The document details a lecture by Luciano Pesci on the transformative benefits of the data age, emphasizing the need for organizations to leverage data for competitive advantage and decision-making. It discusses the importance of establishing a data culture, using all available data, and experimenting to enhance customer experiences. The lecture also explores future trends in data utilization, including the potential for a data-driven society and the implications of blockchain technology.

1. Dawn of the Data Age Lecture Series
Interpreting Data Like a Pro

2. Hi. I’m Luciano Pesci, PhD…
Founder & CEO, EMPERITAS
● Team of economists and data scientists delivering bi-weekly Customer Lifetime Value
intelligence so our clients can beat their competitors for the best customers.
Founder & Director, Utah Community Research Group, Univ. of Utah
● Teach microeconomics, data science, applied research, & American economic history.
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3. Today’s Lecture Outline
● Teach the benefits of the Data Age.
● Show how data is transforming the world.
● Explain what’s on the data horizon.
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4. 4Benefits of The Data Age

5. Next Mandatory Skill
● Imagine not learning to type in the 80s.
○ Or ignoring how to use the internet in the 90s.
● Data is that x100.
○ This is because of the digital transformation.
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6. Competitive Advantage
● Your competitors aren’t trying with data.
○ There’s 80% chance those that are, are failing.
● A great place to start is lead scoring.*
○ Can be a powerful “quick win.”
○ Highest ROI of any data project when done right.
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*Data-Driven Sales Conversions: goo.gl/aKe74W

7. Providing Perfect Information
● Perfect information is the ultimate key to
better, more profitable, decision making.
● Data lets you understand important
frameworks like the customer journey.
○ With that information you can optimize
everything from marketing* to retention.**
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*Data Drive Your Content Creation: goo.gl/xSXPMr
**Customer Research for PMs: goo.gl/qSz877

8. It Helps You Personalize
● You can use your data to identify
personas from your customer profiles.*
● Personalizing your CX and product
increases loyalty & recommendations.
○ Both of these lead to higher profits.
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*Identifying Personas with Agile Research: goo.gl/haebMQ

9. Knowing Real Value
● It requires all your data, across the entire
customer journey, to understand CLV.
● Customer Lifetime Value is the best way
to find strategies that drive profitability.*
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*Calculating Your Customer Lifetime Value: goo.gl/haebMQ

10. It’s Efficient
● Efficiency is your only god.*
○ Doing more with the same is mathematically
equivalent to increasing your profitability.
● You’re already generating a ton of
data, you might as well use it…
○ Huge amount going uncollected too.
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*Think Like An Economist: youtu.be/G29eZIeWljc

11. 11Data Transforms the World

12. Why They’re Winning
● For every four data projects that fail,
one succeeds beyond measure.
○ As orgs jump into data science the
failure rate will increase before falling.
● What’s the commonality of winners?
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13. Getting to Quick Wins
● Success with data is like a snowball that
grows exponentially with additional wins.
● Organizations succeeding with data
science have demonstrated its efficacy.
○ This is best done through quick wins.*
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*Quick Wins with Data: goo.gl/gqQB9x

14. Having a Data Culture*
● A data culture requires there’s clear:
○ Governance, access rules, defined data
dimensions, DRIs & KPIs, and SMART goals
● The winners have also mapped every
customer touchpoint with data.**
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*Creating a Data Culture: goo.gl/bLBk67
**Customer Research for PMs: goo.gl/jdvp6c

15. Hiring The Right Talent
● Successful data organizations understand
their problem and what talent to hire.
○ They empower this talent to fail but hold them
accountable while getting out of their way.
● These teams follow the “2 Pizza” rule.
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*Two Pizza Rule: goo.gl/cFLkS3

16. Using All Their Data*
● The successful organizations are using
observational data where it’s available:
○ Internal vs external
○ Implicit vs explicit
● They use research to produce data
when it’s not available observationally.
○ This gives them a complete data picture.
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*Interpreting Data Like a Pro: goo.gl/71kr8u

17. They’re Experimenting
● Successful data organisations
also experiment extensively:
○ Doing lots of simple A/B tests
○ Using multivariate & behavioral tests
● Unlike observational or survey data,
experiments show causal connections.
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18. They’re All Agile
● 99% of the successful data stories are
from organizations using agile methods.*
● They make consistent daily progress,
rather than big gains sporadically:
○ Keeps them focused on what matters
○ Also makes them more adaptable
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*Accelerating CX Research with Agile: goo.gl/ZdES2J

19. They Keep Innovating
● A secondary use of their data is finding
new product-market fit for innovation.
● They also eliminate friction by observing
bottlenecks or pain points in their data.
○ This improves existing systems, but still won’t
stop the product life cycle from setting in.*
19
*Think Like An Economist (00:22:00): youtu.be/G29eZIeWljc

20. 20On The Data Horizon

21. IoT/IoE
● The digital transformation means that
absolutely everything will be quantified.
○ Since everything will be digitally connected.
● Most immediate use are Smart Cities.*
○ Organizations in those cities will benefit.
○ Everyone will gain from the information.
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*Smart Cities by Cisco: youtu.be/TCbvxb5t5_8

22. Machine (mining) Automation
● We’re hyper-focused on self driving cars.
○ Real impact will come from autonomous
swarms of asteroid mining machines.*
○ Add trillions to GDP, & might save the planet.
● It’ll only be possible because of huge
amounts of data & unending optimization.
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*Planetary Resources Inc: youtu.be/7fYYPN0BdBw

23. Data-Driven Society
● Everything about you will be quantified,
which means you can be predicted.
○ This is Minority Report come to life.*
● Could create an incredibly peaceful society.
○ Punishment for violation would be instant.
○ It means the rules used to program the machines
better be crystal clear & exactly what you want.
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*Knightscope Robot: youtu.be/i38xKr_iRLs

24. Getting Data Owned
● In the near future you will own your data.
○ This will allow you to monetize who you are.
○ It’ll be mostly for the purpose of ads.
● Right now you’re giving away data for free in
exchange for services like Google & Facebook.
○ It’s currently possible to sell your forehead for ads.*
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*Forehead Advertising: wikipedia.org/wiki/Forehead_advertising

25. Always Forward
● We’re moving into a blockchain world.
○ Decentralized, democratic, & transparent.
● Incredible data lets you map everything.
○ It means things cannot easily be undone.
○ Can help us use the past to navigate the future.
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26. Bigger, Stronger, Faster
● The future this perfect information (data)
will create, by accelerating and expanding
global free exchange via blockchain, will be
orders of magnitude above anything we’ve
seen in the recorded past.
○ Could be 1,000,000,000,000 bigger & better.*
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*If Cars Matched Computer Progress: goo.gl/y89iR6

27. Demise of National Boundaries
● One of the things data will show us
is just how similar we all really are.
○ This has been a lesson of market research.
● Will lead to a fall in national borders,
since they’re highly inefficient.
○ They interfere with the free movement
of commodities, people & ideas.
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28. Premium Human Creativity
● Algorithms result from human action.
○ Whether a machine can “create” is debatable.*
● Machine automation has the potential to
make human time even more impactful.
○ It’s already 24x what it was in 1979.**
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*Humans Need Not Apply: youtu.be/7Pq-S557XQU
**How Time Is More Impactful: humanprogress.org/article.php?p=698

29. 29What We Covered Today

30. What We Covered Today...
● The benefits of the data age.
● Data’s transformation of the world.
● What’s on the data horizon.
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31. 31How To Keep Learning...

32. You Should Read - [Will Robots & AI Take Your Job?]*
Authored by Darrell M. West, Brookings Institute.
Cliff Notes: Half of all jobs will be automated in the next two
decades. The potential social and political ramifications of the
sudden change are likely to be large. The outlook isn’t good
and should be understood via a comparison to the Great Depression.
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*Will Robots Take Your Job?: goo.gl/VBn8w8

33. Dawn of the Data Age Lecture Series
Interpreting Data Like a Pro