Data can say a lot about which processes work and which don’t, but with the volume of information a not-for-profit organization is collecting on a daily basis, that message can easily get lost in the shuffle. Robust data analytics programs help to separate the story from all the numbers.
Analytics programs help to do the heavy lifting when it comes to information processing, distilling volumes of data into real time, and actionable insights. When a data analytics program is properly constructed, it can support risk management efforts, budget decisions, and strategic planning. Data analysis helps identify areas of key risk and fraudulent activity. It can be used to evaluate a program’s ability to meet its targets and highlight any inefficiencies. Data analytics can also play a role in donor management, picking up trends in contributions and charitable giving that may not be immediately obvious to a not-for-profit’s board or management team.
The key to a successful data analytics program lies in planning. The following five tips can help your not-for-profit organization build and sustain a powerful data analytics program.
1. Define Success
It sounds basic, but before you start down the path of building the program, your organization will need to get decision makers on the same page about the program’s objectives. Data analytics programs involve both technical personnel, such as members from an information technology or data team, as well as management and boards of directors. Each party may have different expectations for how the data analytics program fits within the not-for-profit organization's strategic goals as well as varying ideas for what data is important to be analyzed.
Before you get too far into the process, bring the stakeholders together to understand their perspective, objectives, and pain points. Converting this feedback into clear goals that everyone agrees to, such as using data to improve risk identification or program monitoring, will arm the data analytics team with an understanding of its core mission.
2. Identify the Best Data Candidates
Not all data is created equal, which is why your not-for-profit organization is turning to a data analytics program in the first place. Determine what data you have, and then based upon your objectives, determine what data you may need and how you can collect it. The best candidates for starting your program will be ones that align with the program’s strategic objectives and where the needed data is already being collected or can be collected fairly easy. For example, if you want to apply data analytics to your donor management activities, what types of information do you need? You may want to consider demographic information, past history and timing of previous contributions, and mode of contributions. But in order to collect that information, you need to consider how easily and consistently that information is being captured. Demographic information might be nice to have, but if you do not have a process that consistently collects birth dates and income levels from your donors, you won’t have the data you’d like to analyze. You may need to adjust your processes moving forward to capture that information.
As your organization decides which data it will use as part of the program, consider also who manages the data, and whether any additional support may be needed to facilitate the data collection and management process.
3. Make the Data Work for You
One of the biggest mistakes that organizations make with their data analytics programs is taking only an ad hoc approach. Your organization needs to transition from conducting one-off analytics to performing continuous analysis and monitoring of the key data and indicators you’ve identified for your program. This means that executing recurring analysis of certain areas important to your organization should be happening automatically. This analysis can be presented in dashboards that focus on the key data points important to your organization. Analytics tools are helpful, but be sure that the software is being used to its full capacity. The internal audit-focused ACL or TeamMate analytics software typically have flexible interfaces that can execute provided commands and save them in a log over time, which can later be loaded to help you create a specific script. This custom script can then be run automatically, sending real-time reports to management for review and remediation. Tools for presenting your data, such as Tableau, can provide visualizations that translate your data into more meaningful information.
When setting up your automated processes, establish a process around scripting to make sure that analytics are tested, you are looking at the correct data, and that outcomes produce the correct results. Make sure to include a process to implement changes, for instance, where the script requires updating, to prevent future failure.
4. Build a Winning Team
Your data analytics program is only as strong as the individuals on which you rely to execute it. When creating your analytics team, it will be important to designate a few individuals to fulfill critical roles. Identifying a data analytics champion should be your first step. This individual should not only have an expertise in working with data and information technology systems but should also have a passion for this type of analysis. He or she will serve as your primary driver for the program execution.
In addition, someone on the analytics team should focus primarily on relationship management. Consider which individuals from which different departments should be involved throughout various stages of your program. If you are using the analytics to evaluate your programming for example, you’ll likely need input from the team responsible for executing that program. The same goes for analytics on donor management or internal audit functions.
Finally, you will need a board and management liaison. This person helps to bridge the data analytics program with management's overall strategy. This individual should provide your team with oversight and understanding of what is truly important to the organization.
These three areas can be fulfilled by the same person, but it is recommended that they be handled by multiple team members to ensure focus is not lost in any area.
5. Maintain an Optimum Program
Once you have all of the above components in place, your organization will be well on its way to maintaining a strong data analytics program. While data analytics takes some manpower, time, and resources on the front end, in the long term the programs can reduce costs by identifying potential issues or inefficiencies in real time.
For more information on data analytics, please contact one of our Risk & Advisory specialists.
Published on November 28, 2018