If you work with alumni and their personal data, you are a data manager. It doesn’t matter what your job title says. The moment you’re responsible for records, you’re responsible for their quality, keeping it up to date and storing it safely and securely. The problem is that nobody hands you a manual on your first day. In my posts I’m trying to give you just that, a few handles and starting points on which you can fall back when starting out. The one you’re reading now is here to help you better understand the fundamental data principles and how they fit into alumni relations.
Quick note before you keep reading: this post leans heavily on CRM and database best practices. If you’re not working with a CRM yourself, parts of it might feel abstract. But if you’re interested in the data principles behind good alumni relations, stick around. The ideas apply to any system, even a well-organized spreadsheet. And if you want the lighter entry point first, my post on two books that shaped my data thinking is a good warm-up.
Three data principles to understand
When it comes to managing alumni data, or data records in general. There is a wealth of information, rules, guidelines and principles you can learn or apply. But for me, in my role as alumni data specialist, it comes down to these three: Data Quality, Data Governance & Master Data (or One source of Truth). I will go over each of these three principles in my posts and talk you through examples in practice. I’ll cover their application in alumni relations and some ways on how to apply the principle.
Let’s get started with the first principle:
Principle #1 – Data Quality
Data quality can mean a lot of things, for a lot of different teams. In alumni relations, however, it means one thing: How accurate, complete, consistent and up to date is your stored alumni data? Right from the start you can see four different topics in that definition that you could work with.
First up: Accuracy. Accurate data means correctness. take email addresses for example. An accurate email addres can be defined as active and complete. That can be as simple as the inbox is receiving emails and it follows the basic structure of name@domain.com.Try and run a search in your CRM system today to find out if all your records at least follow the basics for a accurate email address.
Second one is Completeness, which in data-terms simply means that everything that should be present is there on a record. Of course what “should be present” is decided by you or your CRM-admins. But for the sake of practice let’s say that for each alumni record you need: A name, email address, postal address, graduated program and graduation year. If all those data-points are present, a record can be considered “complete“.
Dig into your records and try and find out how many records are even complete to begin with. You’ll be surprised how many gaps you’ll still find. Filling these gaps alone can be a long, time-intensive project. But it’s worthwhile to do so in the long run.
Third topic to tackle is consistency. Let’s say you have 10 alumni who say they work for Philips. And another 50 who entered Philips Electronics NL, Philips N.V. or some other version of the company’s name. Then your careers office asks how many people work for Philips. Your colleagues in marketing claim it’s 50, you say 10. You’re both counting the same group, just using different names. And at that moment, your inconsistent numbers are costing you your credibility.
Fixing this can be quite straightforward. For company names, you might need a manual audit to bring the variants together. But country or city names can simply become drop-down fields, so anyone entering data picks from the same list. Of course, this is not always feasible, especially for records that are not static. Companies come and go by the minute, job titles change over time. But it’s worth noting: these inconsistencies in your data are what’s stopping you from forming a fully accurate image of your alumni community.
Audit your top 10 groupable fields today: sort by employer, city and industry, and count how many spelling variants of the same value you find. That number is both your inconsistency score and your goal to fix.
Our last topic: keeping data up to date
The moment you enter personal data into your CRM, it can already be considered outdated. Period. It’s a weird statement, but kind of true. Data in your CRM is static: it does not change unless you alter it, or unless the alumni updates it themselves via forms or other methods. And unless you can convince your CRM-team to build a direct connection between LinkedIn and your alumni records, you will never know directly when an alumnus switches jobs. Frankly, you’re probably the last to know.
So how do you fight something that decays by default? Not with one big cleanup, but with a cycle. Fresh data is not a project you finish, it’s a habit you build into your year: check, campaign, update, repeat.
Here’s your Monday action: pull a report of alumni records that haven’t been verified in the last three years and sort them by graduation year. That list will help you determine who your next update-campaign should target, and how fresh your database actually is.
Closing off
Data quality is where every alumni data journey starts. Not because it’s the most exciting work, but because every campaign, action or event you’ll want to run later will be so much more better if the data that backs it is solid. So run the audits, count your gaps, and make data-qualtiy a habit rather than a project. It will be worth it in the long run.