White Paper
Business Intelligence A Model for Nonprofits
Trends in Fundraising for Nonprofit Organizations
About This Paper Nonprofits are adopting and embracing a data-informed decision making culture. Analytics, descriptive statistics, predictive modeling, and real-time reporting have become familiar concepts and valuable tools in the fundraising office. The use of data and analytics has expanded both vertically and horizontally over the past several years. Vertical expansion is evidenced by the growing use of sophisticated tools and methods in the creation of analytic outputs such as predictive models and resource optimization analyses. Horizontal expansion is occurring as the successes of analytics in the fundraising department are creating excitement in the marketing, alumni, constituent relations, stewardship, human resource, and programming offices. Nonprofit leaders are recognizing that the competencies and methods used to enhance prospect identification and optimize annual giving campaigns are the same methods and competencies that can determine the optimal balance of front line fundraising staff and support staff, the non-monetary value of newsletter readership, or the cost-benefit of a website redesign. With the growth of analytic capabilities, many nonprofits have the building blocks of true business intelligence within their grasp. For these, the only remaining obstacles to implementing a business intelligence environment is recognizing the potential, and developing a structure in which it can be leveraged and thrive. This paper provides an expansive view of where a business intelligence unit could take these organizations in the future. For many other nonprofits, still developing the awareness of and competencies related to analytics and data-informed decision making, this paper will help identify the areas that must be targeted for improvement before true business intelligence can be realized. WealthEngine Publications Team:
Special Thanks to:
Tony Glowacki, President and Chief
Sarah Janesko, Program Assistant, Children’s
Sally Boucher, Director of Research,
Jess Kean, Program Associate, Children’s Cause for Cancer Advocacy
Shane Bair, Creative Director
Megan Martin, National Manager, Business Intelligence Operations, JDRF
Executive Officer
WealthEngine Institute
Wendy Tanner, Marketing
Communications Specialist
Cause for Cancer Advocacy
Kelly Quin, Senior Director of Constituent Strategy, Rice University
©2014 WealthEngine TM, Inc. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. This document is informational in nature and we do not guarantee any of the information either expressed or implied. Readers are encouraged to consult with their appropriate legal, accounting and professional counsel before implementing any suggested actions. WealthEngine has no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof and shall not be held liable for any claims or losses that may rise from the implementation of the best practices in this report. This document includes ideas for enhancing WealthEngine’s products. These ideas are subject to change at any time.
Contents What Is Business Intelligence?
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The Nonprofit Business Intelligence Model
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About Our Survey
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Five Stages of Maturity of Business Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Key Characteristics of Data-Informed Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Technical Support. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Getting Started with Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Characteristics of Data-Informed Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Challenges to Achieving Business Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
The Intelligent Nonprofit
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Analytics in the Small Shop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Organization-wide Business Intelligence Implementation in a National Nonprofit. . . . . . . . . . . . . . . . . . . 27 Business Intelligence and the Prospect Research Community. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
The Future of Business Intelligence
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Building a Case for Business Intelligence Conclusion
Business Intelligence
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What Is Business Intelligence?
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While there are many definitions of business intelligence (BI), for our purposes in this white paper, we are defining it as “the right decision support to the right people at the right time.” What does this mean? It means using the data you have available or can acquire, enriching it appropriately as needed, analyzing it to create information and knowledge to answer business questions, and presenting that newly discovered, evidence-based knowledge in an understandable and articulate way to the right decision maker at the right time in order to produce the best possible decision and outcome.
Analytics Data Reporting
THE RIGHT DECISION TO SUPPORT THE RIGHT PEOPLE AT THE RIGHT TIME Business Intelligence
Figure 1: The building blocks of business intelligence
The building blocks of business intelligence are data, reporting, and analytics. Data is the essential and fundamental element of every business and nonprofit organization. Data is a collection of facts and figures that are meaningless on their own, but when put in context, are the lifeblood of any for profit or nonprofit entity. Data relates to: TT donors, prospects and customers of an TT efficiencies of processes organization TT value related to costs and investments TT transactions, sales and interactions TT return on equity / return on investment Without data, there is no business.
Business Intelligence
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Reporting is what takes raw data and turns it into meaningful information. Reporting can take many forms, and most nonprofit organizations have reporting capabilities based on one or more of their electronic data sources, such as their donor management system or their accounting system. More sophisticated organizations have a data warehouse that can house and merge multiple data sources and from which report writers or analysts can extract data and information. Reports typically answer questions such as “What happened?” “How does this outcome compare with that outcome or projections?” or “What is the trend?” Many organizations have come to value real time reporting in the form of dashboards that report on Key Performance Indicators (KPIs) either in real-time with instantaneous updates, or with these key metrics being updated on a daily or nightly basis. Analytics is the third essential component of business intelligence. Analytics takes direction from leadership, decision makers and managers, by understanding the business questions they need to have answered or what pain points they are feeling in their processes and procedures. With this business understanding, analysts can then, using a more technically-based skill set, work directly with programmers, data specialists and IT professionals to identify the needed data and extract, merge and enrich it if necessary, to perform the necessary analysis. Analysis transforms the data and information from reporting into the knowledge needed to make optimal decisions, and answers questions such as “Why did this happen?” “What will happen if …?” and “What is the best possible outcome?” When data and reporting are supported by analytics, an organization can answer these types of questions and is then practicing true business intelligence. We will discuss in more detail the methods and competencies needed in each of these three components of BI, and where they may reside within a nonprofit organization. Figure 2 shows how these elements fit together and serve the overall information flow and strategy in a nonprofit organization.
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BUSINESS ENVIRONMENT DEVELOP INFORMATION STRATEGY
RESEARCHERS, ANALYSTS & REPORT DEVELOPERS DATABASE SPECIALISTS
INFORMATION SUPPLY
OPERATIONAL DECISION MAKERS
INFORMATION REQUIREMENTS
MANAGEMENT TEAM
DEFINE INFORMATION AND KNOWLEDGE NEEDS CREATE AND PRESENT INFORMATION AND KNOWLEDGE FROM DATA TO MEET BUSINESS DEFINED NEEDS MERGE, ENRICH AND MAKE DATA ACCESSIBLE TO BUSINESS USERS SOURCE DATA AND CREATE INFRASTRUCTURE
INFORMATION TECH PROFESSIONALS
TECHNICAL ENVIRONMENT Figure 2: A model of BI for nonprofit organizations
Business Intelligence
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The Nonprofit Business Intelligence Model
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The Nonprofit Business Intelligence Model In this model (see Figure 2), there are five bands of competencies and activity related to the overall information strategy of the nonprofit. TT Nonprofit management, including board, trustees, CEOs and Executive Directors, set the information strategy for the organization. TT Specific information, knowledge requirements and needs are determined by operational decision makers such as department heads and managers in concert with the organizational information strategy. TT These requirements are related to researchers, analysts and/or report developers, depending on the type of information and analysis needed. Analysts and others within this band must be articulate in the business environment as well as the technical environment, as they will next work with database specialists and perhaps IT professionals to articulate the data and reports needed to answer the business questions. TT Database specialists query the database or databases for the required data, merge and enrich it as required and make this data available for the business purpose as specified by the bands above. TT A functional data environment is supported by technical professionals who ensure the database or data warehouse infrastructure is functioning and that if there is additional data that must be collected or sourced to meet business needs that these needs are met. In this model, each band represents a competency that must be present in the organization for a fully realized business intelligence model to function. That is not to say that each band or competency must be filled by unique individuals. Particularly in smaller organizations, one individual may serve as the technical specialist, data specialist and analyst, or as a department head, analyst and data specialist. The key is to understand the importance of each role, and that there is a needed bridge between the business and technical environments which often resides with the analyst, prospect researcher, or prospect manager.
Business Intelligence
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About Our Survey
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About Our Survey In an effort to understand and illuminate the evolution of nonprofits from the mostly qualitative, sensory practices of the 1990’s , when there was more “art” in the art and science of fundraising, to the more quantitative, science-based practice we are seeing in the last decade, WealthEngine Institute conducted a survey in April-May of 2013. We received a total of 1,126 responses which provided details on the technical, analytical, reporting and data environments of the respondents.
4%
4%
3% Education
5%
Social/Human Services
32%
Healthcare 9%
Arts Other Environment
11%
Religious Organization Community Organization Advocacy
18%
14%
Figure 3: Types of organizations represented by survey respondents
Thirty-one percent of respondents represented higher education, 18% represented social and human services, and 14% represented healthcare. Arts organizations, community organizations and environmental causes also figured prominently, as shown in Figure 3. 4% 17% 19%
$25MM and over $5MM to under $25MM $600K to under $5MM 20%
Under $600k Don't know/Not sure
40%
Figure 4: Responding organizations raised from under $600K to over $25M
In terms of size, organizations represented ranged from those receiving annual contributions of $600K or below, and those receiving $25M and above. As indicated above, forty percent of the represented organizations raised between $600K and $5M.
Business Intelligence
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Fundraising budgets ranged from $10K and below to over $25M, with frequencies shown in Figure 5. 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% Less than $10,000
$10,000 $49,999
$50,000 $99,999
$100,000 $249,999
$250,000 $499,999
$500,000 $749,999
$750,000 $999,999
$1MM $4.99MM
$5MM 24.99MM
$25MM+
Figure 5: Fundraising budgets ranged from under $10k to over $25M
5% 9%
13%
Data and Analytics Corporate/Foundation Giving
12%
12%
Planned Giving Annual Giving/Membership Stewardship/Donor Relations
9%
12%
Special Events Major Gifts Marketing and Public (Alumni) Relations
14%
14%
Technology and Information Services
Figure 6: Survey respondents by job responsibility
Figure 6 shows the types of respondents to the survey by their job responsibilities, and indicates a broad representation of both duties and seniority levels. In this paper, we will explore the characteristics of data informed organizations and present a model for nonprofits to follow as they move up the maturity continuum towards achieving full business intelligence. We will also share examples from organizations that are pushing boundaries at one level or another and provide guidance for those who are looking for next steps to increase their maturity level.
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Five Stages of Maturity of Business Intelligence In the survey we asked numerous questions about data based analysis, decision making, data sharing mechanisms, reporting and technical environments. Among the 1,126 responses, we were able to identify five distinct stages of maturity in terms of the use of data, reporting and analytics.
3%
6%
21%
Oblivious Aware
39%
Emerging Investing Optimizing
31%
TT Oblivious: 6% of the respondents described themselves as “very data-uninformed” and said they did not use data or analysis to make decisions. They tend to be struggling with the demands of day-to-day existence, and are therefore oblivious to the data-driven metamorphosis taking place in the industry. TT Aware: 21% of those responding indicated they are “somewhat uninformed.” They use very little data and have little expertise in analyzing data to help inform decisions. These organizations are aware of the innovations taking place in the industry, but have not yet begun to leverage their own data or capitalize on it. TT Emerging: 31% of the survey takers are using data and analysis for basic processes, like developing budgets and creating goals. They are using information as available to support decisions, but have not yet fully embraced the data revolution and are not systematically practicing data based decision making. They are emerging onto the data frontier. TT Investing: 39% of the respondent pool described themselves as data-informed, and use data and analysis regularly to evaluate successes and failures, make decisions about resource deployment, and have embraced the advantages of an analytic decision making culture. These organizations are investing in the tools and competencies needed to navigate the world of Big Data and analytics. TT Optimizing: 3% of our survey respondents were “very data-informed,” on the cutting edge of the data revolution, pushing the boundaries of what they measure and analyze. These organizations are investing in data, reporting and analytics, and have a structure in place to leverage these competencies and use them throughout their division and/or organization.
Business Intelligence
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Key Characteristics of Data-Informed Organizations Most of the organizations we identified in our survey as being Investing and Optimizing organizations shared characteristics relating to their data, technical, reporting and analytic environments. They also faced similar challenges that differed markedly from their Oblivious and Aware counterparts. Data When asked about enriching data through wealth screening or data appends, a full 78% of Investing and Optimizing organizations updated their constituent data either annually, periodically in smaller batches, or every three years. In contrast, over 50% of the Oblivious group never screens data, and nearly 50% of the Aware group either never screens or screens only once every five years. These results are presented in Figure 7. Investing and Optimizing Organizations Screen Data More Frequently 100% 90% 80% 70%
Anually
60%
Peridocially in small batches
50%
Every 3 years
40%
Every Five Years Never
30% 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Optimizing
Figure 7: Screening frequency by BI maturity level
Enriching data with wealth, demographic, biographic and lifestyle/behavioral appends allows organizations to glean more insight on their constituents, and have a richer set of data for more robust analytics. Organizations were asked what sources they use for data collection, with free internet based sources, wealth or asset screening, and subscription based sources being the most commonly used. Other sources used are indicated in Figure 8.
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Optimizing organizations were apt to use a wider variety of sources than Oblivious or Aware organizations, and more apt to use focus groups, surveys and predictive modeling to generate data. Using data from a variety of sources provides a spectrum of information that contributes to a more accurate overall picture, as well as preventing any reporting bias which occurs when the same data and data sources are overused to the exclusion of others. Free Internet, Wealth Screening and Subscriptions Are Most Frequently Used For Data Enhancement 600 500 400 300 200 100
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Figure 8: Resources used for research and data enhancement
Technical Support In terms of technical environments, a strong support system of IT and IS appears to be a critical component of successful Business Intelligence operations. Of the organizations in the Optimizing group, over 50% were “very satisfied” with their technical support, while in the Oblivious group, 50% were “very dissatisfied.”
Business Intelligence
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Optimizing Organizations Express Most Satisfaction with Technical Support 100% 90% 80% 70% 60%
Very Satisfied Somewhat Satisfied
50%
Neither Dissatisfied nor Satisfied
40%
Somewhat Dissatisfied Very Dissatisfied
30% 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Optimizing
Figure 9: Satisfaction with technical support by BI maturity level
As illustrated in Figure 10, the satisfaction level of respondents was somewhat correlated to the type of support available, with Optimizing and Investing organizations more likely to have dedicated technical support or organization-wide support while Oblivious and Aware organizations were more likely to have no technical support. Optimizing/Investing Organizations More Likely to Have Dedicated Tech Support 100% 90% 80%
We outsource our technical support, obtaining support when and where needed
70%
We have technical support specifically dedicated to development /fundraising and research
60% 50%
We have technical support covering the entire organization, but no technical support dedicated to development/fundraising research
40% 30%
We have no technical support 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Figure 10: Types of technical support by BI maturity level
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Optimizing
More dramatically, in Figure11 it is quite evident that organizations that are higher on the maturity scale are planning strategically for technology needs and investments. It stands to reason that organizations who are optimizing their investment in data would want to ensure they have the plans in place to sustain that investment into the future. Organizations on the lower end of the scale have a noticeable lack of strategy planning around technology, and should consider bringing this need forward in their planning cycles. There is certainly no evidence to suggest that reliance on technology will abate in the future. On the contrary, essentially all predictions indicate an increased dependence on technology. Optimizing and Investing Organizations are Much More Likely to Have a Technology Strategic Plan 100% 90% 80% 70%
We have a well-crafted, up-to-date technology strategic plan
60% 50%
We have some technology concerns included in our organizational strategic plan
40%
We have no technology strategic plan
30% 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Optimizing
Figure 11: Strategic planning for technology by BI maturity level
Reporting In addition to data and technical support, organizations that are higher on the maturity scale of Business Intelligence have more sophisticated systems of data reporting in place than those who are lower on the scale. When asked to describe their reporting environment, answers ranged from “We don’t have time for reports” to “We have dashboards with real-time reporting.”
Business Intelligence
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Getting Started with Reporting Reporting does not need to be the burden of time and drain on resources many less sophisticated organizations see it as. Here are a few tips for keeping reporting in perspective and making it a part of your weekly and/or monthly workflows: TT Determine your key performance metrics. These don’t have to be many, but should be determined in conjunction with each department manager. They should tie specifically to the organizations strategic imperatives. TT Design reports. Once all key players have agreed on key performance indicators, design reports to show the metric (performance indicator) against a benchmark – your goal, your past performance, a peer or national standard benchmark. TT One-time setup. Once the report is designed, work with your database administrator, programmer, database vendor representative or if necessary, a paid consultant to do the one-time setup work. TT Run and distribute reports on schedule. Now that the report is programmed, run it regularly and distribute to a predetermined distribution list. TT Stay on course. Do not allow “ad-hoc” report requests to derail your efforts to keep reporting simple and streamlined. Put all but the most urgent ad-hoc report requests in a folder and consider how some of the more frequent requests can be accommodated in periodic revisions of your standard reports.
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As Figure 12 indicates, reporting capabilities are an essential ingredient of the business intelligence environment, and those organizations that have no time for reports, or have trouble getting needed information in a timely way, are much less likely to be investing or optimizing their use of data, information and knowledge. Mature BI Organizations are More Likely to Have Dashboard Reporting 100% 90% 80% 70% No time for reports 60%
Dashboards with real-time reports Pull own reports from DMS
50%
Static Rpts issued periodically Request as needed from IT
40% 30% 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Optimizing
Figure 12: Type of reporting by BI maturity level
Respondents were able to select multiple answers for this question, so it is interesting to note also that Investing and Optimizing organizations are more likely to use multiple methods of reporting, with the most frequent combination being real time reporting via dashboards and pulling their own reports. Obviously, for organizations wishing to elevate their position on the BI scale, an investment in reporting systems that allow non-technical users to pull reports on their own, and get access to real-time KPIs through dashboard reporting, would be a step in the right direction. Analytics Investing and Optimizing organizations were far more likely to express satisfaction with analytic output than other categories, and Oblivious and Aware organizations were significantly more likely to be very dissatisfied or somewhat dissatisfied with analytic output. These results are illustrated in Figure 13.
Business Intelligence
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Mature BI Organizations are Much More Likely to Be Satisfied or Very Satisfied with Analytic Output 100% 90% 80% 70% Very Satisfied
60%
Somewhat Satisfied Neither Dissatisfied nor Satisfied
50%
Somewhat Dissatisfied 40%
Very Dissatisfied
30% 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Optimizing
Figure 13: Satisfaction with analytic output by BI maturity level
Analytics projects range broadly among all levels of BI maturity. Figure 14 shows the types of projects that are benefitting from predictive modeling and other analytic output. Predictive Model Applications for Fundraising Social media planning Corporate and foundation giving Event list development Event strategy and planning Planned giving program Direct mail or email Program strategy and planning Annual fund Major giving program 0
50
Figure 14: Applications for predictive models
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150
200
250
Characteristics of DataInformed Organizations Our survey indicates that data-informed organizations: TT Enrich their data with screening on a regular basis TT Use a variety of other sources for data enrichment when needed, such as surveys, focus groups, and demographic appends TT Are much more likely to have a Data and Analytics department or designated individual conducting analytics than other respondents TT Are actively planning for technology needs into the future either as part of their strategic plan or with a separate technology strategic plan
TT Have a satisfactory or very satisfactory opinion of their technical support, with a slight preference for dedicated technical support or organizationwide technical support over outsourced support or no support TT Have access to information in the form of reports, preferably by pulling reports directly on their own and/or by accessing real-time data through dashboards TT Have leadership that supports and encourages measurement of ROI and accountability for results
The most frequent conductors of analytics within nonprofit organizations are the annual giving department, the prospect research department, executive leadership, prospect management and major giving departments. As illustrated in Figure 15, however, there are numerous departments conducting analytics and all of these can potentially benefit from a true business intelligence environment, where the resources and skill sets for more advanced and accurate analysis can be centralized to the benefit of all.
Business Intelligence
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Analytics Are Carried Out By a Number of Different Departments Technology and Information Services Planned Giving Program Sta Data and Analytics Stewardship/Donor Relation Corporate and/or Foundation Giving Special Events Marketing and Public (Alumni) Relations Administrative Sta Major Gifts Prospect Management Executive Leadership Prospect Research Annual Giving/Membership 0
50
100
150
200
250
300
350
400
450
Figure 15: Departments conducting analytics
Optimizing and Investing organizations are 86% more likely to have a data and analytics department conducting analytics as other respondents. Organizations High on the BI Maturity Scale Are Much More Likely to Have Leadership that Believes Measuring ROI is Very Important 100% 90% 80% 70% 60%
Very important Somewhat important
50%
Somewhat unimportant Not at all important
40% 30% 20% 10% 0%
Oblivious
Aware
Emerging
Investing
Optimizing
Figure 16: The importance of measuring ROI
As illustrated in Figure 16, the importance of measuring Return on Investment is an attribute that correlates with level of Business Intelligence maturity. Over 83% of Optimizing organizations consider it “Very Important,” a figure that shrinks to just 50% among Oblivious organizations.
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According to our survey, the groups most interested in measuring ROI in organizations are organization executives, board members or trustees, development leadership and staff, and donors. Clearly those organizations with leadership encouraging or demanding accountability from within are leading the way in BI maturity.
Challenges to Achieving Business Intelligence What are the biggest challenges most organizations face when attempting to use data, information and knowledge to optimize decision making capabilities? We explored this question in our survey, and found that overall, the two biggest challenges faced were (1) lack of data, and (2) data cleanliness, consistency and/or accuracy. Challenges with software and hardware were less significant, and were shared equally by high performing and lower performing organizations. Oblivious, Aware and Emerging organizations were slightly more likely to suffer from having a poor data culture as evidenced by less supportive leadership, while Investing and Optimizing organizations were more likely to have slightly higher rates of challenges with data collection and data cleanliness. Figure 17 summarizes findings from questions related to challenges faced. All Organizations Face Data Challenges Including Data Collection, Lack of Data, Data Cleanliness, Consistency and Accuracy 100% 90% 80% Software
70%
Hardware
60%
Poor Culture - Leadership Uninterested Lack of Analytical Skills
50%
Lack of Technical Skills-All
40%
Data Cleanliness, Consistency and/or Accuracy - all
30%
Lack of Data - all
20%
Data Collection - all
10% 0% Oblivious
Aware
Emerging
Investing
Optimizing
All
Figure 17: Challenges to implementing BI
Business Intelligence
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The Intelligent Nonprofit
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The Intelligent Nonprofit We’ve shared in Figure 2 a model to show the ideal flow of data, information and knowledge through an organization, with information needs being specified at the management or business levels of the organization and information being supplied from the technical level of the organization. Analysis, interpretation and insight are added in a mid-level tier, where analysts, prospect researchers, and/or report developers interface with both environments. In addition, the competencies that must exist in the organization for true business intelligence to function, including data, technical, reporting, analytical, and business competencies have been explored. It has also been noted that communications capabilities, to bridge the divide between the business and technical environments of the nonprofit, are essential. We have also discovered that the competencies and skill sets identified as essential are ones that must be present within the organization, but need not be represented by individual employees. Small nonprofits and large nonprofits, those with large budgets and small, hundreds of staff members or a few, all have business intelligence within their reach. We’ve briefly identified the many programs, functions and departments within nonprofits that are conducting analysis and analytics, and will now explore in more depth how business intelligence can improve the overall efficiency and effectiveness of these organizations.
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Success Story: Analytics in the Small Shop As we have stressed, BI can flourish in small or large shops, among few staff members or many. The following case study shows how a combination of analytics know-how and leadership can create BI even in the smallest of nonprofits. The Children’s Cause for Cancer Advocacy (CCCA) is a small, Washington, DC-based organization with three full-time and one part-time staff member. The charity focuses on analyzing policy and advocacy work around children’s cancer issues. Sarah Janesko, the program assistant focuses on fund development; planning events, coordinating donor stewardship and managing the constituent database. Jess Kean, is the program associate and works part-time managing CCCA’s communications and marketing through social media, monthly e-newsletter as well as online and direct appeals. This is an example of a small organization where many business intelligence (BI) competencies reside in one or two people. Janesko acts as data base manager and is therefore the organization’s expert in data and data sourcing. In addition, she handles reporting functions, providing information to others in the organization as requested. Kean, having an intimate knowledge of the communication’s content, is also responsible for deeper analysis when strategy adjustments warrant it. Characteristic of a small staff, Kean and Janesko often work together on projects that draw from both their knowledge and skill sets.
CASE STUDY
Newsletter Redesign Idea Leads to Analytic Approach Kean and Janesko collaborated on a project related to a communications plan overhaul. The organization was sending one newsletter to all of their constituents, which included donors, prospects, advocates, health professionals and advocacy allies. The organization newsletter was academic because they were primarily targeting health professionals and advocacy allies. Because donors and advocates were also receiving their newsletter, they were considering making a change to the content and format, to better reach these key constituencies. At the same time, they were also planning to continue communicating with the health professionals and advocacy allies through a stripped-down, quarterly policy brief.
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Before making any changes, they realized they needed more information about who was reading the newsletter, so Kean spearheaded a newsletter readership analysis. “We first looked at ‘open’ and ‘click through’ rates,” Kean explains. “We determined that at 15% and 2.1% respectively, these were well within normal averages.” Kean next focused on the click through readers themselves – those who were actively involved to the point of interacting with the content. When they identified these readers, and determined which content they were focusing on, it was a surprise to the staff. While their newsletter had been targeting health professionals and advocacy allies with academic and institutional content, Kean found most of the readers were families of cancer patients and nonprofit groups who were focusing on the case studies included with the content.
“Any projects we present to our Board, from a communications plan to a budget request, we always back it up with data so that we can make an informed decision on how to move forward.” - Sarah Janesko Program Assistant, Children’s Cause for Cancer Advocacy This finding, or “aha!” moment, led the team to confirm their hunch that a newsletter redesign was warranted. Knowing who their readers were, they decided to focus on providing interaction opportunities through social media and a newly launched blog, to present more personal and intimate reflections of the work the CCCA is doing within the newsletter. This would not only have a greater impact and more appeal for the current readers of the newsletter. It would also have a greater probability of drawing additional supporters – donors, prospects and advocates -- into the fold.
CASE STUDY
Monitoring Results of Redesign Will Guide Future Strategy With the newsletter reformatted, Kean and Janesko will continue to monitor open and click through rates for additional insight. Their outcome—a more thoughtful and focused communications strategy, targeting different groups with different content, is certainly not a new idea. But analyzing current readership and using data to make strategic decisions is something not every organization takes the time to do. Kean notes, “With baseline data in hand, CCCA can easily measure the impact of the changes they make and implement course corrections as needed. With no baseline analysis, any benefit, or detriment, from changing the communications plan would have been purely anecdotal.”
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CCCA is an example of an organization investing in business intelligence and datainformed decision making. According to Janesko, “Any projects we present to our Board, from a communications plan to a budget request, we always back it up with data so that we can make an informed decision on how to move forward.” This makes data-informed decision making a part of the organizational culture, and demonstrates it can be done even with a small staff and minimal budget. In terms of BI, information needs were specified by program directors and board members. Janesko, serving as data and reporting guru, pulled the needed data and Kean performed the analysis to enhance knowledge and understanding as it related to the business goal specified by management.
Analyzing current readership and using data to make strategic decisions is something not every organization takes the time to do. With baseline data in hand, CCCA can easily measure the impact of the changes they make and make informed course corrections as needed. With no baseline analysis, any benefit, or detriment, from changing the communications plan would be purely anecdotal. Too often, organizations blame a lack of technology or a small staff for their failure to use business intelligence for decision making. As illustrated in the case of CCCA, any organization, regardless of size and resources, can use the data they have to make data-informed decisions.
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Success Story: Organization-wide Business Intelligence Implementation in a National Nonprofit Business Intelligence can be especially important in large organizations, where there may be many sources of data with massive volumes of data generated on a daily basis. As well, their operations may be decentralized and staff may be geographically as well as psychologically diverse. One organization that has built business intelligence into their national organization structure is JDRF, formerly known as the Juvenile Diabetes Research Foundation. Megan Martin, National Manager, Business Intelligence Operations at JDRF, led a team that collaborated with business and fundraising personnel on one side, and technical, programming and data experts on the other, to produce a system of real time dashboards built on agreed-upon key performance indicators (KPIs), with imbedded analytics to provide information and insight to end users in real time. The project was costly in terms of time and investment in technology and programming, but, Martin explains, “This project has helped push us forward. We hadn’t been investing in technology and that was hurting us. So we invested a lot into this project, with the outcome that it will save us a ton of money moving forward. And there is so much more we can do with the data and stronger internal communication channels.” The following JDRF Case Study shares more on this unique project.
CASE STUDY
A Decentralized National Nonprofit Faces Many Information Challenges JDRF is the leading global organization funding type 1 diabetes (T1D) research. JDRF’s goal is to progressively remove the impact of T1D from people’s lives until we achieve a world without T1D. JDRF collaborates with a wide spectrum of partners and is the only organization with the scientific resources, regulatory influence, and a working plan to better treat, prevent, and eventually cure T1D. As the largest charitable supporter of T1D research, JDRF is currently sponsoring $530 million in scientific research in 17 countries.
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JDRF is an international organization raising funds through a national office and decentralized volunteer-led activity in more than 100 locations worldwide. They evolved over time with various regions and the national office using different and multiple database and tracking systems including Oracle, SalesForce CRM, Convio, Raiser’s Edge, Excel and many others in between. “If you asked three people a simple question, like ‘How many chapters and affiliates do we have?’” says Megan Martin, National Manager, Business Intelligence Operations, “you would get three different answers.” JDRF had no centralized data source, finance records were always in disagreement with development reports, and while chapters could identify totals for fundraising events, they were unable to determine whether they were good, bad or indifferent as they could not evaluate the results against a larger context.
“We had to think horizontally as well as vertically. We had too much data, it was overwhelming. Our charge was to make Big Data understandable and digestible.” - Megan Martin National Manager, Business Intelligence Operations, JDRF
CASE STUDY
JDRF Adopts Business Intelligence In the spring of 2011, James Szmak, Chief Operating Officer at JDRF, approached Martin and colleague Emily Eakin about developing an organization-wide business intelligence (BI) unit dedicated to creating a more efficient and collaborative system of data reporting and dissemination to drive organizational decision making. Martin and Eakin came out of the Prospect Research and Prospect Management side of the house. The team Szmak, Martin and Eakin created merged technology expertise with business and fundraising acumen. This was intended to provide a solution that was not just a technological improvement, but one that would drive collaboration, business understanding and knowledge, and ultimately result in more effective operations. The Solution: They began by gathering the resources they would need. JDRF engaged vendor Altosoft to create custom dashboards that combine data and insight into brief real-time reports and visuals that end users can access and understand. With the team in place, the priorities were simple, if not easy:
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1. Identify the key operational areas needing unique dashboards: a. Finance b. Fundraising including walk, ride, gala c. Human resources d. Research 2. With end users, collaboratively define the most relevant 3 or 4 things that drive business – the Key Performance Indicators (KPIs) – for each dashboard 3. Determine a single ‘source of truth’ for the necessary data 4. Design and implement a reporting system that can pull data in real time from disparate systems, create insight from the data in a format that is user friendly and actionable Martin and team began developing the finance dashboard first, because the data supporting it, primarily from Oracle, was in relatively good order. Key challenges involved paring down the many metrics tracked (35+ metrics) to an ideal number of 3-4. Martin admits that they did not meet that goal, ending up with 5-8 for each area. “We had to think horizontally as well as vertically,” says Martin. “We had too much data, it was overwhelming. Our charge was to make Big Data understandable and digestible.” Collaboration with end users was essential, and for several reasons. For one, it was a process just to create a common definition of terms. What do we mean by “net” and “gross?” What constitutes a “walker” in the walk-a-thon program? Even these seemingly simple and straightforward concepts could be rife with complexity and confusion. Second, adoption was dependent on buy-in and understanding. “If people aren’t comfortable with it, or don’t believe in it, they aren’t going to use it,” says Martin. So it was essential to create a product that was both insightful and user friendly.
CASE STUDY
Insight is created by looking at metrics and numbers within context. “One number doesn’t mean anything,” shares Martin. “Anyone can report, ’We’re at 8 million,’ so is that good, or bad? We don’t know without putting another number next to it. You need to look at the deltas; this time vs. that time, this event vs. that event, this year vs. last year. That’s what tells you if it’s a good story or a bad story. With our dashboards, we provide that context.“ And through the dashboards, the context and insights are delivered in an easy to read format. Instead of reading through columns of numbers, and culling summaries from reports, users get the context they need from a glance at the screen, with a couple of mouse clicks.
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Business Intelligence Implementation Streamlines Resource Use Martin and her team rolled out the first iteration of the finance dashboard in August 2012, first to regional directors and program heads, then expanding to national development staff. With over 60 users, they received good feedback and met with immediate success. Next they developed the ‘Walk’ dashboard, capturing, digesting and elucidating the many sources and types of data relevant to the entire grassroots walk program including registrations, costs, revenue, social marketing, emails and fundraising. They created several versions, using data visualization, especially the use of icons to simplify understanding and limiting the use of grids and tables. They also added landing and home pages to highlight particular information and insights. “By choosing what to highlight on home pages,” shares Martin, “we can set business direction and drive results.”
“By choosing what to highlight on home pages, we can set business direction and drive results.” - Megan Martin National Manager, Business Intelligence Operations, JDRF
CASE STUDY
In late 2012, they rolled the Finance and Walk dashboards out to the chapters across the country. They provided two logins for each chapter’s leadership, and were met with enthusiasm. They have now granted access to anyone in the chapters who wants it. Martin shares some of the reasons for the successful adoption: “There is no data entry required on their part. It’s purely a tool to use. We focus on celebrating the successes, as well as the shortages, and we are consciously distancing ourselves from any perception of a “Big Brother” trying to watch their every move or dictate decisions. One thing that really helps to build buy-in is that when someone has a design suggestion, assuming it’s a good one, we are able to implement that immediately. That creates enthusiasm.” There are obvious benefits to having implemented a Business Intelligence platform at JDRF. Asked to name the top three, Martin can’t resist including four:
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TT Effectiveness: “We are no longer spending hours developing reports and analysis in fragile Excel” TT Efficiency: “Having a centralized reporting system” TT Collaboration: “We’re all speaking the same language now” TT KPI’s: “We’ve identified the key metrics that drive our business” “The improvements to our fundraising process and results are already becoming evident” shares Martin. Specifically, she points to three things that have so far streamlined resource use and improved ROI: TT Having real time data allows national and chapters to make course corrections much more quickly. They can see if a chapter is falling behind in its recruitment of family teams, for example. “Once we see that, we can call attention to it and the chapter has time to recover.” TT It has also helped improve the integrity of the data. Because the data is now both visible and transparent, chapters and national are more aware of the need to get clean and accurate data into the system. “The data is front and center on the dashboards, so everyone is interested in getting their data in and having it accurately reflect their progress.” TT A third benefit already being realized is that having the real time reporting has freed up a lot of time for the national staff, who used to expend a lot of time and resources compiling reports from disparate data sources and manually calculating metrics.
CASE STUDY
“There Is So Much More We Can Do” Martin is already on to the next phases of development and roll out, with dashboards underway for human resources, the ride program, gala, and research. In addition, she is traveling the country, visiting chapters, providing education as well as gaining feedback. “The dashboard is now in the hands of the users,” she explains. “And they are moving forward with it.” Many of them are interested in learning more about analytics, others are developing small case studies to share. The vision to create an overarching business intelligence framework for JDRF has been born, and now takes on a life of its own. “This project has helped push us. We hadn’t been investing in technology and that was hurting us. We did invest a lot in this project, but in the end it’s clear it will save us a ton of money. And there is so much more we can do.”
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Success Story: Business Intelligence and the Prospect Research Community Research and Analysis Professionals are often the catalysts for organization-wide business intelligence. In order for the competencies housed in this sometimes insular unit to flourish in a true BI sense, it takes a recognition by leadership that the skills and acumen exist, that they are useful and important, and that they can be leveraged to benefit a wider spectrum of programs than simply major gifts, which is normally the program that spawns the need for research. In the case of Rice University, all of these assumptions were met, and BI is indeed flourishing, to great effect. The following case study illustrates how BI can grow organically from prospect research. Annual Fund Unrestricted Receipts 9.000,000 8,000,000 7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 0
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CASE STUDY
Kelly Quin, Senior Director of Constituent Strategy with Rice University in Houston Texas, describes her position at Rice as ‘organic.’ “I started at Rice 20 years ago, and have served as Manager of Prospect Research, then Director of Prospect Research, and more recently, with budget issues and the need to reallocate functions, I was given oversight of campaign reporting and stewardship functions. Basically, I am like the chief firefighter,” she explains. “But my title really reflects what we are trying to do here at Rice, which is to strategize about how to reach the people we need to engage.”
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Development Research and Data Analysts Provide Context and Understanding Quin directs a team of four development research analysts, with one position currently vacant, and a Development Data Analyst, who is a statistician and business analyst for the department. “We are not just a group of people who find facts,” Quin shares, “we provide the context and understanding for why the information is important. Analysis has become a much more important focus of what we do.” They are nearing the end of a seven-year campaign begun in 2006. Around the time the campaign launched, they also ushered in an era of respect for data and data-derived decisions and strategies. For instance, for 15 years leading up to 2006 the Rice Annual Fund had been flat, raising about $4.1M per year. At Rice, annual fund is truly unrestricted giving, with designated giving, no matter the size, not counted towards annual fund receipts. With a new emphasis on data and analytics, the annual fund team and the constituent strategy team determined which constituents to target for larger gifts of $2,500 and up. They also initiated new business practices, infusing marketing techniques and better use of technology. For these reasons and more, annual giving began to grow, and in 2012 had doubled to $8.2M. The constituent strategy team at Rice has also focused on planned giving, tackling projects such as profiling their ‘typical’ planned gift donors to help them identify other similar prospects who might be ripe for a conversation around planned giving. They are also looking at time series data to help identify what their planned gift donors looked like when they made their commitment, to better understand the characteristics of these donors at the time of the decision to make a planned gift.
CASE STUDY
Research, Reporting and Analysis, Supported by Leadership, Form the Hub for Business Intelligence So how does prospect research and analysis become a hub of business intelligence? “Six years ago, we hired someone to manage the database, gift processing, records, etc., who was a real techy,” says Quin. For the past six years, they’ve focused on data cleanup, and built a data warehouse to house and merge disparate data sets. Crystal Reports sits on top of the data warehouse, which is fed by the Sage Millennium database, and “Voila’!” – robust reporting including on-demand reports and real-time dashboards.
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Clean, accessible data, robust reporting, and analytics are the building blocks of business intelligence. Marry these competencies with leadership who respects what data and those with data competencies can contribute, and you have data magic. Fortunately for Quin and her team, the leadership at Rice does respect the information and knowledge that data analysis can provide, and have established a Strategic Leadership Team that includes Quin.
“We’re frequently the translators between the business and the technology sides of the house. We hear the needs a program is having, and can figure out how to source the appropriate data and information, and what analysis to do, to assist in bringing understanding to the issue or help in finding a solution to test.” - Kelly Quin Senior Director of Constituent Strategy, Rice University The development leadership, program directors and managers for principal gifts, major giving, development services, alumni affairs, foundation relations, annual giving and talent management meet bi-monthly with Quin to discuss key issues and concerns. This allows Quin to serve as a liaison between the program managers and research, data and analytics. “We’re frequently the translators between the business and the technology sides of the house. We hear the needs a program is having, and can figure out how to source the appropriate data and information, and what analysis to do, to assist in bringing understanding to the issue or help in finding a solution to test.”
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CASE STUDY
Rice University is a leader in the data science trend in nonprofit organizations, demonstrating how acknowledging and leveraging the competencies in the prospect development arena can lead to business intelligence for the entire advancement unit. Data, reporting, analytics and leadership merge to form business intelligence and optimized decision making.
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The Future of Business Intelligence
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The Future of Business Intelligence Business Intelligence as we’ve defined it, the right decision support to the right people at the right time, while still being optimized by only a small fraction of organizations, is growing into a powerful and transformative business competency. We’ve looked at three examples of very different organizations using their data and analytics capabilities to optimize decision making. TT Currently, BI primarily relies on historic data and provides insights based on past transactions and/or behavior. As more and more organizations grow and invest in their BI infrastructure as well as develop more sophisticated analytic capabilities, BI will evolve to include more predictive analytics, where knowledge can be delivered to decision makers that not only suggests actions based on past events, but suggests strategies based on predictions of what may occur in the future. TT Data visualization will take center stage, as the importance of communicating analysis in easy to understand, real-time snapshots becomes the norm. TT Data will need to be shared on smart phones, tablets and other devices as well as on computers, so the ability to succinctly disperse needed information to any device and any location will be key.
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Building a Case for Business Intelligence
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Building a Case for Business Intelligence þþ Find the “Pain Points” in your organization. Talk with each decision maker, including marketing, development, community outreach, alumni relations, finance, human resources, and programming to determine which decisions they struggle with, where they are seeking new information and insight, and how your competencies can complement their needs. þþ For each, show how the structured approach to BI would alleviate uncertainty, and maximize accuracy and confidence in the decision. þþ Develop a map of current organizational competencies. Do several departments employ analytics and prospect and/or market research expertise? Where are there overlapping or complementing competencies? Can forming a loose or structured Business Intelligence Group or Unit improve collaboration, generate ideas or provide added attention to the potential value of business intelligence to the organization? þþ Find and form relationships with those in your organization who have the competencies you lack, perhaps data, IT, programming or other technical specialties. Or alternatively, on the business side, those who bring business strategy and business process understanding to the table. þþ Find and form relationships with decision makers in the business environment who believe in and will help champion your case. þþ Where possible, begin by helping with some of the smaller projects. Small successes will feed your case and your project pipeline. þþ Strive to develop a reporting relationship with strategic and business decision makers rather than with IT or technical decision makers. A BI unit can become the organization’s problem solving team, as long as it interfaces with leadership and is encouraged to flourish in a top-down environment. Where BI is housed and reports to Information Technology, projects tend to be bottom-up and the value may not be evident to leadership. More importantly, the problems being solved are less likely to have a strategic business purpose, and value to the nonprofit will be lost.
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Conclusion
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Conclusion Business Intelligence is not a tool or software program, it is a systematic and consistent process of identifying information and knowledge needs and filling those needs with data, reporting and analytics delivered to the right decision maker at the right time. As we have shown, any nonprofit organization can embrace and implement business intelligence to improve their decision making in every facet of their operations, from fundraising, marketing, and finance, to programs and measuring impact. Wherever you are in the business intelligence maturity model, from Oblivious to Optimizing, recognizing what business intelligence is and how to use it is an imperative for nonprofits who hope to grow and succeed into the future. In order to deliver social impact effectively and efficiently and to continue to raise funds in an ever more competitive and closely-scrutinized environment, nonprofits must invest in data, technology and analytics at whatever level is appropriate to their size and scope, and make data-informed decision making a part of their business practice. The alternative is to be left behind.
About WealthEngine ™, Inc. WealthEngine TM, Inc. is a leading provider of sophisticated wealth identification and prospect research services to charities, hospitals, institutions of higher education, political campaigns, advocacy groups, and other nonprofit organizations, as well as to firms that offer luxury goods and financial services. More than four thousand clients use WealthEngine’s products for comprehensive research on individuals, companies and foundations. Headquartered in Bethesda, MD, WealthEngine serves both the United States and the United Kingdom. For more information, visit www.wealthengine.com.
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