RSMUS.comNonprofit organizations are increasingly excited about leveraging AI, but many are coming to the realization that AI is only as good as the data behind it. A lack of a robust data infrastructure, including siloed systems and fragmented processes for collecting, storing and managing data, is often the barrier keeping nonprofits from successful AI initiatives. In fact, Gartner predicts 60% of AI projects will fail by 2026 due to weak data foundations.* Data is the fuel to AI and if data is incomplete, outdated or incorrect, it will yield a flawed output. In this blog post, we outline how nonprofit organizations can build a data infrastructure and practical steps to get started preparing your data for AI.
How to build a data infrastructure for AI readiness
AI is only as good as the data you feed it. However, the process of building a solid data infrastructure doesn’t happen overnight. Don’t feel overwhelmed; many nonprofit organizations start from multiple spreadsheets and messy systems. The key is to get started, stay consistent and gradually evolve.
1. Audit current systems
Start reviewing and documenting the current systems you leverage. Note any integrations between systems, where data gaps exist and where hygiene is needed. Additionally, many systems have AI functionality baked into the platform. During your audit, capture what AI capabilities you currently have at your fingertips.
Tip: Take this time to standardize definitions across teams. For example, define what a “Current Donor” vs. “Lapsed Donor” means. Having a shared definition across teams and systems helps AI models draw more accurate conclusions.
2. Get serious about data hygiene
Transform your data before chasing AI tools. AI doesn’t fix your data issues, only amplifies them. If your data is incorrect, incomplete, or inconsistent, you will produce unreliable results.
Tip: Begin with a thorough data hygiene process by identifying and correcting inaccuracies, removing duplicates, and standardizing formats.
3. Plan for the future
Once you complete your audit and understand your current state, you can start plotting how your nonprofit organization can move towards a unified data model in a systematic way. Focus on moving disparate data into a unified system to centralize and integrate data silos. Next, review and map your current data model to future systems to ensure a smooth transition and prevent loss of critical information. Prioritizing integration over volume and having a smaller, well-connected set of trusted data is far more valuable for AI.
Tip: Document data context as well as data fields. AI needs to understand why data exists. Without context, AI is prone to generate irrelevant outputs, failing to provide value.
4. Establish data governance
Define clear policies for data privacy, security and ethical use; ensure compliance with regulations like GDPR. Your nonprofit organization should clearly establish who is responsible for monitoring quality, access and change control of your data.
Tip: Communicate openly with stakeholders about how their data will be used to maintain trust.
5. Start training staff
According to Nonprofit Tech for Good, 40% of nonprofit staff say that no one at their organization is educated in AI and only 4% have AI-specific training budgets.** Conduct internal training sessions to equip teams with knowledge of AI concepts and data handling best practices. Creating and supporting a data-driven culture ensures successful AI adoption and long-term growth.
Tip: Assessing your current AI tools during your audit is a great on-ramp for your staff to start familiarizing themselves with AI.
Follow the crawl, walk, run model to become AI data-ready
Establishing a data infrastructure may seem daunting and overwhelming. Following these guidelines and taking a scaffolding approach will allow your nonprofit organization to transition into a data-ready state while bringing your staff along to skill up and become more comfortable with AI. The crawl, walk, run model is simple: start where you are with the data you have and slowly build up. Even if your nonprofit organization is not ready for large scale AI initiatives, these data improvements will pay off in stronger insights, increased efficiency, and better constituent engagement.
For more information or if you need support getting started, reach out to RSM today.
Check out our other recent nonprofit tech blogs here.
Sources:
* Gartner
Emily Smartt
Emily serves RSM in Boston as a supervisor. She is an experienced Senior Data Analyst with a demonstrated history of working in the higher education industry. Skilled in Microsoft SQL Database, Statistical Data Analysis, Databases, .NET Framework, and C#. Strong information technology professional with a Master of Business Administration - MBA focused in Data Analytics from The University of Tennessee at Chattanooga.
