital technology.
• Human to Human, Machine to Human, and Machine to
Machine communication all captured now.
• Machines need to assist, not replace, humans. Your gut still has
a HUGE role to play.
6
Types of Data Talent
Caveat Emptor
• There are no data science unicorns. Data science is
a team effort and requires specialization & cooperation.
• Most data failure is the result of hiring, and
improperly utilizing, the correct type of data talent.
8
Different Types of Data Talent
• Data Detectives (Analysts & Researchers)
• Data Guardians (DBAs & ETLs)
• Data Scientists (Statisticians & Programmers)
• Data Translators (Visualizers & Storytellers)
9
Culture & Goals
Creating a Team of Superforecasters
• The purpose of using information is to make better
decisions for the future (you can’t change the past,
but you can learn from it).
• Superforecasters – people who can process a lot of
information, are flexible in their beliefs, and perform
exceptionally in teams when taught how to cooperate.
11
Cooperative Culture is the Key
• Specialization in data projects requires a well-functioning, highly
coordinated team, that’s involved in creating the strategy and agreeing on
measures of success & failure.
• “Planning is valuable, a plan is useless.” This means flexibility to
pivot the strategy over time is paramount.
12
Building Blocks of a Cooperative Culture
• Transparency – Expectations, goals, milestones, and KPIs should be clearly
visible to everyone at all times.
• Roles & Responsibilities – Clear lines of responsibility and freedom to
execute is the only way specialization works.
• Resources & Training – People need (ever-evolving) tools to succeed.
• Accountability – Merit is the ultimate motivator, but that
means balancing reward and punishment equally.
13
Think Small, Grow Big
“Small Data” Is Usually Enough
• Data doesn’t have to be “big” to be useful.
• Start small. Begin with qualitative information then
use what you’ve learned to find larger quantitative data.
• You need to be connecting insights across data sources,
regardless of their size.
15
Let’s talk…
The Conversation Doesn’t Have to End Here…
luciano@emperitas.com / 801-810-5869/ EmperitasSG / 4609 South 2300 East Suite 204, Holladay, UT 84117

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Grow Your Own - How to Create a Data Culture at Your Organization

  • 1. Grow Your Own: How to Create a Data Culture at Your Organization Emperitas Webinar June 14th 2016 www.emperitas.com / 801.810.5869 / 4609 South 2300 East Suite 204, Holladay, UT 84117
  • 2. Hi, I’m Luciano Wheatley Pesci… Founder & Director, Utah Community Research Group (UTAHCRG), Univ. of Utah • Teach microeconomics, statistics, applied research & data analytics, and American economic development & history. Co-Founder and CEO, EMPERITAS • Team of analysts, data scientists, and economists who find actionable business intelligence through marketing analytics and agile research, to help our clients beat their competitors for the most profitable customers. 2
  • 3. My Basic Argument Today… • Entering The Data Age: If you’re not using data daily, then you’re falling behind - personally and professionally. • Types of Data Talent: Specialization is necessary, which means you need to understand the types of talent that can solve your problems. • Culture & Goals: Cooperation is the cornerstone of specialization, which requires transparency, accountability and clear responsibilities. • Think Small, Grow Big: Data doesn’t have to be big to be useful. 3
  • 5. Welcome to the Data Age! • Era of human evolution where everyone is using massive amounts of information to inform daily decision-making. • Personal Life: Fitbit, mobile data, home automation. • Professional Life: Forecasting sales, validating marketing effectiveness, identifying profitable customer personas, building competitive strategies. 5
  • 6. Human + Machine = Success • The Data Age was made possible through huge advances in digital technology. • Human to Human, Machine to Human, and Machine to Machine communication all captured now. • Machines need to assist, not replace, humans. Your gut still has a HUGE role to play. 6
  • 7. Types of Data Talent
  • 8. Caveat Emptor • There are no data science unicorns. Data science is a team effort and requires specialization & cooperation. • Most data failure is the result of hiring, and improperly utilizing, the correct type of data talent. 8
  • 9. Different Types of Data Talent • Data Detectives (Analysts & Researchers) • Data Guardians (DBAs & ETLs) • Data Scientists (Statisticians & Programmers) • Data Translators (Visualizers & Storytellers) 9
  • 11. Creating a Team of Superforecasters • The purpose of using information is to make better decisions for the future (you can’t change the past, but you can learn from it). • Superforecasters – people who can process a lot of information, are flexible in their beliefs, and perform exceptionally in teams when taught how to cooperate. 11
  • 12. Cooperative Culture is the Key • Specialization in data projects requires a well-functioning, highly coordinated team, that’s involved in creating the strategy and agreeing on measures of success & failure. • “Planning is valuable, a plan is useless.” This means flexibility to pivot the strategy over time is paramount. 12
  • 13. Building Blocks of a Cooperative Culture • Transparency – Expectations, goals, milestones, and KPIs should be clearly visible to everyone at all times. • Roles & Responsibilities – Clear lines of responsibility and freedom to execute is the only way specialization works. • Resources & Training – People need (ever-evolving) tools to succeed. • Accountability – Merit is the ultimate motivator, but that means balancing reward and punishment equally. 13
  • 15. “Small Data” Is Usually Enough • Data doesn’t have to be “big” to be useful. • Start small. Begin with qualitative information then use what you’ve learned to find larger quantitative data. • You need to be connecting insights across data sources, regardless of their size. 15
  • 17. The Conversation Doesn’t Have to End Here… luciano@emperitas.com / 801-810-5869/ EmperitasSG / 4609 South 2300 East Suite 204, Holladay, UT 84117