Monitor your FinAid Office Pulse with Instant Data. Chris Chumley, COO at CampusLogic

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1 Monitor your FinAid Office Pulse with Instant Data Chris Chumley, COO at CampusLogic 1

2 Meet Your Presenter Joined CampusLogic: 2014 Background: Specializing in product management, project management, and business process design throughout his 20-year career, Chris is an expert in managing change Chris Chumley COO CampusLogic Responsibilities: Making financial aid easy, mobile, and personalized 2

3 Agenda Using Data to Move through OODA Loop Creating a Culture of Data in Aid Office Using Metrics to Drive Team Performance Key Takeaways 3

4 Postsecondary education simultaneously suffers from too much data and too little information. The system is awash in data, but too often the data are not useful or not accessible to the people who need actionable information to make decisions. Gates Foundation 4

5 FinAid Analytics Challenges Mountains of data Unclear what insights are needed Difficult to access data Don t have data when it s needed Limited visibility? Waiting on IT to build reports Assumption-based decisions Culture resistance No time to learn new BI tools! Lack of data-based discipline 5

6 John Boyd 6

7 OODA Loop for Decision Making 7

8 Top 5 Benefits of Data Visualization Absorb information faster More effectively see connections Earlier detection of emerging trends Allows interaction with data Fosters a new business language and discussion - Brian Gentile, Data-Informed.com 8

9 ISIR Insights Insights powered by CampusMetrics 9

10 Operational Insights Verification Aging Cycle Time Trends Processing by FAO Student Task Status Breakdown Insights powered by CampusMetrics 10

11 AwardLetter Usage Insights Volume of Opened AwardLetters Time of Day for Viewing AwardLetters Opened vs. Unopened AwardLetters Avg Time Award Letters Are Viewed Insights powered by CampusMetrics 11

12 Moving to Data-Based Decisions Many Financial Aid Offices and Universities are making crucial decisions on enrollment strategies, efficiency tactics, and student experience initiatives based on financial aid assumptions, not data and insights. 12

13 Elements of Data Driven Culture Data-literate Know and understand what you re measuring. Curious Testing mindset, scientific approach Action-oriented Go beyond describing what happened. Describe what you ll do because of insights. Communicative Share data-driven insights in all communication not just formal reporting. Skeptical Ask essential questions, wait to see data before deciding 13

14 Data Leadership Be responsible for the quality of your data Ensure data is obtained in any new project Ask what data is this opinion based on? Provide data analysis training for all Be data transparent even with unflattering data Provide open-access to data for your team Assign all objectives an indicator or target Allocate budget for data collection projects Test improvements on small populations and measure Don't use data to lay blame 14

15 4 Disciplines of Execution Focus on a Wildly Important Goal (WIG) Act on Lead Measures Keep a Compelling Scoreboard Create a Cadence of Accountability 15

16 Discipline 1 -Wildly Important Goal (WIG) Team should have no more than 1-2 WIGS Goal to move from x to y by a date The leader can veto but not dictate the goal 16

17 Discipline 2 - Act on Lead Measures Leading not lagging Measures what team believe will drive the WIG Measurable goals tracked on shorter timeframe 17

18 Discipline 3 Create a Compelling Scoreboard Simple and visual Public and easy to see Frequently updated with progress against WIGs and lead measures Everybody can see at a glance whether team is winning or using 18

19 Discipline 4 Create Cadence of Accountability Weekly review of scoreboard Report on Individual commitments report on successes and challenges Plan to clear path for success in the next week make new commitments Quarterly review of impact of lead measures are they the right measures? What process changes are needed? 19

20 CL Example - Development WIG Q Grow from 40 customers in Dec 31, 2015 to 100 customers by Dec 31, 2016 Product Development Reduce cycle time average from 21 days to 10 days by June 30, 2016 Impediments Avg. # of impediments for rolling week period Days In Groomed Avg. time a story is groomed ready to pick up for development Development Time Avg. In Progress Development time represents under 70% of total time Q2 Goal: < 5 per week Q2 Goal: <= 4 days in stage Q2 Goal: < 70% of story total 20

21 Key Takeaways Speed up your OODA Loop: Use data to speed up observation of your environment and get feedback to test hypothesis Visualize Data: Visual data is faster to interpret and act on than tabular or detailed data Manage a Data Driven Culture: You don t need to be a data scientist, but you do need to be a data leader Use Data to Execute on Strategic Priorities: Focus on a WIG, Act on Lead Measures, Keep a Compelling Scorecard, Tools are Good: Data Driven Leadership, Culture and Process is Complete Package 21

22 Q&A 22