For more than 25 years, we’ve focused on one thing: helping colleges and universities use data and technology to move real work forward. Today we’re excited to introduce the Field Guide Series, a six-part collection of short, practical modules you can put to use right away. With downloadable templates and quick exercises you can complete with your team, our goal is to help you find value faster and make progress on your campus.  

We’re starting the series with Data Literacy because shared language and simple conventions are the fastest way to reduce dueling dashboards, protect student privacy, and speed up decisions. So, let’s dive in and get to work—together! 

If you’ve ever sat through a meeting where “retention” meant three different things, or two dashboards told two different stories, you know why data literacy matters. This post introduces Part 1 of our Field Guide on Data Literacy, and gives you a simple, repeatable way to build shared language and a one-page map of where your data lives. It’s practical, fast, and designed so you can start today. 

So, What is Data Literacy? 

Let’s start with a simple definition: data literacy is the ability to read, write, and communicate with data. Just like alphabetic literacy, at its core, data literacy enables us to have meaningful engagement with our campus communities so we’re all speaking the same language. In addition to communication, data literacy includes knowing where the data came from, what was included or excluded, how it was analyzed, and how to use it to make decisions. Our working definition is largely drawn from the work at EDUCAUSE and is probably something you’ve seen before.  

If data literacy sounds broad, that’s because it is. It’s not an analyst-only skill. Leaders, advisors, faculty, finance, IR, and IT all benefit from a shared baseline of understanding. When we use the same language consistently, meetings move faster, and reports become easier to trust. In education settings, data literacy also means building role-appropriate skills so each group, including leaders, faculty, advisors, staff, and technologists, can access, interpret, and act on education data responsibly. The U.S. Department of Education’s Forum Guide to Data Literacy  frames this well by defining practical skills and structures for making better decisions with data. In higher education, that context includes understanding academic terms and cohorts, census dates, privacy obligations such as FERPA, and how to handle small groups without inadvertently identifying students. 

Why This Matters Now in Higher Ed 

In today’s higher ed environment, using data to inform decisions is more important than ever. Here are some concrete reasons you can provide your leadership for why now is the time to improve data literacy on your campus: 

• Better decisions, faster — Shared vocabulary and conventions reduce time spent debating numbers and increase time spent deciding on shared actions. 

• Student success & equity — Clear cohorts, denominators, and small-n practices reduce misinterpretation and protect privacy when reporting on student groups. 

• Operational alignment — A simple “data ecosystem map” helps teams find the right source of truth, understand refresh timing, and route requests to the right steward and avoiding “dueling dashboards.” 

• Campus-wide capability — Data literacy is now a foundational skill for staff and students. Building these basics makes every other data initiative easier, from KPI tracking to responsible AI. 

Start with Ten Common Terms 

To get started on our data literacy journey, we first need to agree on some shared data literacy language (data literacy about data literacy…meta). We’ve selected ten common terms below to help your campus stay aligned.  

1. Metric — a number that describes something (applications this week). 
2. Key Performance Indicator (KPI) — a priority metric tied to a goal and reviewed on a cadence (first-year fall-to-fall persistence). 
3. Dimension — a way to slice a metric (department, modality, residency). 
4. Filter — a rule that limits a dataset (first-time, full-time undergraduates only). 
5. Cohort — a group defined by a shared condition (2024FA first-time, full-time). 
6. Census date — the official snapshot when counts are frozen (10th day of class). 
7. Grain — the level of detail per row (one row per student per term). 
8. Numerator / Denominator — the part and whole in a rate (persistence = enrolled next fall ÷ original cohort). 
9. Baseline — the comparison level for change (last year’s term-to-term retention). 
10. Source of Truth — the agreed system/table for official data (SIS census view). 

These are our definitions with an example. We recommend you download our Core Definitions Starter Pack and customize the examples for your institution. Ultimately, you want to keep the language short, specific, and visible. Once you have some agreed upon definitions, be sure to save this in a space your teams use every day. 

Conventions that Prevent Confusion 

Once you agree on some definitions for common terms, it’s helpful to have a handful of “house rules” to make your reports consistent and reproducible. Additionally, these rules provide report users with additional details about the data they are using.  

• Use ISO dates (YYYY-MM-DD) in filenames and labels and note the time zone once on each artifact if you have colleagues working across multiple time zones. 

• State the time window explicitly on reports and data extracts: “Weeks 1–4 inclusive” or “rolling 7 days ending <date>.” 

• List inclusions/exclusions and the grain on reports and data extracts (e.g., one row per student per term). 

• For any rate, show the numerator and denominator and use percentage points when you talk about change. 

• Suppress or aggregate small samples (we suggest any time n < 5) by default to protect privacy. 

• Add refresh cadence and last refresh timestamp to footers so people know how fresh the data is. 

• Consider versioning your dashboard and reports and keep a small change log so people know what’s new and what’s been changed.  

• Provide alt text for all charts and target 4.5:1 contrast. Add one sentence stating the decision the chart supports as a caption. 

Just like our Core Definitions Starter Pack, our Naming & Date Conventions are examples to help you get started. Edit with your teams to make it yours. 

Map Your Campus Data 

With your common definitions and “house rules,” you’re off to a great start. Now, it’s time to create a lightweight Data Ecosystem Map. The point isn’t to catalog everything; it’s to make the first three systems obvious and usable. We recommend starting with SIS, LMS, and CRM. 

For each system, capture: 

• Owner (office) and Data Steward (person). 

• Refresh cadence & window (with timezone). 

• Source of Truth? (Yes/No and for which counts). 

• Access tier (Viewer/Analyst/Steward/Owner). 

• Small-n threshold (default 5) and PII risk (Low/Medium/High). 

• Key entities and primary keys (e.g., Student_ID, Term_Code). 

• Known caveats/quality notes (“LMS grades lag one week”). 

• Landing location/connection and contact. 

You’ll be surprised how much confusion disappears when these fields are filled in—even roughly. Route questions to the right data steward, set expectations on freshness, and stop guessing how the data can be used. 

How To Get Started 

Download our Core Definitions Starter Pack, Naming & Date Conventions, and Data Ecosystem Map and add them as a discussion point for your next data team meeting.  

Consider starting by: 

1. Writing the definition for four common terms your campus mixes up. Write an example for each and post the resulting list somewhere that’s accessible to everyone. 
2. Adopt three agreed upon conventions and start using them in your next report or dashboard. 
3. Fill in at least two rows on the data map, adding one caveat per system. 
4. 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, 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 first post in our six-part series on Data Literacy. Building a strong foundation is key to tackling what comes next. Throughout the series, we’ll cover everything from turning fuzzy problems into clear data questions to strengthening your visualization skills so you can tell more compelling data stories. Each installment builds on the last, but you can use Part 1 on its own and see immediate results. 

Join us in two weeks for Part 2: Asking the Right Data Question!