Data, Analytics, and AI Trends for Nonprofits to Watch in 2025

​In a fast-paced world, 2025 is set to mark a transformative milestone for data and analytics. This blog post will not only examine trends impacting various industries but will also consider the domain of nonprofits and charitable fundraising organizations, where data plays a pivotal role. Fundraising nonprofits increasingly leverage data to optimize strategies, enhance donor engagement, and make impactful decisions that align with their missions. Drawing insights from the latest research and expert predictions, this post explores key trends that organizations should prioritize to remain competitive and innovative.

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​Trend #1:
​Generative AI gets even more important

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​For instance, e-commerce platforms are using generative AI to create personalized product recommendations, while customer service operations employ AI-driven chatbots to resolve inquiries faster and more efficiently. By 2025, generative AI is poised to become a foundational technology for innovation and automation across industries. The impact of generative AI will extend far beyond traditional content creation, including images, text, music, and now also video.

​In particular, this will mean:

• The rise of domain-specific bots tailored to particular industries and the integration of large language models (LLMs) into various processes. Fundraising will not be an exception. Use cases will range from donor-facing applications (e.g., bots you can write instant messages to, asking where your donations go) to internal tools (e.g., systems that help identify lessons learned from previous fundraising campaigns).
• In fundraising, generative AI also offers the potential to design even more donor-centric processes and communication strategies, such as personalized messaging based on donor preferences and giving history.​

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​Trend #2:
​Predictive AI has come
​to stay in fundraising

While predictive AI and machine learning are transforming various industries, their full potential in charitable fundraising has yet to be fully realized. This is likely due to a combination of barriers, including resource constraints that limit access to the necessary expertise and tools, as well as limited awareness and some resistance to adopting advanced analytics. Additionally, the lack of integrated data platforms and other key technological enablers also plays a significant role.
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Taken together, there are several compelling reasons why predictive AI is and will continue to be a critical driver for fundraising nonprofits in 2025 and beyond:
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• Significant Efficiency Gains: Data science helps streamline fundraising activities, ensuring resources are utilized more effectively.
• Decision Support: Advanced analytics provide actionable insights that inform strategic decision-making.
• Integrated, Multivariate Data Views: By combining multiple data sources, nonprofits can gain a holistic understanding of donor behavior and emerging trends.
• Utilization of All Relevant Data Sources: Data science enables nonprofits to harness and leverage diverse datasets for greater impact.
• Cross-Industry Relevance: Innovations from other sectors can be adapted to enhance and modernize fundraising efforts.

As Mark Twain once said, "The secret of getting ahead is getting started." This wisdom applies perfectly to data science in fundraising. Even smaller-scale optimizations - such as identifying statistical twins for campaign responders - can serve as a significant lever, delivering measurable improvements and increasing organizational appetite for using predictive AI in fundraising.
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​Trend #3:
​Synthetic Data will gain momentum

Synthetic data, which mimics real-world data while maintaining privacy, is gaining traction as a powerful tool for training AI models, conducting simulations, and testing systems without exposing sensitive information. In healthcare, it is being used to create patient data sets that adhere to privacy regulations, enabling research and AI training without risking sensitive information. The finance industry leverages synthetic data to model fraud detection scenarios and enhance security algorithms. Its versatility and compliance advantages make synthetic data a critical enabler for AI development in 2025.
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Why synthetic data matters:

• Accelerating AI Development: Synthetic data helps overcome data scarcity and enables rapid iteration of AI models.
• Ensuring Privacy: It provides a viable alternative to using real-world sensitive data, reducing regulatory risks.
• Versatility: Applicable across industries, from healthcare to finance, for creating diverse and robust datasets.​​

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​Trend #4:
The rise of Data Lakehouses

A quick glossary to start with:

• Data Warehouses: Centralized platforms designed for analyzing large volumes of structured, historical data.
• Data Lakes: Repositories that store raw, unstructured, or structured data in its original form, offering flexibility for future analysis.
• Data Lakehouses: Hybrid architectures that merge the scalability of data lakes with the structured querying power of data warehouses.

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Data architectures are transforming with innovations like data lakehouses, which combine the best features of data lakes and data warehouses. A data lakehouse offers the scalability of a data lake for handling unstructured data and the analytical power of a data warehouse for structured queries. 

So, why do Data Lakehouses matter?

• Unified Data Management: Streamline data operations by eliminating the need for separate storage and analytical systems.
• Flexibility and Scalability: Support for both structured and unstructured data, enabling real-time and historical analysis from a single platform.
• Cost Efficiency: Lower storage costs compared to traditional data warehouses, while maintaining high performance for analytics.
• ​Business Value: Enhance decision-making with a holistic view of organizational data.​

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​Trend #5:
Data-Driven Culture as
​Organizational Imperative

Building a data-driven culture is no longer optional​; it has become a strategic necessity for organizations seeking to thrive in a competitive landscape. A data-driven culture emphasizes the value of data as a core asset and integrates it into decision-making processes at all levels of the organization. Reports from Gartner and BARC highlight the importance of fostering data literacy and cultivating a data-first mindset to unlock innovation and growth opportunities
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Why a data-driven culture matters:

• Improved Decision-Making: A data-driven culture empowers organizations to make informed, evidence-based decisions rather than relying on intuition or guesswork.
• Faster Innovation Cycles: By leveraging real-time insights, organizations can identify trends and opportunities quickly, enabling them to innovate faster.
• Enhanced Collaboration: Data-driven organizations encourage cross-departmental collaboration, breaking down silos and ensuring teams work together with a shared understanding of business goals.
• Increased Accountability: When decisions are guided by data, teams are more aligned and accountable for outcomes, fostering transparency and trust across the organization.
• Competitive Advantage: Organizations that embed data into their workflows are better equipped to identify growth opportunities, optimize processes, and deliver exceptional customer or stakeholder experiences.

Before the benefits come the efforts. Promising tactics and measures to build and nurture a data-driven culture include:

• Leadership Commitment: Leaders must champion the importance of data, setting a clear vision and fostering a culture of curiosity and accountability.
• Data Literacy Programs: Invest in training to improve employees’ ability to read, understand, and communicate insights from data.
• Centralized Data Access: Implement robust data infrastructure, such as data lakehouses, to ensure seamless, organization-wide access to trusted data sources.
• Clear Metrics and KPIs: Establish measurable goals and KPIs to assess progress and continuously refine data-driven initiatives.
• Continuous Improvement: Encourage experimentation, innovation, and the use of advanced analytics and AI to drive meaningful outcomes.

By prioritizing data literacy, democratizing access to data, and embedding analytics into everyday decision-making, organizations can unlock the full value of their data assets and position themselves for sustainable success.

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We wish all our readers, friends, and partners a great start to an inspiring and successful 2025. Let’s make more out of data together!

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Sources and further reading

• ​Charting a Path to the Data- and AI-Driven Enterprise of 2030: Published by McKinsey, this report explores strategies for leveraging data and AI in enterprises.
• Data, BI and Analytics Trend Monitor 2025: Published by BARC, this report provides insights into data and analytics trends for 2025
• Over 100 Data, Analytics and AI Predictions Through 2030: Published by Gartner, this report offers predictions and trends in analytics and AI. Access it here:
• The 10 Most Powerful Data Trends That Will Transform Business in 2025: Published on Forbes, highlighting key business-oriented data trends.
• Top 9 Data Analytics Trends to Watch in 2025: Published on Medium by MetricMinds, focusing on cutting-edge analytics developments.
• Top 10 Data & AI Trends for 2025: Published on Towards Data Science, exploring data and AI advancements with industry-specific implications.