Data Engineering for Nonprofits: Empowering Change Through Data

Author: Sarthak Ahir

As nonprofits, we strive to make a real difference in our communities. In today's data-driven world, limited resources often leave us struggling to leverage the valuable information we collect. Data engineering offers a solution, unlocking the power of your data to help your organization better communicate your impact or optimize your operations.

Imagine data as a puzzle. Each piece holds a story. Data engineering helps you gather, analyze, and interpret these pieces, painting a clear picture of your organization's impact and potential.

What is Data Engineering?

Data engineering is the process of extracting data from its source, transforming it as needed, and loading it into appropriate data storage. This process is crucial for organizations to systematically understand their data and use it for strategic decision making. Data that is not thoughtfully extracted from its source, transformed into the appropriate format, and loaded into the appropriate data store puts organizations at risk of making errors that impact their communities. This key set of tasks is often called “Extract-Transform-Load" or “ETL”.

Data engineering plays an important role in managing the entire data lifecycle, from acquisition to consumption. They design and implement scalable and robust data pipelines, ensuring that data is available, accessible, and usable for various analytical and operational purposes. It is also the process of building the broader data architecture of how different systems relate to each other.

In addition to data pipeline development, other common data engineering tasks are:  

• Data Modeling: Designing a blueprint of how data should be arranged and interconnected within a system to facilitate efficient storage, retrieval, and analysis of data.
• Performance Optimization: Ensuring that data navigates from its source to its destination while optimizing for speed, reliability, and scalability.  
• Data Quality Assurance: Implementing processes to monitor and maintain data quality, including data cleansing, validation, and anomaly detection.  
• Documentation: Documenting data processes, workflows, and system architecture to ensure transparency, repeatability, and knowledge sharing within the organization.

Why Does Data Engineering Matter?

Nonprofits juggle multiple priorities, balancing social impact with strict regulations and resource limitations while generating copious amounts of raw data. This data can be structured or unstructured. Often it cannot be used for analysis right away. Using data engineering techniques helps to make data in your organization readily available for storytelling and analysis. This could allow your organization to:

• Boost Fundraising: Analyze donor behavior to personalize outreach and optimize campaigns, leading to more donations.  
• Streamline Operations: Automate workflows and eliminate data silos for smooth, efficient work.  
• Amplify its Impact: Track program outcomes and create compelling reports, proving effectiveness, and attracting new supporters.  
• Adapt to Change: Predict future needs and trends, allowing proactive adjustments and seizing new opportunities.
• Do More with Less: Efficient data management systems can optimize resource allocation, allowing non-profits to stretch their budgets further and make a bigger difference.
• Make a Lasting Difference: Continuous analysis of data enables nonprofits to evaluate their long-term impact, track progress towards goals, and adapt their strategies for sustainable success.

How Should I Get Started?

It can be daunting to get started with a data engineering practice at your own organization, but you can start small by following best practices. In whatever system that you choose to start with, one of the most important things you can do is to set standards towards building a single source of truth for your data – one place where you know your data can live that appropriate members of your team can access. Here is one pathway that your team could consider to get started with data engineering practices.

Start with SharePoint

The simplest way to get started is by using spreadsheets and a web-based collaborative platform. For example, using Excel with SharePoint. It's affordable and likely a tool that your organization has access to already. At this stage, your organization should be considering best practices and processes for how to use those tools more effectively to align with a more permanent, long-term solution. Some examples of how you might do that include:

• Organized Structure: create folders for each project, including subfolders for data sources, processed data, reports and documentation.
• Standardize Spreadsheet Formats: Develop and standardize organization wide, machine readable templates for elements like column headers and data types -  
  • Each variable forms a column. (i.e. ID, Name, Age, Gender, etc.).
  • Each observation forms a row (individual person).
• Data Security: Emphasize security best practices and establish clear data governance policies to be followed to protect the data.

• Implement User Training: Provide staff with training on effectively utilizing SharePoint for data management, including data entry, updating, and sharing, to promote uniformity across teams and projects.

   

At some point your organization will grow into more modern data engineering needs that these tools weren’t designed for. Some signs that you might need more advanced data engineering solutions include...

• When you run into scaling issues: Spreadsheets can become cumbersome with large datasets, prone to errors and difficult to manage as the team grows.
• When you have many versions of the same document: SharePoint tracks changes at the file level, not individual data elements. This makes it difficult to pinpoint specific changes and understand their impact on the data.
• When you encounter functionality issues: Spreadsheets lack portability for secure data sharing, advanced data analysis and automation capabilities.
• When you need better security: Organization working with other orgs will have to grant access to their internal platforms which poses risk of exposing unnecessary data.

Transition to the Cloud

As your organization begins to encounter these problems and your needs become more complex, you may begin to consider cloud storage options such as Microsoft Azure, Amazon Web Services, or Google Cloud. Creating automated processes using cloud platforms can make data highly accessible stored in secure environments. This can come with increased costs and complexity, but with many benefits as well:

• Compliance Support: Nonprofits deal with Private Health Information, Personal Identifiable Information. Cloud platforms comply with data privacy regulations like GDPR and HIPAA, streamlining compliance efforts for nonprofits.
• Built-in Security Features: Cloud providers offer strong security measures like encryption, identity and access management, and threat detection, improving data security and reducing vulnerability.
• Centralized Data Storage: Store all data in a centralized, secure cloud location for easier access control and management. To create automated processes which in turn increase data quality.

• Auditing and Monitoring: Cloud platforms provide tools for logging user activity, tracking data access, and monitoring for potential security breaches, enhancing data transparency and accountability.

   

Effectively leveraging cloud platforms for data compliance, security, and centralized data storage requires individuals with proficiency in cloud computing platforms. These tasks are typically done by data engineers, who understand cloud security best practices, compliance requirements, and data encryption techniques. Also, they should be able to implement effective data storage strategies, access controls, and data lifecycle management processes to ensure data is stored, accessed, and used in compliance with regulations and organizational policies. This expertise is essential for effective data management and governance in nonprofit organizations.

Final Thoughts

Data engineering is an investment in your mission's long-term impact. Start small, iterate, and adapt; don't aim for perfection. Build your data architecture step-by-step, learning and adapting as you go.  

Data shapes the future of non-profits by making insights more accessible, predictions more accurate, and outreach more personalized and efficient.

It's about empowering your organization to make informed decisions, maximize impact, and ultimately, create lasting change.