World Enough and Time
How to spend your time as a busy data leader in a nonprofit or public sector organisation
I’ve spent over a decade working with data in academic, charitable, and local government organisations, and in my experience the key to achieving data excellence is the ability to keep delivering value with data. To achieve this, proficiency and sustainability are the necessary but not sufficient preconditions.
Proficiency is the product of velocity and quality (“quickly deliver good data”), which comes from having capable staff. This has to be paired with sustainability (“keep quickly delivering good data”), which comes from capable staff who share knowledge and maintain their data products.
But why is it not enough to be proficient and sustainable? Because it’s possible to quickly deliver good data that serves little value to the business or anyone else, or for the value to remain misunderstood. For a data team to achieve excellence, then, they need to keep quickly delivering value with data.
But value isn’t a simple thing, it’s a multi-dimensional concept. In my experience, it has four dimensions.
What are the four dimensions of data value?
When we talk about the value of data, it is neither absolute nor binary. What do I mean by this? Well, data that is valuable to one stakeholder might appear useless to another, so its value is relative. And even when two stakeholders agree that the data is valuable, they might not agree on the quantity of that value, which means it’s on a continuous scale. For this reason, I think the purpose of data is more than to simply give you a causal model of your business in your head, and that’s especially true in nonprofit and public sector organisations.1
In the area where I’ve worked I find that it’s worth asking these questions of the data you’re using: is it governance data, operational data, strategic data, some combination of the three, or is it neither of these and therefore of niche interest? Understanding which dimension(s) of data you’re working in is the key to understanding how valuable it is and to whom, and unlocking this as a data leader enables you to better prioritise your team’s efforts.
Managing your time effectively as a data leader is important wherever you work, but that’s especially true in resource-constrained nonprofits and public sector organisations. As Andrew Marvell wrote in very different circumstances: “Had we but world enough and time”; except you don’t so you need to be deliberate about where you’re focusing your energy.2
Let’s look more closely at these four dimensions of data in nonprofit and public sector environments:
1. The Governance Dimension
We have to report this, either by law (e.g. for a regulatory body, or for elected politicians) or because of internal policies, procedures, and controls mandated by leadership (e.g. to manage risk). In most places where I’ve worked this is usually prioritised above the others below because the risk of non-compliance can be high.
2. The Operational Dimension
We need to know this to run the business. This is often prioritised above strategic data because lots of businesses are more focused on delivering what they usually do than they are on delivering against their strategy (if they even have one).
3. The Strategic Dimension
We can use this to measure progress against our strategic objectives. It’s less common for an organisation to prioritise this because it involves making difficult decisions about what not to do, but the best will put time into it.
4. The Niche Dimension
If it’s neither of the three dimensions above then it’s niche data: it’s probably of interest to one team or one person (maybe just you), or of interest to an important external stakeholder (for good reasons or not). Depending on who’s asking, this data could be just as mandatory to provide as data required for governance, operations, or strategy, despite the fact that it falls under neither of those headings.
There’s value in data that serves governance, operational, and/or strategic needs. There’s even value in niche data, especially if it’s overlooked3. The only time data serves no value is when absolutely no one is interested in it.
For the rest of this piece I’m going to give some examples of the kinds of data that fall into these categories. I’m drawing from my own recent experience working at Auckland Council, but as a disclaimer I want to note that the strategic and operational perspectives are only illustrative and don’t necessarily reflect what the business is doing (the governance points are real - you can look those up). As with everything I write in this newsletter, I’m speaking on behalf of myself here and not that of my employer.
The four dimensions of data value in practice
Governance + operational + strategic
Library visits: we might use it to determine staffing levels (operational), we report it in the Annual Report for the public as one of our Long Term Plan measures (governance), and it might help us to measure progress against our strategic objectives (for example, increasing access to our library spaces). Any data that sits at the intersection of governance, operations, and strategy should be prioritised above all else.
Regulatory + strategic
Total checkouts of items in the library collections: we report it in the Annual Report (governance) and we might use it to measure progress against our strategic objectives (for example, promoting our physical and electronic collections). It’s not operational because total checkouts include electronic items that aren’t linked to a library, and most physical items are checked out without staff assistance.
Regulatory + operational
Internet sessions (PC + WiFi): we report it in the Annual Report and we could use it to determine staffing levels (staff time for assisting customers with public computers and connecting to the free WiFi), but perhaps it’s not a strategic priority right now to move the dial on this measure. This is an example of a measure that’s important to regulators or other key external stakeholders, but maybe less important to the business right now (in those circumstances you’d probably just want to maintain it at current levels at least).
Strategic + operational
Customer service desk interaction times: we could use it to determine staffing levels at some sites (operational) and we may want to see it track down as newly-trained staff become more proficient at their jobs (strategic).
Governance only
Data that has to be shared with an internal or external overseer but which is of no operational or strategic interest suggests one of two things: either there’s disagreement about the value of this data (whilst someone thinks it’s important, the wider business doesn’t), or that it’s caught in between a regulatory review cycle (everyone recognises it serves no value, but there’s nothing they can do about it yet).
Operational only
Data that’s needed to run the business but which serves no governance nor strategic purpose. This data can be especially helpful to determine whether the activity it’s measuring should continue or not, given it’s not a strategic priority at present and isn’t part of any governance reporting. We can also use this data to determine whether a change in business processes has led to an improvement, but if such changes don’t come from the strategy (which would also make it strategic data) then we may be focusing on the wrong areas.
Strategic only
Data that is of strategic priority but serves no governance purpose and isn’t needed to run the business is often data that you’re not capturing yet. It might relate to new activities, or activities that previously flew under the radar. In time, this may become of interest at the operational and/or governance level.
Niche data
If someone is interested in data that isn’t needed for governance and serves no operational or strategic purpose, then it’s niche data. This is the kind of data that might be used in ad-hoc analysis to satisfy curiosity about emerging trends or curious anomalies in the business. It might also be used for market analysis, or to further innovation initiatives that might lead to new discoveries and ways of working.
Niche data probably constitutes the majority of the data available to many organisations, but in nonprofits and public sector organisations with limited resources it’s especially important to be deliberate about delving into it; do you really have the time to go down a data rabbit hole when governance, operations, and strategy depend on your expertise as a data professional?
Where’s the true value?
This might seem like a dizzying array of categories for data, but it does reflect my own experience of how data is often used in organisations. But when data isn’t just one thing, how do we determine which data delivers the most value? How do we prioritise our data work?
I think there isn’t a single answer to that question, because it depends on the concerns of the person who wants to use the data. For example, if you’re worried about the repercussions of inadequately reporting your regulatory data then you’ll probably prioritise this above all else, but someone else might consider it less valuable if it serves no strategic purpose and will treat it instead as a tick-box exercise.
To give another example, you might foresee a seismic fundraising opportunity for your nonprofit that might be exploited through careful analysis of niche data, but if your team is busy with reporting on day-to-day business operations then your colleagues might not see the same value in the data you’re digging into.
For what it’s worth, I think you should prioritise data of strategic value above all else; if it intersects with the operational and/or governance areas then all the better, but data without a strategic purpose isn’t going to help your business move forward. That’s important because if the business isn’t achieving strategic objectives then over time it will underserve its constituents, which in the nonprofit and public sectors include beneficiaries, donors, residents, students, alumni etc.
So if you want to know which data to spend your time on, I think the solution is simple: follow the strategy.
See https://commoncog.com/becoming-data-driven-first-principles/, which brilliantly lays out how to actually know if the data you’re looking at is telling you something important or not. In my four-dimensional model, improving a metric goes to the heart of what I think we’re looking for in the strategic dimension.
That’s right, it’s actually seventeenth century smut: https://en.wikipedia.org/wiki/To_His_Coy_Mistress
For example, I’m willing to bet that many organisations, including those in the nonprofit and public sectors, overlook data that underpins indiginous data sovereignty. How many New Zealand public sector organisations, for example, are disaggregating Māori data in a way that prioritises Māori needs and aspirations? See the resources available from Te Mana Raraunga for more: https://www.temanararaunga.maori.nz/