80% of data projects fail. How can something so promising be failing so badly? While organizations are scrambling to stay competitive by hiring data-talent, they don't fully understand the types available, how to integrate them into existing workflows, what to expect from their efforts, and how to gauge success.
You can watch the accompanying Webinar here: https://youtu.be/MUv-tqMHbvs
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.
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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.
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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.
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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.
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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.
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9. Different Types of Data Talent
• Data Detectives (Analysts & Researchers)
• Data Guardians (DBAs & ETLs)
• Data Scientists (Statisticians & Programmers)
• Data Translators (Visualizers & Storytellers)
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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.
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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.
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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.
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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.
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