From good to exceptional: how nonprofits can simplify data to elevate impact

Delivering sustained impact in the world of nonprofits is challenging.

The stubborn, systemic issues purpose-driven organisations address can leave you feeling like the protagonist of a good novel, navigating intricate plot twists with clues and red herrings around every corner.

Limited resources, multiple stakeholders and shifting policy priorities can generate a flood of tasks, activities and processes that suck resources and staff away for the things that make a difference. Add to this the intricate web of donors and funders, beneficiaries, and regulatory requirements — it’s enough to give anyone a headache.

When everything is so complex, how do you separate signal from noise to show your work is truly making a difference?

As counter intuitive as it may sound, when used wisely, the data you collect as part of your organisation’s operations can reduce complexity and elevate your performance from good to great.

Wait. Won’t more information make things more complex? Not if you keep it simple.

Why we embrace complexity over simplicity

I used to coach my daughter’s netball team, which had a wide range of ages and abilities. After an unsuccessful month trying to get them to play as a team, I realised I was giving them too many instructions.

I was making the simple, complex.

Humans often prefer complex solutions over simple ones. This tendency to choose the complex is known as complexity bias.

Like other cognitive biases, complexity bias is a mental shortcut that helps us evade the need to understand. When a challenge we face contains too much information, we label it complex. We then use this as a way of absolving ourselves from the task at hand: by think something is more complicated than it really is, we abandon our duty to resolve it.

In his book Living with Complexity, Donald A. Norman uses the example of a morning ritual most of us enjoy, drinking tea or coffee, to highlight our inclination to prefer the complex over the simple:

In principle, it should be easy to make a cup of coffee or tea. Simply let the ground beans or tea leaves [steep] in hot water for a while, then separate the grounds and tea leaves from the brew and drink. But to the coffee or tea connoisseur, the quest for the perfect taste is long-standing. What beans? What tea leaves? What temperature water and for how long? And what is the proper ratio of water to leaves or coffee?

Norman’s point is that we innately see complexity as superior, and simplicity as boring and dull. In a contest of the complicated versus the simple, we’re likely to choose complexity.

I changed my coaching approach to one of simplicity, I restructured training to focus on improving only two skills at a time. During matches I provided a maximum of three instructions. Within six months we improved from getting beaten 40–0 to making a grand final.

All I did was simplify the message.

Complexity bias and data: how this unfolds in purpose-driven organisations

Complexity bias leads us to overcomplicate things, even in how we work with data and analytics.

For example, data engineers can get stuck searching for the perfect data model. Analysts can get lost trying to deduce meaning from mountains of variables that are more chaotic (devoid of any structure and meaning) than complex (parts of a bigger connected system).

That’s not to say that some complexity isn’t necessary. But we argue that the complex work needs to remain in the background. If it is brought to the foreground, or the frontline, in purpose-driven organisations trying to deliver social impact, then data can do more harm than good.

We end up overcomplicating data to the point that it loses all ability to generate insights.

When this happens, data and analytics is reduced to a supporting role around compliance and regulation and not seen as crucial to innovation and the creation of business value. Passionate people, driven and committed to the cause, measure their impact in terms of what feels good rather than what is proven to work. End users are skeptical and mistrusting of data and its ability to help them perform their role.

Employed this way, data becomes part of the problem of greater complexity, not the solution.

Distilling the essential from the non-essential

Like my daughter’s netball team, I think we’re often guilty of providing people with too much data and information. All this does is allow complexity bias to kick in.

Not all data is essential. It might be useful for various activities that underpin your business, but that doesn’t make it essential to your mission or your cause. Nevertheless, we seem to collect it anyway.

If we take the time to engage with our data with the explicit goal of simplifying things, we can separate what is essential. This is what helps measure impact and elevates performance against the mission.

Instead of using data to tick boxes concerning compliance and regulation, we generate crucial metrics and frameworks to measure the impact that matters most. It becomes easy to provide clarity to staff, clients, stakeholders and constituents. Rather than assessing performance on what feels good, intuition and data to work in tandem to create better decisions.

Data becomes a service to the organisation and generates new business ideas and creates additional value. It becomes a solution to complexity.

Three steps to reducing complexity in your data

Determining what data is essential eliminates unnecessary complexity. But how do we do this? I’ll share three key concepts I believe are critical.

A good data strategy

Just about every business these days involves data. And as Bernard Marr has said, if every business, regardless of size, is now a data business, every business, therefore, needs a robust data strategy”.

What makes a good data strategy? Start with the overall business strategy and map where data can help achieve business goals. Next, align data technologies with the organisation’s unique needs to deliver this value.

Tell stories

Unless you place context on your data and use it to tell a story, it is useless. As Nate Silver points out in his fantastic book The Signal and The Noise, numbers have no way of speaking for themselves. We speak for them.

The job of data is to help you tell a story — your impact story. Effective communication of data-driven impact can engage donors, empower beneficiaries, and foster trust among your staff.

Telling stories with data starts with asking the right questions and exploring patterns. It’s more detective work than rocket science.

Build a data-driven culture

Data-driven cultures don’t grow overnight. Building one requires a collective mindset that values data as a tool for continuous improvement. Whether you’re the executive director or a program coordinator, we all play a role in creating this culture.

What can executives do to begin creating a data culture? Start small, focus on relevant data, and gradually build your data culture muscle. And keep it simple!

Keep it simple to increase your impact

Data is a key ally in the fight for social impact.

But unless you take the time to simplify your data and align it to what matters most (your purpose), it will remain overly complex and difficult to manage. The more complex something is, the greater chance it has of collapsing.

It takes a lot of effort to maintain complexity.

Classifying data into what is essential and non-essential, can generate a level of elegant simplicity that will help navigate difficult challenges, measure impact, and communicate stories with authenticity.

The process itself is a never-ending story. It’s not about reaching a destination; it’s about the journey of improvement. Using data as your compass, can take you from good to exceptional and elevate your impact in unimaginable ways.