80% of all data projects are currently failing. This means that organizations who successfully use their data are in possession of a major competitive advantage. This lecture will show you tried-and-true methods for setting data project goals, managing data teams, and how to quickly validate your data findings to reach quick wins. This Lecture will: -TEACH YOU TO SET REACHABLE DATA PROJECT GOALS -EXPLAIN SUCCESSFUL DATA PROJECT ROAD-MAPPING -OUTLINE EFFECTIVE DATA PROJECT MANAGEMENT -SHOW YOU HOW TO TEST/ITERATE WITH YOUR DATA You can watch this lecture here: https://youtu.be/VqMCK7Whyd4

1. Dawn of the Data Age Lecture Series
Getting To Quick Wins with Data

2. Hi. I’m Luciano Wheatley Pesci…
Founder & Director, Utah Community Research Group, Univ. of Utah
● Teach microeconomics, statistics, applied research & data analytics, & American economic history.
● Teach data science for Westminster and developed their 3-class MBA emphasis in data science.
Co-Founder and CEO, EMPERITAS
● A Services as a Subscription team of economists and data scientists delivering bi-weekly Customer
Lifetime Value intelligence so our clients can beat their competitors for the most profitable customers.
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3. Today’s Lecture Outline
● Teach you how to set reachable data project goals.
● Explain successful data project road-mapping.
● Outline effective data project management methods.
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4. 4Setting S.M.A.R.T. Data Goals

5. A Word About Change...
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6. The Most Important Step
● Getting aligned on your goals for the data is
the most important step in any data project.
● If you don’t know where you want to end up,
there’s almost no chance of getting there.
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7. Bring The Tribe Together
● Host a kickoff meeting with all stakeholders.
○ Use food as bait for participation.
● Ask what people want to know from the
data AND what they’ll do based on the data.
● Try using the S.M.A.R.T. method...
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8. S.M.A.R.T. Goals*
● SPECIFIC
○ What do we need to learn from the data?
● MEASURABLE
○ What data will we use to learn how to achieve our goal?
● ACHIEVABLE
○ What’s a “win” for this goal?
● RELEVANT
○ How will we use the data results to achieve our goal?
● TIMELY
○ When is the data results need to inform the goal?
8*Source: www.mindtools.com/pages/article/smart-goals.htm

9. SPECIFIC: What To Learn
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● GOALS = What You Want To Learn From Your Data.
○ Each goal should be a single, narrowly defined unknown
you want to learn about using data.
● For example, “what’s our customer lifetime value?”

10. MEASURABLE: What Data to Use
● Identify the data you have (or that you can get)
for use in reaching your goal.
○ Often this involves multiple data sets.
● For example, “customer lifetime value will
require Salesforce and Quickbooks data.”
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11. ACHIEVABLE: Defining a “Win”
● Clearly define the standard of success for each
goal, using the data you’ve identified.
○ The purpose of this step is to create accountability
against hard, prestated, expectations.
● For example, “knowing the average customer
lifetime value will be enough for us right now.”
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12. RELEVANT: Ensuring Usefulness
● Nothing’s gained by learning from data that
you can’t act on.
○ Define usefulness before any analysis begins.
● For example, “we’ll use the average
customer lifetime value to change who
we’re marketing to across all channels.”
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13. TIMELY: Setting Deadlines
● Deadlines are key to successful data projects.
○ They help avoid mission creep, and keep you from going
too far down the data analysis rabbit hole.
● For example, “to change our marketing targets
we’ll need the average customer lifetime value
before October 1st 2017.”
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14. Getting Even S.M.A.R.T.ER
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15. Responsibility Matters
● Each goal needs a Directly Responsible
Individual (DRI) assigned to it.
○ “If it’s everyone’s job it’s no one's job.”
● This person may not work alone, but they’re
ultimately responsible for success of the goal.
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16. Prioritization
● Once goals are listed (with data you’ll use & what
you’ll do with results) you need to prioritize your list.
● The basic tradeoff is speed vs depth of insight.
○ Start with goals that have quickest wins or biggest impact.
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17. An Example Goals Sheet
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18. 18Data Project Road-Mapping

19. Planning Is Half The Battle
● SMART goals are so important because all of
the remaining project work depends on them.
● Once you’ve assigned the prioritized goals to
a DRI, the next step is to map out the project.
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20. Whiteboards Are Your Friend
● Experiment with various brainstorming method,
just ensure your whole team participates.
○ The 6-3-5 Method* is a popular approach.
● Visualize THEN Explain.
○ Seriously, sketch it out then verbalize a short summary.
20*Source: en.wikipedia.org/wiki/6-3-5_Brainwriting

21. Be Flexible Enough To Pivot
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22. Sprints for 60 Days
● Create a 60-day timeline in 2-week sprints.
○ Gantt charts are great for visualizing this.
● Each sprint needs to make progress.
○ If it can’t be done in 2 weeks, break it into pieces.
○ “Perfect is the enemy of better.”
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23. A Fail-Resistant System
● With your prioritized goals and a roadmap, the
next step is to execute on your plan.
○ Executing the project plan is 80% of the actual work.
● You Will Fail. It’s a fact.
○ The key is building a resilient project management
system that allows failures to be known & learned from.
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24. 24Data Project Management

25. Transparency & Accountability
● For your data project to succeed, you need to
embrace radical transparency.
● Improves accountability to team & the individual.
○ Requires clear rules & enforcement (radical candor).
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26. Task Management Software
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27. Trust But Verify (with Stand-Ups)
● Host “stand-up” meetings every two weeks.
○ Short meetings where everyone stands and talks.
● The goal is to get project updates & identify
needs that are holding up progress.
○ If a project pivot is needed, this is when you’ll discover it.
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28. Constant Visibility
● Identify one Key Performance Indicator
(KPI) to track for each active project goal.
○ Collect these (quickly) at stand-up meetings.
● Hang a “Goals Board” where everyone
can see it. Update it every two weeks.
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29. Test, Validate, Repeat.
● Build a system that outlives any single DRI.
○ Watch for people with a high truck factor.*
● Test & validate continually. If you’re organized
you’ll quickly repeat past work.
○ For example, customer lifetime value changes over time.
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*The worse it’d be if that person got hit by a truck (in terms of the project success)
the higher their individual truck factor.

30. In Conclusion: Now You Know...
● How to set reachable data project goals.
● How to create data project roadmaps.
● How to effectively manage a data project.
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31. JOIN US FOR THE NEXT LECTURE
How to Interpret Data Like a Pro, September 19th 2017
emperitas.com/lecture