This is the second of four articles in the series “why it’s so difficult to agree how many of something we have.”  

(note: this is a Guest article by my colleague Rav Ubhi-Adams. First published on Linked In)

In the first of our articles, @AlexLeigh set out a high-level approach for answering the common question we all get asked or ask – how many students do we have? Populations are a potential solution to ease some of the frustration on this. Both for those asking and attempting to answer what feels like a simple question! In this article, we’re going to give you an example approach on defining and verifying your populations 

Start where you mean to end 

This is the single most important step! You cannot begin to define a population without knowing what you’re trying to count. I know, I know, everyone in a university thinks the same thing; that they know what they need and their use case is the most important. Our first article suggested you start with a population which already exists or to create a new population based on a university-wide requirement. A population which immediately comes to mind is that used by that well-loved process (tongue-in-cheek!), student number planning. Whichever you see real value in addressing, make sure it’s something with has longevity through this process. You don’t want to do all the legwork to then not be able to deliver the outputs: 

So here are some questions to ask yourself before you start: 

Do you have data specialist resources? 

Can these staff access system data to apply population specifications? 

Is there critical university reporting which will illustrate the value of this work? 

Moving from the what to the how 

So, you know what you want to get to. What do you do next? At this point we need to be honest – although I have an inkling you know already – this won’t necessarily be easy! You need to make this work as structured and holistic as possible to create credibility and to begin to address some of those data culture barriers we’re all too familiar with! While there isn’t a universal approach, data governance is the consistent wraparound. This is how we’ve been successful:  



Bring people with you 

Whether you’re starting with a population which already exists and needs review and/or audit, or if you need to start from scratch to define a population, you need to engage with key stakeholders e.g., data/reporting analysts, end users of the data being defined, process owners where the defined data is a pipeline, sponsors of relevant data driven processes. You need to recognise that this work shouldn’t be a “thou shalt” programme. Partnering with people will set a solid foundation of buy-in, trust, sponsorship, verification, and prove to be invaluable for the data governance framework that needs to ground everything.  

Engage people with a structured and transparent plan. Here’s what we recommend: 

  • Be clear on your objective – what’s the problem, why are you trying to solve it now, how are you formulating a solution, what will the benefits be? 
  • Briefly describe the current state – what works well, what doesn’t work well, challenges, opportunities. 
  • Who and why – who are you engaging with and why, set the scene for needing their input 
  • Anchor the work – describe how data governance will be supporting, from define through to business as usual.  

We then need to understand the uses around the counting issue of our stakeholder uses. We do this by capturing a description of use cases for the issue, and for each: 

  • Who’s the audience for the data use. 
  • The purpose of the data. 
  • Understand what i.e., what issues or limitations are you facing, do you have any current data definitions you use, do you forward on your data use for other purposes. 
  • How the data is delivered i.e., lists, dashboards, tables etc.  
  • The timescales at which the data for this use is required 

This insight is critical to the investigate and formulate stages of any population definition exercise. Don’t stop there with your engagement though! These stakeholders will assess the validity of any data specifications you create. There’s also the potential they’ll be champions of the solution once it moves into BAU and hold a role in the data governance framework you design.  

 Potential stakeholders and their data population needs 

Having demonstrated the criticality of specifying the counting problem you need to solve using a population, and of stakeholder involvement, it would be useful to have worked examples, right? That’s what we’ve got planned for our next article! We’ll give you examples of stakeholder groups, their data needs, and potential data populations to help solve some of their reporting/process needs. Stay tuned!