The lecture by Luciano Wheatley Pesci focuses on effectively setting and managing data project goals using the S.M.A.R.T. framework. Key steps include defining specific, measurable, achievable, relevant, and timely goals, as well as assigning responsibilities and creating a transparent roadmap for project execution. The session emphasizes the importance of accountability, prioritization, and adaptability within data projects to ensure successful outcomes.

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.
2

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.
3

4. 4Setting S.M.A.R.T. Data Goals

5. A Word About Change...
5

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.
6

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...
7

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
9
● 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.”
10

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.”
11

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.”
12

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.”
13

14. Getting Even S.M.A.R.T.ER
14

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.
15

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.
16

17. An Example Goals Sheet
17

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.
19

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
21

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.”
22

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.
23

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).
25

26. Task Management Software
26

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.
27

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.
28

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.
29
*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.
30

31. JOIN US FOR THE NEXT LECTURE
How to Interpret Data Like a Pro, September 19th 2017
emperitas.com/lecture