As the data revolution continues to change every facet of society, for-profit companies are realizing significant benefits from adopting data-driven decision making strategies for managing their operations. Not-for-profit organizations can also benefit by following suit, but the transformation is not an easy one to make. The authors present seven challenges faced by not-for-profit organizations trying to make the transition to data-driven decision making and offer solutions to each one.
Major stakeholders throughout the not-for-profit sector require data. Donors need data to gauge mission effectiveness, watchdog organizations need data to ensure their funding is being used for the intended purpose, regulators need data to ascertain that organizations are acting in a responsible manner, and lending institutions need data to measure organizations’ financial health. Within not-for-profit organizations, data is also necessary to make strategic decisions, measure donor retention, understand staff turnover, and evaluate which communication tools best tell the organization’s story. Despite all this, not-for-profits are encountering a variety of challenges as they strive to become data-driven decision making organizations. This article describes seven of these common challenges and offers suggestions on how to overcome them.
Challenge #1: Choosing the Right Data
It is easy to become overwhelmed by the sheer volume of data available today. To avoid drowning in data, organizations need to determine what they are trying to achieve and identify which specific questions need to be answered. Organizations should not measure something trivial merely because it is easy to measure. Once the critical questions have been identified, organizations should inventory the data available and determine how it will be gathered. If the data doesn’t exist, find ways to collect it or access external data. Treat data gathering like any other business decision; if the cost to extract data exceeds the benefits, alternative data sources should be sought.
Challenge #2: Identifying the Right Tools
While many organizations feel there is a lack of tools to help analyze and present data, often they just don’t know where to look. A quick Internet search will yield numerous options for tools to use for data analytics, but it will not tell an organization how to select the right tool for its situation. For example, if an organization is searching for a more robust tool, it has probably already been working heavily in Microsoft Excel to analyze its data. Now it needs a way to combine that information with other sets of data to take a holistic approach to data analytics.
Technologies that allow for the collection, integration, analysis, and presentation of business information are collectively referred to as business intelligence (BI). BI systems are driven by data that can be collected from both internal (e.g., accounting systems, donor databases, programmatic statistics collected) and external (e.g., census or national surveys, GuideStar/Form 990s) sources, and they can include historical as well as new data. There are multiple BI tools that can pull together even complex sets of data and present them in a way that makes insights more digestible for decision makers. By utilizing a variety of tools, BI platforms bring together data, systems, and knowledge to create custom solutions. BI can include data visualization software, as well as many forms of advanced analytics, such as data mining or statistical analysis.
Of course, BI is about more than just presenting pretty pictures; it’s about visualizing the insights in a way that is relatable, making it easier to see which actions need to be taken and ultimately how the information can be used in the organization. Organizations can use the data BI generates to see exactly what is happening internally and use that information to make themselves more agile by testing out different scenarios and their likelihood of success. An advantage of BI is that all of this work is done “behind the scenes,” so that the user sees only the finished product.
Common BI systems used by notfor-profit organizations are listed in the Exhibit. It is important to evaluate several options in order to see which one is the right fit for the organization, as they can vary greatly in price, capabilities, ease of use, and visual appeal. Designating an individual (or selected team) within the organization to do the research and develop the process for implementing a tool is recommended. Organizations that do not have the expertise or capacity available internally should consider bringing in a consultant to help guide the process.
Common Business Intelligence Systems
Challenge #3: Building Experience Using Data
People are needed to analyze data and provide insight, but organizations may not have staff that are proficient in analyzing and presenting data. While most nonprofits cannot afford to hire a room full of data scientists, they can identify existing employees who are curious, have a comprehensive understanding of the organization as a whole, and are passionate about data. Presentation skills are also important, as insights gleaned from the data need to be communicated to multiple stakeholders across the entire organization. These qualities can help identify employees that can be trained to work with data.
In addition, staff recruiters should be data focused when hiring for all positions, not just for those within the information technology and finance departments. Job descriptions should reference curiosity, the use of data, and communication skills, and staff must be thoroughly trained on the use of data and analytical and visualization tools.
Challenge #4: Dealing with Decentralized Data
Decentralization of data often occurs when organizations have nonintegrated, siloed systems; that is, when the organization’s data resides in many different systems and there is no single source of “truth” for the information. In these environments, staff frequently expend significant effort extracting and compiling data and far too little time analyzing it. Fully integrating systems throughout an organization can be cost prohibitive; however, organizations should try to automate processes and find a way for critical decision making data to be stored in one system to the extent possible. The ability to fully integrate with existing systems should be an important factor when selecting new software in the future.
Challenge #5: Ensuring Data Integrity
If data is inaccurate and inconsistent, why bother using it? Not-for-profit organizations need to practice sound data governance, which goes beyond data storage, security, and HIPAA compliance. Data should be treated as the organizational asset that it is. Just like having a single person directly responsible for the treasury function, someone should have direct oversight over an organization’s data. Many organizations are adding a chief data officer to carry out this role.
Owners of various types of information should be established throughout the organization and be held responsible for ensuring data accuracy. In most cases, this will not be IT staff, but individuals within other departments (e.g., fundraising, finance, communication) who understand the data best. Organizations should develop a style guide, which is essentially a set of rules for entering information into an organization’s system to ensure data consistency—for example, using the word “Street” instead of the abbreviation “St.” when entering donor addresses. Failure to follow this protocol could result in having duplicate records for donors. Policies and procedures to test the accuracy and quality of data should also be created and implemented.
Challenge #6: Creating a Culture Shift
Using data instead of intuition and opinion may not already be embedded in a not-for-profit’s organizational culture. A data-driven decision making organization is not built in a day. The board and executive team must encourage curiosity and be seen using data when making critical decisions. A data-driven decision making infrastructure is also essential. The organization’s commitment to data-driven decision making must be evidenced by investments in the appropriate technologies and staff. One advisable approach is to start small and focus on a project that can showcase how the analysis of data can lead to sound decision making. The following examples illustrate how small projects can help organizations create a culture of data-driven decision making:
- Organization J, a social service organization, creates a pilot program enabling people to make programmatic site visits. Seventy percent of the individuals participating in the site visits make a donation of $5,000 or more. Organization J decides to invest in and expand this program.
- Organization K, an animal rights organization, includes holiday gift wrapping with its December 2019 direct marketing materials. The December 2019 mailing realizes 10% more in contributions than the December 2018 mailing. Organization K decides to include holiday gift wrapping in its December 2020 mailing.
It is important that senior leadership communicate throughout the organization when data analysis reveals something enlightening. The organization-wide sharing of this information reinforces the power, importance, and usefulness of data and encourages staff to take action.
Challenge #7: Taking Action
Many organizations fall short of acting upon their data. As noted above, data-driven decision making is not about presenting an attractive dashboard; the intent is to gain insights from the data, make decisions based on these insights, and then act upon those decisions. The following examples illustrate this process:
- Organization X, a fundraising entity, wants to increase its younger donor base. Analysis performed by Organization X reveals that younger donors respond more to online solicitations than to traditional direct mail. Based on this insight, Organization X invests more staff time and money creating online solicitations.
- Organization Y, a social service organization, notes that its operating reserves are 75 days, while most of its peers have operating reserves of 90 days. Organization Y realizes the importance of operating reserves to its financial sustainability and puts forth a plan to realize surpluses over the next three years in order to increase its operating reserves to 90 days.
- When Organization Z, a midsize museum, analyzes its staff turnover, it realizes that many staff members leave because they feel unsupported by their managers. Museum Z thus creates a manager training program and requires all supervisors and managers to attend the training.
The Next Leap Forward
The increased demand to show programmatic outcomes and manage resources more efficiently, accompanied by vast quantities of data, faster computing capabilities, and advancing analytics techniques, is changing the way not-for-profit organizations structure themselves, conduct operations, and ultimately fulfill their mission. Not-for-profit organizations will face several challenges in their efforts to become data-driven decision making organizations; however, managing information effectively in this dynamic environment will also lead to new opportunities. Data-driven decision making will enable not-for-profit organizations to make evidence-based decisions that will influence their strategies and ultimately enhance mission effectiveness.