Measuring engagement can answer crucial questions, with a little more work

Measuring alumni and constituent engagement is no longer a new thing. Many Advancement shops do it. Not all of them have settled on a solid key performance indicator, or set of KPIs.

We are still evolving on this front. After measuring consistently for five or six years, now it’s time to consolidate what we’ve learned and align the tool with a new operating model for engagement.

A lot of work got us this far. We laboured over the specific components of engagement (giving, event attendance, volunteering, accepting visits, and other things) and how to weight them. We created a score for each individual, and developed some aggregate reporting.

The work was good, but now we need to understand the significance of our metrics and how they can spur action. More work lies ahead.

What questions to ask of our metrics? A few thoughts:

How deep? How successful are we from year to year in engaging the maximum number of people who were available to be engaged? What is the ratio of engaged to engageable? By engageable I mean all constituents who are contactable and genuinely available to us this year. The exact definition is arbitrary. If a person who graduated 15 years ago has never had any meaningful interaction with us, they are probably not “available”. Including them will dilute the KPI with people beyond the reach of our communication and programs. I suggest a ratio rather than a percentage of engageable; if someone not considered engageable does come to us, we can count them on the left side of the ratio without needing to add them to the right side as well.

How good? How successful are we in engaging who we want to engage? To what extent did we involve loyal donors, engaged alumni, major gift prospects, people with bequest intentions, influential community members, and other preferred, high-value constituencies? This measure of quality can be used to evaluate events, especially when eschewing large social shindigs in favour of smaller, higher-octane gatherings. Quality, not quantity – even in the all-digital era.

How effective? How successful are we in moving people in numbers from one stage of engagement to the next? We need to know what engagement looks like at each stage in order to properly locate individuals.

Getting to these answers requires us to move on from “what’s in and what’s out.” We need to define “engageable,” decide who’s in our favoured constituency, and figure out how to quantify our engagement pipeline goals.

Data to dollars: Thoughts on valuing data as an institutional asset

A core principle of data governance is that data, regards of who creates or has custody of it, is an asset of the institution. But what’s an asset? Some ground-breaking work in Canada and Europe might point the way to putting an actual dollar value on our data.

For the first time, Statistics Canada last year tried to estimate the value of the country’s stock of data. The total value of data, databases and software, and data science in Canada is between CAD $157 and $218 billion, the agency estimates. (1)

This is a first stab. Like every national statistical organization in the world, Stats Can is playing catchup with the explosion of digital data capture and processing and its importance to the economy. Data is clearly part of a country’s productive capacity, but it doesn’t show up in national accounting statistics such as gross domestic product (GDP).

The same for universities. Buildings are tangible assets recorded on the balance sheet. Data is intangible and, unfortunately, invisible to generally accepted accounting principles. What if the university were able to value data as a capital asset?

The value of software and analysis is more easily measured than the store of data. Subtract those two from the Stats Canada estimate, and the value of data by itself ranges from $105 to $151 billion for the whole country.

Using share of GDP for our sector, I estimate that publicly-funded institutions such as hospitals and universities might be holding $6.6 to $9.5 billion in data assets, assuming all subsectors have been equally productive. (2)

Drill down farther, and my back-of-envelope calculation finds that Dalhousie University’s data might be valued between $24 and $35 million. (3)

There are too many layers of assumptions to feel confident in the accuracy of that range. (For example, it is unclear whether or where research data is counted in the national statistics.) Better than trying to decompose the national figures, we might try valuing our data using Stats Canada’s methods.

Our university’s data is not for sale, so a direct market value cannot be determined. Stats Canada values data at the cost of producing it, plus an estimated return on capital. Data is produced by people engaged in data-related activities: Working with many years of labour-market surveys, the agency identified roles likely involved in the creation of data (data entry clerks, researchers, analysts and so on), estimated a likely percentage range for the amount of their job spent on data creation, and calculated the cost based on salaries plus associated costs.

Value based on cost of creation sets only the lower bound; as new uses for data are found, its market value could change dramatically. More work remains to be done. But as a starting point it might hold promise for institutions seeking to measure their data as a financial asset.

In the system of national accounts, data may come to form a whole new asset class. For businesses, non-profits and other organizations, adding data investments to financial statements would give it added prominence.

Which brings me back to data governance. The value proposition for data governance at a university is hard to define. Counting data as an asset — literally — might be a step in the right direction.


  1. Statistics Canada has produced two highly readable papers on this topic: “Measuring investment in data, databases and data science: Conceptual framework,” released 24 June 2019, and “The value of data in Canada: Experimental estimates,” released 10 July 2019.
  2. Economic production in the non-profit sector totalled $169.2 billion in 2017, representing 8.5% of Canada’s gross domestic product (GDP). In recent years, publicly-funded institutions such as hospitals and universities have accounted for about 6.3% of GDP, which leads me to conclude that our subsector might be holding $6.6 to $9.5 billion in data assets.
  3. In 2017, education institutions in Canada accounted for $46.5 billion, or 2.3% of GDP, suggesting total data assets of $2.4 to $3.5 billion. Education institutions in Nova Scotia accounted for $1.4 billion, or 0.07%, of GDP – implying data assets of $74 to $106 million. Taking the size of Dalhousie University relative to all the others in the province, our stock of data could be worth between $24 and $35 million. (Thank you to Juuso Vesanto, BI Analyst with Dalhousie Advancement, for pointing me to an Economist report on this topic which gave me the idea to use share of GDP.)

Data is an expensive tool. We should teach people how to use it.

When you buy a tool you have to learn how to use it, or you’ve wasted your money. Our team understood this when we implemented a new CRM system: If frontline staff used it and used it well, the investment would deliver on the promise of facilitating advancement of the mission.

Data is also a tool. Managers and decision-makers will succeed if they know how to use data. The question is, what have we done to maximize on that investment?

During our CRM implementation, we had more than 50 working sessions with frontline staff – focus groups and training sessions that involved nearly everyone in configuring the software and applying it in their work. So many hours!

CRM was big, but our investment in data, spread over years, is much bigger. Like other advancement shops we have staff employed in the collection, creation, and management of data, staff who design and maintain the infrastructure for securely storing, assembling, and preparing the data, and staff who use the data to develop reports and business insights.

That investment far exceeds the cost of any CRM, yet has it been matched with an equivalent degree of training in its end-use by managers and decision-makers? For us and many other organizations, the answer is no.

Operations can get very good at translating between the data and the business, but staff across Advancement must be able to speak the language. Author and advisor Bernard Marr says, “… organizations that fail to boost the data literacy of their employees will be left behind because they are not able to fully use the vital business resource of data to their business advantage.” (1)

Organizations large and small, in every sector, are coming to this realization. A 2019 Gartner survey found 80 percent of organizations now plan to start developing staff competency in data literacy. (2)

Data literacy simply means the ability to understand data in the context of one’s business knowledge. It includes knowing where the data comes from, how it’s defined, the methods used to analyze it, and having a view to applying it to achieve an outcome.

You don’t need to be a mechanic to drive a car. You don’t need to be an analyst to make decisions with data. The next big leap forward in data-informed decision making might lie in helping more and more staff across the organization learn how to drive.

  1. Why Is Data Literacy Important For Any Business?” by Bernard Marr (see also “What Are The Biggest Barriers To Data Literacy?“)
  2. Design a Data and Analytics Strategy,” Gartner Inc., 2019