Getting to Quick Wins with Data - Dawn of the Data Age Lecture Series
- 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
- 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
- 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
- 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
- 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