Guide: Starting Your Grantmaking Data Visualization Journey

Countless articles and talks through the years have promised that ‘data’ will fundamentally change grantmaking and the philanthropic sector. While this prophesied revolution appears to always be just around the corner, the reality has often been far more incremental and evolutionary. In 2023, however, it increasingly feels like the opportunity exists for a majority of foundations to realize (at least some of) these long promised benefits. 

While there are a number of exciting data developments that contribute to my measured optimism, including the Philanthropy Data Commons and TAG’s 2023 Data Curriculum, the main opportunity we currently see is in interactive data visualization. Data visualization is a practical way to make better use of available data to help inform grantmaking and operational decisions. Importantly, data visualization is not new and it’s not new to philanthropy. It's been increasingly tried, tested and proven to bring value to the grantmaking context. And the tools, technologies and skills required to support effective data visualization are increasingly accessible to organizations of all sizes (for example with the upcoming launch of Fluxx’s Grantelligence tool).

At Grantbook, we are seeing increasing numbers of foundations coming to us with data visualization needs. They have often outgrown the built-in reporting tools or legacy pdf reporting solutions they are using to try and meet their data needs. Existing reporting processes are simultaneously burdensome on staff time while generating reports with limited interpretability that quickly become outdated. While Grants Management Systems have varying reporting capabilities with some able to produce (limited) visualizations, we’ve found that investing in modern SaaS “Business Intelligence” (BI) or data visualization tools tends to be the most effective path to more effective data use and lower administrative burden.

Above is just one example of a data visualization dashboard, customized for a team primarily interested in year over year comparisons. 

Instead of focusing on the specific tools and technologies in this guide, we wanted to provide some more general advice that we typically give to clients as they start out on their journey with data visualization. 

  1. Set realistic expectations

This is the number one piece of advice we give. Be clear with yourself and leadership that ‘better data’ won’t solve everything. Implementing a BI tool can be a big investment, not only in terms of direct tool costs but also in the time investment required by staff to get the most out of the tool. Even the best dashboards and visualizations fall flat without users who are willing and able to use them to effectively inform decision-making. We have noticed a trend that often, the biggest challenges and rewards of the successful implementation and adoption of a new tool are cultural, rather than purely technical. 

  1. Start with your data needs

Effective data visualization is centred around users. If you just build it, they probably won’t come. This is why we always start our visualization projects by digging into a foundation's core data needs, gathering individual user needs and  then working collectively to prioritize the foundation’s overall requirements. We then typically use this list as a backlog to guide us through the report building process.

  1. Build alongside users and iterate together

Building data visualizations is complex work, but if you can set up the right conditions, you can drastically cut down your timelines and obstacles.

Once we’ve established clarity around a foundation’s data needs, we gather some initial requirements from a subset of core users who share those key needs. We then build an initial draft and take it back to the same group for additional feedback. We fold in that feedback and repeat for two to three iterations, after which we find it best to stamp “V1” on the output and push it out into the wild, where users can start to really stress test it as we  move on to repeat the process with the next need.

  1. Recognise that data democratization is a process

If BI tool promotional materials and industry articles are to be taken at face value, the future of data within organizations is full “democratization”. This vision of all employees self-serving their own insights with minimized centralization is laudable but to some grantmaking organizations may seem laughable. In trying to meet the data needs of all foundation staff, we have to meet users where they are. In our experience, while some staff can pick up and run with building data visualizations, most staff do not immediately have the capacity to learn a new tool and skillset. By all means empower your power users – but most staff want their insights carefully curated and readily available (for now).

  1. Grow your data culture and governance

Once you’ve understood and met users where they are, you can start to guide them towards better data-informed decision making. At first, this will likely involve building trust and confidence in your dashboards and underlying data products. Next, you can start to build increasing data literacy and data skills among staff. Finally, you can begin to embed a strong data culture, where data and particularly data analysis supports your entire organization’s every day processes. However, this is unlikely to be a neat linear process and it’s unlikely that everyone will embrace this change evenly. You’ll likely have to nurture and coax your growing data culture, embrace the fits and starts, and find and capitalize on your data champions to help make it a reality.

Alongside these recommendations, it's also useful to know what not to do. The three biggest barriers we’ve seen to success are:

  1. Don’t let ‘bad data’ stop you

Many organizations are reluctant to invest in data visualization because they are concerned they have ‘bad data’. These concerns are often rooted in valid assessments of data quality but any analyst will tell you there are no truly perfect datasets. Instead of waiting for perfect data, implementing data visualization alongside good data governance practices can actually help expose the most important data issues and incentivise meaningful fixes.

  1. Don’t fall for technology hype

When it comes to data visualization and BI tools, there are a lot of different solutions available. While there are plenty of pros and cons to each, there is also a lot of feature parity, resulting in BI tool marketing often using buzzwords as a differentiator. Beware the promises of on-demand AI to build charts and “Magic” data pipelines; for the majority of organizations, hype-adjacent features are not anywhere near as important as the basics, like intuitive interfaces and chart customizability.

  1. Don’t let data visualizations stagnate

As with anything, the path through data visualization to data informed decision-making is not always a smooth one. One of the biggest failure points we’ve seen is when dashboards are left unattended, usually due to staff turnover. We’ve seen plenty of sorry dashboards full of stale data, abandoned by users who had no input or training on their functionality.. The result is not only a loss of trust in those specific dashboards but in dashboards as a mechanism for delivering insights in general, which can harm future initiatives at the organization. If you’re not actively managing or maintaining a dashboard, it can be better to retire it completely than to let it slowly fade into obscurity (though better still to resource ongoing support).

Hopefully this post helps you to think through some ideas and reflect on how data visualization can be a mighty tool for your organization. If you feel like now might be the right time, then it is over to you to get started! Our final pieces of advice are: think big but start small, and know that you are not alone. There are plenty of peers who are just starting out on this journey, and those who have been at it a little while and are happy to share their experience. Seek out support from your networks and member organizations (TAG is currently running a data governance education series). Whether you’re feeling confident, curious, or confused, Grantbook is also here to support foundations at any stage in their journey, as we continue to evolve our Data Visualization and Reporting services to help organizations of all shapes and sizes get the most from their data. We’re always ready to help!

Jamie Fawcett's headshot

Jamie Fawcett

Implementation Specialist

Data Architecture

On parental leave until November 2024.

Jamie brings a background in data governance, data strategy, and data science to the Grantbook team. Prior to joining Grantbook as an Implementation Specialist, Jamie worked with various government, commercial, and philanthropic clients to design and build robust data infrastructure. 

After completing a BSc in Politics and International Relations, Jamie spent four years doing research and consulting work around data sharing and data governance at the Open Data Institute. While working on the challenges of sharing data between people and organizations, he met lots of people doing really interesting things with data—which sparked a desire to pursue more practical hands-on data science, analysis, and visualization skills. That desire eventually led him to the University of Oxford, where he earned a Masters in Social Science of the Internet, exploring the application of cutting-edge computational social science methodologies, including social network analysis, agent-based modelling, and big data analytics.  

Following the completion of his Masters, Jamie relocated from the UK to British Columbia, and joined the Grantbook team as the first fully remote employee.