While there are many ways that organizations can tackle their data goals, Daniel Liadsky of Purpose Analytics believes that organizations are most likely to succeed when they engage in regular self-assessment, implement change in small steps, and emulate best practices from other industries.
Increasingly, it feels like the promise and peril of a data-centric world is no longer the stuff of science fiction. Artificial intelligence is already changing lives daily, whether as an embedded technology in eyewear that describes its surroundings to people with visual impairments or as a diagnostic tool that helps radiologists spot cancer in medical images. Yet, at the same time, AI has also been the source of deepfakes, famously creating a realistic image of the pope wearing a white puffy jacket and generating convincing videos of politicians spouting disinformation. We now live in a world in which technology simultaneously helps us to see better and teaches us to distrust what we see.
For non-profits, imagining themselves in this future can be particularly daunting. No doubt, this is due in part to the seemingly sudden arrival of these tools, but also because many non-profit leaders have an uneasy relationship with data and technology. Many believe that their organizations have made little progress in developing their data capacity, and only 16% of Canadian non-profits surveyed in Salesforce’s 2022 Nonprofit Trends Report (the fifth edition) scored high in their “ability to leverage data to inform decision-making, reach new audiences, personalize communications, and forecast fundraising income.” Indeed, many non-profits still labour over routine data-gathering tasks, including preparation of their quarterly and annual funder reports. Not surprisingly, it is difficult for non-profits to reconcile the gulf between their current reality and the possibilities offered by the latest technology.
It is difficult for non-profits to reconcile the gulf between their current reality and the possibilities offered by the latest technology.
While there is no shortage of inspirational articles and other thought leadership about the exciting things that data-enabled non-profits will be able to achieve in the future, less attention has been given to mapping out how we will get there. To address this blind spot, this article and two that follow will focus on issues that the non-profit sector will need to tackle on its way to a data-informed future.
In this first article, I will start with actions that non-profits can take on their own to make meaningful progress along their data journey. The perspectives that I share here are informed through my experience managing Purpose Analytics, a non-profit data consultancy that serves the non-profit sector. Through our work supporting a range of organizations, we have observed that many are either unsure about how to begin building their data capacity or they try to do too much too fast. While there are many ways that organizations can tackle their data goals, we believe that organizations are most likely to succeed when they engage in regular self-assessment, implement change in small steps, and emulate best practices from other industries.
Assess early, assess often
No matter where you are in your data journey, all non-profits can benefit from completing a data-maturity assessment, a survey that provides a third-party measurement of an organization’s capability to use data. This is something that can be repeated periodically, with your first assessment providing a baseline from which you can measure improvement over time. There are many tools to choose from, but in our consulting practice we recommend one created by Data Orchard, a UK-based consultancy that offers its data-maturity framework and assessment tool for free. Data Orchard’s framework considers data capability across seven themes – uses, data, analysis, leadership, culture, tools, and skills – and categorizes each theme into five stages of maturity, ranging from “unaware” to “mastering.” The framework is presented in a format that reminds us that the pathway to mastery is a progression through intermediate steps – the data equivalent of learning to walk before you can run.
No matter where you are in your data journey, all non-profits can benefit from completing a data-maturity assessment.
Using the results from your data-maturity assessment, the next step is to decide how best to invest your time and resources based on the thematic areas you wish to advance. If you are not sure what to prioritize, look for the thematic areas in your data-maturity assessment that scored the lowest and choose these as a starting point. At this stage, we find that some non-profits are tempted to look for new software as a quick fix to their data challenges. However, software migrations can be costly and can disrupt operations, often introducing as many new issues as they solve. We advise organizations to stick with their existing software until they have clearly outgrown its capabilities.
Start small
To advance your priority areas, we recommend taking an incremental and iterative approach that builds on your existing processes rather than embarking on large, complex projects. For example, there is almost always someone in every organization who manually pulls data together to report to the funder, the board, or a staff team. Consider opportunities to streamline this process so that it takes less time from start to finish. Reducing production time is a critical step toward remaking data into a helpful resource rather than a burden to manage. This could be as simple as developing templates that streamline the report-making process or working with a consultant to replace manual steps with automation tools. You can then convert these time savings into new data-related tasks such as running the report more frequently or expanding it to include additional data elements.
A prototype-to-implementation progression is a powerful way of testing and refining ideas.
We also recommend being explicit about where and when data should be reviewed rather than thinking of it as something you keep around and use only when requested. For example, if counting the number of people receiving services is an important statistic for your organization, consider making it a standing agenda item with data to support it. This does not need to be high tech to start – try including one or two statistics such as the number of current service users and the number of new service users in the last month. Observe how the team makes use of this information and what other questions this generates. By committing to review a narrow set of data points on a regular basis, you are, in effect, prototyping a low-tech dashboard. And by making it routine, you will find that even low-tech solutions can be effective in building your organizational culture and comfort around using data. Once you are confident that your prototype dashboard has lasting benefit, you can invest in upgrading it to a high-tech, automated solution.
In general, this prototype-to-implementation progression is a powerful way of testing and refining ideas. Not only is it more forgiving if your assumptions are incorrect or if you did not anticipate every need or exception, but once you have refined your prototype, it becomes a blueprint for a more permanent implementation. This is particularly valuable when you are working with a consultant as the prototype helps to clearly communicate your business requirements.
Follow the well-travelled path
While we encourage organizations to focus on their existing processes, it is still important for non-profits and especially those that are further along in their data journeys to gaze a little bit into the future. The purpose of this activity is to think about what comes next after you have taken care of your most urgent needs. No one can accuse the sector of being cutting edge when it comes to data and technology, so we do not need to be prescient; we can simply look at what other industries are doing now for this glimpse into our future. I think of my dentist’s office, which allows me to book appointments online, sends me text message reminders and post-appointment surveys, and remembers when it is time for the next set of X-rays. The digital tools that facilitate these interactions not only improve the service-user experience, but they offer effective ways to collect and use data. Many of these tools are low-cost, relatively easy to implement, and can sit beside existing information systems – they should be commonplace in the non-profit sector, yet adoption has been slow.
No one can accuse the sector of being cutting edge when it comes to data and technology, so we do not need to be prescient; we can simply look at what other industries are doing now for this glimpse into our future.
In general, the way in which we have learned to create and interact with data in our personal lives seems to have had limited influence on non-profit approaches toward using data. In our personal lives, we rely on algorithms to show us the next news item on our phone or to recommend a new song in our playlist. We check the number of likes on our social media posts and the number of steps we took last week. And we find delight in being reminded with a photo of a memory from a year ago. We do this all voluntarily and we marvel at seeing this small fraction of our total data footprint reflected back at ourselves.
In our professional lives, we leave a similar data trail on our work devices and the digital tools that we use daily. This information remains largely unutilized by non-profits, but much like our personal data it can also be used to generate recommendations, produce digests of our activity, and offer comparisons to past history. While the volume of staff-generated data can appear to be overwhelming, it does not need to be harnessed all at once. Just one question and a few relevant data points are all you need to get started. Here again, following the prototype-to-implementation progression is a good approach.
For instance, suppose that as part of a quality-improvement initiative, staff are planning to reach out to service users if they fall out of contact with the agency. This is the community-services equivalent of the email that my dentist sends me when I haven’t made an appointment in a while. Of course, staff could routinely review all service-user files looking for the last date each person was contacted, but this may not be reliable or sustainable on an ongoing basis. Instead, you might pull data from your client-management system with the contact history for all service users. This list could be filtered to retain only the service users who have been out of touch for more than 30 days. And the list could then be reviewed regularly at team meetings. You now have a low-tech recommendation engine that provides staff with a list of service users that they need to contact. As with the earlier example, automation is the obvious next step for enhancement. Instead of creating this list by hand, an automated tool could fetch the data and schedule an email to go out to the appropriate staff the day after a service user passes the 30-day mark.
For most non-profits, a focus on incremental change and regular assessment offers the fastest path to realizing a future state where data and technology assist in benefiting the communities that they serve.
It should be evident by now that the suggestions above will not transform non-profits into power data users overnight, nor do they contemplate the advanced applications of data and technology that other industries are rapidly adopting. No doubt, the non-profit sector will eventually benefit from these advances, too, but if we look too far into the future, it is possible to lose sight of the journey ahead. As my dentist demonstrates, there are other industries that are further along on this journey, so non-profits need not worry about blazing their own trail as much as they should concentrate on following in the footsteps of others. For most non-profits, a focus on incremental change and regular assessment offers the fastest path to realizing a future state where data and technology assist in benefiting the communities that they serve.
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In the second instalment of this series, I will explore what changes are required in the non-profit labour market to support the data needs of the sector as it matures. While we have already seen a rapid expansion of specialized data roles and teams in both the private and public sectors, this has yet to arrive in the non-profit sector. In this article, I will reflect on lessons that we can learn from past labour-market transitions, opportunities to prepare non-profits and data professionals for this transition, and considerations that need to be made for smaller non-profits that may never be large enough to host their data expertise in-house.
