The nonprofit world has, somewhat grudgingly, begun to embrace the use of data in decision-making. This is good, not because numbers are everything, but because informed decisions are usually better than off-the-cuff choices. Public Interest Management Group’s applied research shows that data orientation is among the attributes most highly associated with nonprofit organizational success.
The term “dashboard” has become commonplace in nonprofit board and management team meetings. It refers to an assemblage of numbers about different aspects of the organization’s work and business. I prefer the term “scorecard,” which feels more proactively concerned with results, but they’re both fine. The point is to better understand how the organization is doing, and data can be informative.
But is any dashboard a good dashboard? Not necessarily. Let’s go through a few common misconceptions.
1. More data is better.
Boards and management teams often receive overwhelming quantities of data. This type of reporting is like dumping a haystack on the front lawn. What we need is a few needles. Good data reporting simplifies a messy situation for people with limited time and bandwidth. An effective dashboard or scorecard includes a small set of important metrics that convey the essence of the story of how you’re doing.
It’s about quality, not quantity.
2. Faithful reporting on financial metrics ensures fiscal responsibility.
Wrong, for a few reasons.
First, many boards and managers haven’t received training in how to interpret financial data. Reporting, in itself, is not the point – understanding is. Boards and managers don’t need CPA credentials, just some basic orientation, which can make a world of difference in turning dazed looks to inquisitive conversations.
Second, reporting, in itself, never ensures responsible stewardship. It’s what you do with the data – the decisions you make, and the way these decisions affect your value propositions to stakeholders – that translate to stewardship. Financial reporting must be in a context of embracing and using information for constructive purposes.
3. Higher “ROI” is better.
As a generalization, no.
ROI is code for return on investment. Using the code (or the full term) may make someone sound like the smartest person in the room, but it’s really pretty simple: the amount you reap from an expenditure divided by the amount spent to get results. For nonprofits, it’s usually used in the context of fundraising, though it’s equally applicable to sales. (Note that fees for services, not donations, are the greatest source of income in the nonprofit sector, another misconception.) All nonprofit leaders should have an understanding of returns on their investments to generate revenue.
If you spend $1 to get $3 in income, that’s an ROI of 3.0. Pretty slick work. That’s clearly better than spending $1 to get 85 cents back. But is it better than getting $10 back from that $1 investment?
The answer, which may be counter-intuitive, is no. There’s a low end for a “good” ROI, but also a high end. An ROI that is too high (and I would certainly say that an ROI of 10.0 fits this label) means your organization is probably leaving money on the table. This comes from a rule of economics, essentially that the low-hanging fruit comes at lowest cost. (Picking fruit is a perfect analogy – you need ladders, machines and a good workers comp insurance policy to start attacking the tops of the trees.) The marginal ROI (the return on the last dollar you spend) will usually drop as you invest more in fundraising or sales, but as long as your marginal ROI is greater than 1.0, you are still bringing more money into the organization than you’re spending.
If, like most nonprofits, you could do more to meet your mission if you had some more money, a really high ROI should not be your goal. Instead, you should aim to optimize your ROI by spending money until that point where it is no longer productive.
In other words, unless your organization’s mission is to be as efficient as possible (and it isn’t), a high ROI can be a sign of a problem.
4. Nonprofits’ outcomes usually can’t be measured.
Programmatic outcomes may not always lend themselves to direct measurement – an example is a health promotion program for children that aims to decrease chronic diseases much later in life – but there are various ways to use proxy data as practical estimates for outcomes.
Every mission-related program’s effectiveness can be estimated, usually within a modest budget. This isn’t as easy or precise as determining a small business’s profits, but don’t fall victim to the excuse that it can’t be done. (In the example above, we can measure behavioral changes and/or short-term health indicators linked by existing research to long-term outcomes.)
By not measuring the outcomes of your mission activities at all, you may either be underselling the value of your work or turning off some of the many donors that want evidence before making big gifts. A commitment to measurement is the first step – finding metrics may not be as hard as you thought.
5. Measures of the organization’s activity tell us if we’re doing a good job.
They do not!
Being busy is not to be confused with being effective. Your organization’s mission is not to stay busy. That’s what activity measures are – indicators of how much work you’ve done. They don’t speak to what you’ve accomplished in meeting your mission.
Data such as numbers of students served, meals provided, and housing units created don’t get to the essence of why those activities were planned in the first place. Worse, if they aren’t done in the context of a valid theory of change (a very common problem), activity alone may be tangential to, or even aggravate, the problems we’re really trying to solve.
It is important to understand the outputs of your work, but only alongside outcome measures that assess effectiveness and impact.
6. Data measurement is a luxury we can’t afford.
Bet you weren’t expecting a bonus myth, but here it is, at no extra charge… This one (you may be following the trend) is untrue.
A data-driven organization needs four things: basic data systems, data collection mechanisms, analysis skills, and the will to use data to inform decisions. Data systems need not be costly. Collection methods can be simple and, with current technology, very cost-effective. Analysis skills are straightforward and trainable. The will to use data is free, but also the hard part – it requires commitment and culture change.
The costs of data orientation aren’t high, but more importantly – the costs of not using data can be huge, in the form of lost funding, uninformed decisions, and unclear focus.