Toward Decision-Driven Analytics for Nonprofits

Data has become one of the most valuable assets of modern businesses, representing a new way that organizations create value for their business customers and stakeholders. Nonprofits are catching up with their for-profit counterparts, and in their quest to be a data driven organization, are jumping on the data-driven decision-making bandwagon. Predominantly, in nonprofits, data is acquired for reporting to the donors and there is limited or no utilization of data beyond reporting. The analytical capability is limited to descriptive analysis, which actually is more than enough for nonprofits to track trends and change over time and report on their progress. 

The data paradigm in nonprofits is more about accountability and risk management, whether financial risk or reputational risk. Working for a nonprofit, the major challenge for me as a Chief Data Officer was to change this perspective from an accountability-driven data paradigm to a decision-driven data paradigm. Program staff, especially at the field level, have been constantly asking the questions: Why are we being asked to collect data after data? What happens to my data? Who uses it? They have been so busy pumping data into the different data systems because  the quest to be a data-driven organization actually led to an increase in staff workload. 

As I pondered this challenge, I asked myself how, as a CDO, could I reduce the data collection effort, get rid of the gray data and provide leadership to ethically harness the power of data and derive value? Secondly, I asked, “What does “value” mean in a nonprofit context? I realized that value in a nonprofit context will come by going beyond using data for reporting and risk management to building organizational capability for decision-driven analytics. 

Working with nonprofits over the last 30 years, I realized that there are only four categories of decision types that any organization makes. In order to better leverage data and harness value, data must provide actionable insights for following the four decision types described in the table below:

Having defined the decision categories prior to any data acquisition enabled us to bring a decision-driven analytical perspective into our data journey. We asked some fundamental questions:

 As an organization, have we really spelled out the WHY of data, i.e., why do we need the data (data for purpose)? The four decision categories defined the “why” for us. Defining the “why” also clarified the data utilization and consumption, i.e., using data to make business decisions. Then it was critical to define the WHAT of data, i.e., to make those business decisions, what data do we need? Further, in order to make those business decisions, do we even have the right data and the right insights (appropriateness)? This helped get rid of the unnecessary (gray) data that we were collecting. This brings us to the HOW of data. How are we going to acquire this data and what are the appropriate technologies and the methodologies we need to collect data? Finally, the WHO of data, i.e., who is responsible for acquisition, who owns the data and who is accountable for ensuring the data quality (completeness, timeliness, validity and reliability) and that the data is trusted?  The schematic view of our decision-driven analytical journey is reflected in the figure below. 

There were a couple of lessons that we learned on this journey. First, the radical shift is required in the organizational data paradigm whether building a data system or refining data processes from acquisition to consumption. We need to approach our systems and processes through the data-centric lens. Technology is only the enabler, not the driver. Secondly, using Simon Sinek’s golden circle helped us understand that data is a powerful asset only when it can be governed well, trusted, and analyzed to generate actionable insights. “Speed of Thought” is critical but it is not enough. It is having the purpose for data, defining right business questions, acquiring the appropriate data, and providing access to right data to the right people at the right time. 

Analytics is not the afterthought, but rather the analytical plan is defined prior to the data collection — the types of analysis and insights needed to drive decisions and having the right people in place who can interpret and make sense of the data. Using MIT’s mind-to-machine concept, most of the analytics can be moved to machines. However,  interpreting the data and making sense of it to make decisions is where I find a big gap in the nonprofits. 

Thirdly, even though the data-driven approach is considered a best practice in the industry, it can often be flawed. Data-driven decision-making anchors on available data. This often leads decision makers to focus on the wrong question. On the other hand, decision-driven data analytics starts from a proper definition of the decision that needs to be made and the data that is needed to make that decision. Instead of finding a purpose for data, find data for a purpose. 

In conclusion, I am reminded of Pablo Picasso who once said, "Computers are useless. They can only provide you with information.” The need for asking the right questions is emphasized by decision-driven data analytics. This approach emphasizes unknowns and the importance of appropriate data collection and analysis. Organizations with data analytics focused on actionable insights, addressing the questions that matter, and challenging the organizational leadership paradigm about how the world works will benefit both the organization and leaders that take this decision-driven analytics approach.

Subodh Kumar, Ph.D., is the Chief Data Officer (CDO) of World Vision International (WVI), the world’s largest nonprofit operating in over 90 countries. 

Dr. Kumar is responsible for developing a shared vision and providing strategic leadership for data-driven business transformation and creating value out of data so that WVI can become a best-in-class data-driven organization. He has over 30 years of experience working in the humanitarian space and has provided leadership to several bilateral and multilateral donor-funded programs among humanitarian organizations, and authored and published several papers in reputed journals. Papers presented in several international conferences have contributed to global dialogues on the Faith-Based Approach to Impact Measurement and Monitoring and Evaluation of International Development Programs. 

Dr. Kumar holds a doctoral degree in Missiology and Statistics. He is based out of Phoenix, Arizona.