For many campuses, the hardest part of data work is not the analysis itself. It is the moment at the end of the meeting when everyone nods at the dashboard, closes their laptops, and heads back to their day. The charts were clear. The conversation was good. Yet nothing really changes for students (or faculty and staff or alumni…you get the idea).  

Part 6 is about that last mile. This capstone in the Data Literacy Field Guide is where you assemble everything from Parts 1 through 5 and practice closing the loop between insight and action. The goal is not a perfect strategic plan. The goal is a repeatable habit: start with a clear question, use shared metrics and safe stories, then leave each conversation with one written decision, an owner, and a plan to check what happened. If you have ever worked with Plan–Do–Study–Act cycles or other continuous improvement tools, the rhythm here will feel familiar: try something small, look at the data together, and then decide what to do next. 

As the 2026 EDUCAUSE Top 10 reminds us, data only improves outcomes when it is embedded in routines, roles, and shared norms, creating a data-centric culture across campus. This Field Guide is one small, practical way to help campus teams move from “we have data” to “we have a way of using data together.” If Part 1 gave you shared language, Part 2 helped you write better data questions, Part 3 clarified the KPIs that matter, Part 4 improved your data stories, and Part 5 added guardrails for privacy and governance, Part 6 is where you put those pieces to work in everyday decisions. 

What you will learn 

Over the course of this post, we’ll cover how to: 

• Turn a data conversation into a one sentence decision that is specific and testable.  
• Name an owner or a small team that will act on the decision.  
• Set a timebox so the decision becomes a short experiment rather than a forever commitment.  
• Define a simple success signal and a stop or extend rule.  
• Capture decisions in a lightweight log so people can see what has been tried and what happened.  

You are not creating a new bureaucracy. You are giving your existing meetings and dashboards a clearer “so what.” 

From Question to Written Decision 

By now you have a few good questions that have survived the Question to Query Path from Part 2 and a handful of KPIs and charts that are defined in KPI specs from Part 3 and Part 4. Part 6 starts when you are in the room with those ingredients and someone asks, “So what do we do with this?”   

A useful pattern is to end every discussion with a one sentence decision:   

Decision: We will [action] for [population] in [timeframe] because [signal].  

For example: 

• “We will require advising appointments for all first-time, full-time students with fewer than 15 credits at Week 3 this fall because our credit momentum KPI shows they are twice as likely to stop out.” 
• “We will pilot extended tutoring hours in Biology and Chemistry this spring because our historical DFW rate for those subjects has increase 25% over the past three semesters.” 

The point is not to cram every detail into the sentence. The point is to write down the commitment clearly enough that someone who was not in the meeting could understand what you plan to do and why.  

When you write the decision, reference the specific metric or chart that supports it. That keeps the decision tethered to the evidence and reminds everyone that this is part of a broader data story, and not a hunch. 

Naming an Owner and a Timeframe 

A decision without an owner is a wish. After you write the decision, name a person or a small team that will carry it forward. Be as concrete as possible. “Advising” is a department. “Maya in the First Year Advising Center” is an owner.  

Then give the decision a timebox. Instead of “from now on,” try language like:  

• “We will try this for Fall 2025 and revisit it in December.”  
• “We will run this outreach for six weeks and review the results at the midterm retention meeting.”  

 A timebox turns the decision into a small experiment instead of a permanent policy change. It also gives everyone permission to stop or adjust if the results are not what you expected, which reduces fear around trying something new. 

Defining a Success Signal and Stop/Extend Rule 

Next, describe what “good” would look like in terms of your metrics. You already have leading and lagging indicators from Part 3. Use them.  

For example, if your lagging goal is Fall to Fall retention for first-time, full-time students, your leading signals might include:  

• Share of students at or above 15 credits by the end of the first term.  
• Percentage of at-risk students who completed an advising appointment.  
• Percentage of students who used tutoring within two weeks of a referral.   

A simple way to frame this is:  

• Success signal: “We will consider this pilot successful if advising appointment completion rises from 60 percent to at least 75 percent for students under 15 credits.”  
• Stop or extend rule: “If we do not see at least a 5-percentage point improvement by the end of the term, we will stop this version of the outreach and revisit our approach.”  

You do not need a complex statistical model. You need a clear expectation that everyone in the room understands. The success signal keeps you from calling every change a win. The stop or extend rule keeps you from quietly continuing efforts that are not moving the needle in the right direction. 

A Simple Decision Log 

Once you have a decision, owner, timebox, and success signal, capture them in a lightweight decision log. This does not have to be a new system. A shared spreadsheet or table in your existing project space is enough.  

A basic decision log might have these columns:  

• Date  
• Question or KPI  
• One sentence decision  
• Owner  
• Timebox or revisit date  
• Success signal  
• Outcome (What happened when you checked back)  
• Notes or next step  

Filled out, a row might look like this:  

• Date: August 15, 2025  
• Question: Where should we focus Week 3 outreach to improve Fall to Fall persistence for FTFT students?  
• Decision: We will prioritize outreach to FTFT students in Psychology and Nursing who are under 15 credits at Week 3 this fall, because they make up the largest share of at-risk students in the credit momentum chart.  
• Owner: First Year Advising Center  
• Timebox: Fall 2025, revisit at December retention meeting  
• Success signal: At least a 10-percentage point increase in advising completion for Psychology and Nursing students under 15 credits by Week 6  
• Outcome: Met; advising completion increased 12 points and Fall to Spring persistence improved 4 points in those majors  
• Notes: Extend pilot to selected STEM programs next term  

Over time, this log becomes a quiet but powerful artifact. It shows leaders that data conversations are producing concrete actions, not just reports. It also gives new staff a way to see what has been tried and what worked or did not work, instead of starting from scratch every year. 

Lightweight Adoption Planning 

The last piece of Part 6 is making sure this habit has a home. Decisions and decision logs should live in places where people already gather, and not in a separate data world.  

A few patterns that work well:  

• Add a five minute “Decision check” to the end of existing meetings that review dashboards. Use that time to write the one sentence decisions and update the log.  
• Link key KPIs and charts directly to their most recent decisions in your reporting tool, so someone can click from a metric to the history of actions it has informed.  
• Choose one or two recurring meetings, such as a student success committee or enrollment management council, where reviewing the decision log is part of the standing agenda.  

To make this concrete, we created a short From Insight to Action Habit Planner. Use it in one or two meetings where this practice will have the greatest impact. 

How To Get Started 

Download our Decision Log and From Insight to Action Habit Planner and add them as a discussion point for your next data team meeting.  

Consider starting by: 

1. Complete three rows on the Decision Log during your next three meetings and share the Log with the group. 
2. Pick two meetings to try the From Insight to Action Habit Planner in.  
3. Find at least one collaborator who can help you gain traction at your institution.  

You don’t have to do everything at once. Start small and remember that perfect is the enemy of good. Give some of your ideas a try and refine as you go. Building small habits now can offer big returns as the academic year continues.  

If this still feels overwhelming or you’d like a little extra support, don’t hesitate to reach out to your Strategic Solutions Manager. Our Client Experience Team (CET) is always here to partner with you and help bring your reporting and data analytics goals to life. 

Where the Field Guide Goes Next 

This is the sixth and final post in our series on Data Literacy. Across the series, you have built a shared language, learned to write better questions, clarified your metrics, told clearer stories, and added guardrails for safe and ethical use. Part 6 invites you to turn that foundation into a simple habit of action, so every important chart has a decision on the other side of it.  

Data literacy is not a one-time training. It is a campus practice that grows every time a team uses data to make a thoughtful, transparent choice for students, faculty, staff, or alumni. Our hope is that this Field Guide gives you a set of tools you can return to whenever you need to reset a conversation, refresh a metric, or move from another “interesting chart” to one more real decision.  

We would love to hear how you adapt these ideas on your campus. If you have a decision log, a chart, or a small experiment you are proud of, send it our way so we can keep learning together.