The advantages of evidence-based decision making are clear, but there are several reasons why data-driven decision making is a relatively new concept for many notfor-profit organizations. A for-profit business can easily gauge its success in purely economic terms, whereas a not-for-profit organization’s success is measured by mission effectiveness. Many not-for-profits struggle with quantifying this measure. A museum can easily establish the number of visitors it has per year, but may have difficulty demonstrating that it is encouraging a greater understanding and enjoyment of art. A social service organization can easily determine the number of people it serves, but may find it hard to assess whether it is improving the lives of those people.
Resource constraints are another reason not-for-profits have been slow to embrace data-driven decision making. Funding requirements often force not-for-profits to allocate a significant amount of time to reporting, audits, and other compliance-related activities. In the past, funders and watchdog organizations have focused on the amounts expended on administrative overhead versus programmatic outcomes. Becoming a data-driven decision-making organization requires an investment in infrastructure for which there is often little to no funding available; the prevailing attitude is that such investments are inappropriate because they divert assets away from programmatic activities.
Previously, technology was unaffordable; robust systems and analytical tools are needed to provide real-time data. Low-quality data has also prevented not-for-profit organizations from adopting data-driven decision making. Finally, silos often exist in not-for-profit organizations, and alignment among programmatic, fundraising, and financial activities is critical to the creation of a data-driven decision-making culture.
Why Be ‘Data-Driven’?
There are several advantages to data-driven decision making. It is a systematic plan of action instead of a haphazard approach, enabling the identification of opportunities and challenges. Amid major market volatility and a dynamic funding landscape, it allows an organization to adapt with agility. It fosters an objective, rather than subjective, decision-making process. It also depersonalizes decision making and leads to standardization and consistency. Using only intuition when making decisions can carry a big risk.
Data-driven decision making also allows an organization to test different strategies to see what is truly effective. Progress is measured using data rather than emotion and gut feelings. It also provides a more predictive (what will happen) versus historical (what did happen) outlook. It gives decision makers and stakeholders greater confidence, because decisions are based on evidence instead of opinions. Also, the use of data from such nontraditional sources as social media allows organizations to better understand constituent preferences, needs, and concerns.
Challenges to Consider
There are also many challenges with data-driven decision making. Often organizations have an abundance of data but fail to use it when making decisions. In addition, there is increasing pressure to make accurate decisions and achieve results in less time.
Becoming a data-driven decision-making organization requires a different disposition and a major shift in organizational culture. Staff tend to agree with the most senior person in the room instead of relying on data. Organizations need to decide what to do when data suggests taking one direction and the leader of an organization recommends taking another. Staff at all levels must develop stronger analytical skills and incorporate the use of data and analytical tools into their day-to-day work.
Issues regarding “data hygiene” must be acknowledged. Many not-for-profits have failed to focus on data integrity and consistency in the past, resulting in poor data quality. If data cannot be trusted, why analyze it? The volume of data will only continue to increase, and it is therefore imperative that organizations focus on the health of the data they need.
Decentralization of data is also a major challenge for many organizations. Data is often extracted from multiple nonintegrated systems, meaning that a single organization-wide picture of all the data is elusive or even nonexistent; as a result, more staff time is expended on extracting and manipulating data than analyzing it. There are even instances when data is unavailable and gap analysis is needed to determine how to acquire missing information.
Ask the Right Questions
Applying data to decision making and converting data into action is a multistep process. The first step is to identify the questions the organization needs to have answered. If data is to be meaningful, it must answer the right questions. Questions not-for-profits have asked in the past include the following:
- What is the size of the operating budget?
- What are the operating results from three months ago?
- What percentage of expense is spent on administration?
- How much does it cost to raise one dollar?
The data to answer these questions is easily accessible, but little insight is gained from the responses, as they are insufficient indicators of mission effectiveness. Instead, the questions not-for-profits ask should focus on mission fulfillment, quality of services, availability of goods and services to constituents, exceptional customer service, liquidity, financial sustainability, the ability to take advantage of opportunities in the future, sound governance, and reputational risk.
Once the right questions have been identified, the next step is to determine what data is needed to answer those questions. Organizations should start with taking an inventory of available data. If the right data does not exist, they need to find ways to collect it or consider accessing external sources. Data gathering should be treated like any other key business decision. If the cost to extract the data exceeds its benefit, consider alternative data sources.
The data then needs to be analyzed with the intent to extract meaningful and useful insights. This step will most likely require not-for-profit organizations to reevaluate the skill sets available throughout the organization to determine whether staff have the appropriate level of analytical aptitude.
The data must then be presented in a meaningful way. It is important that data be displayed so that everyone can understand it. It’s not about presenting pretty pictures; it’s about presenting information in a way that makes it easier to determine what actions need to be taken.
The final step is to convert the data into action. The organization needs to use the data to make decisions and then act on those decisions.
Start at the Top
Developing a data-driven decision-making culture starts at the top. Leaders must welcome analytics and use data when making decisions. Curiosity and respectful disagreement should be encouraged throughout the organization. Staff should be persuaded to ask questions and challenge data. Organizations should start small, realizing that culture change takes time. “Aha” moments, those times when data reveals something fascinating and enlightening, must be celebrated and shared. Encourage communication between those who know the business and those analyzing the data. Appropriate access to data and the tools to analyze it should be provided to staff at all levels.
When striving to become a data-driven decision-making entity, not-for-profit organizations should avoid getting bogged down in the details. Organizations should measure what is important and avoid the pitfall of measuring something trivial merely because it is easy to measure.
Organizations cannot rely solely on data when making decisions; making a good decision in a timely manner is a matter of balancing data analysis with judgment. Years of business experience cannot be ignored. Tools such as the “Key Performance Indicator Template” (Exhibit 1) can assist an organization in determining what to measure and how to measure it.
Exhibit 1
Key Performance Indicator Template, Metric Number 7
Use Available Tools
Now is an ideal time for not-for-profits to become data-driven decision-making organizations, given the availability of affordable technology, the ease of data storage, and the abundance of internal and external data. Notfor-profits should take advantage of the wide array of user-friendly analytical and visualization tools currently on the market. Visualization tools can be used to present data simply to decision makers and to tell a not-for-profit’s story effectively through images. There are several online resources available to evaluate these products.
The use of dashboards has also become popular. Dashboards are a powerful way to visually present thought-provoking information to an organization’s stakeholders. An effective dashboard is easy to understand and allows constituents to ascertain quickly whether specific objectives are being met. The sample dashboard (Exhibit 2) for Not-For-Profit ABC, a social service organization, highlights the usefulness of a dashboard and underscores many of the advantages and challenges associated with data-driven decision making.
Exhibit 2
Not-For-Profit ABC Dashboard
When creating its dashboard, Not-For-Profit ABC spent a considerable amount of time determining the metrics that were most relevant to its audience. The “Support Satisfaction” metric (Metric 1) is an indicator of mission effectiveness and is designed to measure whether the people supported are doing the things they are passionate about. “Levels of Service” (Metric 2) is the organization’s primary revenue driver. The reasons why the actual levels are less than the budgeted levels should be investigated.
The organization can meet its short-term financial obligations, as evidenced by a “Current Ratio” (Metric 4) of 1:0. The organization’s rainy-day fund target is 60 days, but the organization has only 45 days of operating reserves. The organization may decide to budget and generate operating surpluses over the next few years, thereby enabling it to build its rainy-day fund.
The “Funding by Source” metric (Metric 8) suggests the organization is heavily reliant on governmental funding. Perhaps the organization should work on diversifying its funding portfolio.
The “Employee Promoter” metric (Metric 9), which measures the percentage of employees surveyed who would recommend Not-For-Profit ABC as a place to work, and the “Employee Turnover” metric (Metric 10) are nonfinancial in nature and designed to measure employee satisfaction. The organization should understand why the actual staff turnover percentage is higher than the norm for its peers, and determine ways in which to reduce staff turnover.
Hire the Right People
Data governance is critical to an organization’s ability to become a data-driven decision-making organization. Organizations have begun hiring chief data officers and developing data-strategic plans, recognizing that data is an organizational asset. It is important to establish data owners who are accountable for the integrity and consistency of data. In most cases, the ownership of data will not reside within the information technology department, but rather within the departments that frequently use and understand the data (i.e., program, fundraising, accounting). Written policies and procedures to determine, when, where, how, and by whom data is entered into systems (often referred to as a style guide) should be developed and implemented. Policies and procedures to test the integrity of data should also be created and executed.
An organization may have lots of data, but unless its workforce can analyze and interpret the data, all it has is lots of data. Most not-for-profits cannot afford to hire a team of data scientists. Internal staff who have a comprehensive understanding of the not-for-profit organization and are curious about data should be identified in the following manner:
- Expand the qualification section of job descriptions to include analytical skills and require backgrounds in the use of statistics.
- Encourage human resources to be data focused when screening candidates.
- Look for performance metrics on résumés when determining which job applicants to interview.
Organizations may need to seek help from outside consultants when enough analytical skills are unavailable within the organization. Adequate training is essential.
Many for-profit businesses have the financial wherewithal to invest in the technology and staff recruitment/development necessary to embed data-driven decision making into their daily operations. Even though the for-profit sector measures success by profit versus mission effectiveness, there is a tremendous amount of insight that can be gleaned from the way in which the for-profit sector is using data, because the two sectors share many of the same workforce challenges and customer service issues.
The ability to allocate more time to interpreting data, thereby enabling real-time decision making, is one of the cornerstones of operational excellence. Research has shown that organizations that have successfully integrated data-driven decision making into their organizational culture have reported increased productivity, improved financial performance, reduced risks, and an enhanced ability to take advantage of opportunities.
