White Paper Business Intelligence - WealthEngine

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White PaperBusiness IntelligenceA Model for NonprofitsTrends in Fundraising forNonprofit Organizations

About This PaperNonprofits are adopting and embracing a data-informed decision making culture. Analytics,descriptive statistics, predictive modeling, and real-time reporting have become familiar conceptsand valuable tools in the fundraising office. The use of data and analytics has expanded bothvertically and horizontally over the past several years. Vertical expansion is evidenced by thegrowing use of sophisticated tools and methods in the creation of analytic outputs such aspredictive models and resource optimization analyses. Horizontal expansion is occurring as thesuccesses 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 prospectidentification and optimize annual giving campaigns are the same methods and competencies thatcan determine the optimal balance of front line fundraising staff and support staff, the non-monetaryvalue 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 businessintelligence within their grasp. For these, the only remaining obstacles to implementing a businessintelligence environment is recognizing the potential, and developing a structure in which it canbe leveraged and thrive. This paper provides an expansive view of where a business intelligenceunit could take these organizations in the future. For many other nonprofits, still developing theawareness of and competencies related to analytics and data-informed decision making, this paperwill help identify the areas that must be targeted for improvement before true business intelligencecan be realized.WealthEngine Publications Team:Special Thanks to:Tony Glowacki, President and ChiefSarah Janesko, Program Assistant, Children’sSally Boucher, Director of Research,Jess Kean, Program Associate, Children’s Causefor Cancer AdvocacyShane Bair, Creative DirectorMegan Martin, National Manager, BusinessIntelligence Operations, JDRFExecutive OfficerWealthEngine InstituteWendy Tanner, MarketingCommunications SpecialistCause for Cancer AdvocacyKelly Quin, Senior Director of ConstituentStrategy, 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 informationcontained herein has been obtained from sources believed to be reliable. This document is informational in nature and we donot 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, omissionsor inadequacies in the information contained herein or for interpretations thereof and shall not be held liable for any claims orlosses that may rise from the implementation of the best practices in this report. This document includes ideas for enhancingWealthEngine’s products. These ideas are subject to change at any time.

ContentsWhat Is Business Intelligence?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2The Nonprofit Business Intelligence Model6About Our Survey8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Five Stages of Maturity of Business Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Key Characteristics of Data-Informed Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Technical Support. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Getting Started with Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Characteristics of Data-Informed Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Challenges to Achieving Business Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21The Intelligent Nonprofit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22Analytics in the Small Shop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Organization-wide Business Intelligence Implementation in a National Nonprofit. . . . . . . . . . . . . . . . . . . 27Business Intelligence and the Prospect Research Community. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32The Future of Business Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40Building a Case for Business IntelligenceConclusionBusiness Intelligence1

What Is Business Intelligence?2

While there are many definitions of businessintelligence (BI), for our purposes in this white paper,we are defining it as “the right decision support tothe right people at the right time.” What does thismean? It means using the data you have availableor can acquire, enriching it appropriately as needed,analyzing it to create information and knowledgeto answer business questions, and presenting thatnewly discovered, evidence-based knowledge inan understandable and articulate way to the rightdecision maker at the right time in order to producethe best possible decision and outcome.AnalyticsDataReportingTHE RIGHT DECISION TO SUPPORTTHE RIGHT PEOPLE AT THE RIGHT TIMEBusinessIntelligenceFigure 1: The building blocks of business intelligenceThe building blocks of business intelligence are data, reporting, and analytics.Data is the essential and fundamental element of every business and nonprofitorganization. Data is a collection of facts and figures that are meaningless on theirown, 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 processesorganizationTT value related to costs and investmentsTT transactions, sales and interactionsTT return on equity / return on investmentWithout data, there is no business.Business Intelligence3

Reporting is what takes raw data and turns it into meaningful information.Reporting can take many forms, and most nonprofit organizations havereporting capabilities based on one or more of their electronic data sources,such as their donor management system or their accounting system. Moresophisticated organizations have a data warehouse that can house and mergemultiple data sources and from which report writers or analysts can extract dataand information. Reports typically answer questions such as “What happened?”“How does this outcome compare with that outcome or projections?” or “Whatis the trend?” Many organizations have come to value real time reporting in theform of dashboards that report on Key Performance Indicators (KPIs) either inreal-time with instantaneous updates, or with these key metrics being updatedon a daily or nightly basis.Analytics is the third essential component of business intelligence. Analytics takesdirection from leadership, decision makers and managers, by understanding thebusiness questions they need to have answered or what pain points they arefeeling in their processes and procedures. With this business understanding,analysts can then, using a more technically-based skill set, work directly withprogrammers, data specialists and IT professionals to identify the needed dataand extract, merge and enrich it if necessary, to perform the necessary analysis.Analysis transforms the data and information from reporting into the knowledgeneeded to make optimal decisions, and answers questions such as “Why did thishappen?” “What will happen if ?” and “What is the best possible outcome?”When data and reporting are supported by analytics, an organization can answerthese types of questions and is then practicing true business intelligence. We willdiscuss in more detail the methods and competencies needed in each of thesethree components of BI, and where they may reside within a nonprofit organization.Figure 2 shows how these elements fit together and serve the overall informationflow and strategy in a nonprofit organization.4

BUSINESS ENVIRONMENTDEVELOP INFORMATIONSTRATEGYRESEARCHERS,ANALYSTS & REPORTDEVELOPERSDATABASESPECIALISTSINFORMATION SUPPLYOPERATIONALDECISION MAKERSINFORMATION REQUIREMENTSMANAGEMENTTEAMDEFINE INFORMATION ANDKNOWLEDGE NEEDSCREATE AND PRESENT INFORMATIONAND KNOWLEDGE FROM DATA TO MEETBUSINESS DEFINED NEEDSMERGE, ENRICH AND MAKE DATAACCESSIBLE TO BUSINESS USERSSOURCE DATA AND CREATEINFRASTRUCTUREINFORMATION TECHPROFESSIONALSTECHNICAL ENVIRONMENTFigure 2: A model of BI for nonprofit organizationsBusiness Intelligence5

The Nonprofit BusinessIntelligence Model6

The Nonprofit Business Intelligence ModelIn this model (see Figure 2), there are five bands of competencies and activityrelated to the overall information strategy of the nonprofit.TT Nonprofit management, including board, trustees, CEOs and ExecutiveDirectors, set the information strategy for the organization.TT Specific information, knowledge requirements and needs are determined byoperational decision makers such as department heads and managers inconcert 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 andothers within this band must be articulate in the business environment as wellas the technical environment, as they will next work with database specialistsand perhaps IT professionals to articulate the data and reports needed to answerthe 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 businesspurpose as specified by the bands above.TT A functional data environment is supported by technical professionals whoensure the database or data warehouse infrastructure is functioning and thatif there is additional data that must be collected or sourced to meet businessneeds that these needs are met.In this model, each band represents a competency that must be present inthe organization for a fully realized business intelligence model to function.That is not to say that each band or competency must be filled by uniqueindividuals. Particularly in smaller organizations, one individual may serve asthe technical specialist, data specialist and analyst, or as a department head,analyst and data specialist. The key is to understand the importance of eachrole, and that there is a needed bridge between the business and technicalenvironments which often resides with the analyst, prospect researcher, orprospect manager.Business Intelligence7

About Our Survey8

About Our SurveyIn an effort to understand and illuminate the evolution of nonprofits from the mostlyqualitative, sensory practices of the 1990’s , when there was more “art” in the art andscience of fundraising, to the more quantitative, science-based practice we are seeingin 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%Education5%Social/Human gious OrganizationCommunity OrganizationAdvocacy18%14%Figure 3: Types of organizations represented by survey respondentsThirty-one percent of respondents represented higher education, 18% representedsocial and human services, and 14% represented healthcare. Arts organizations,community organizations and environmental causes also figured prominently, asshown in Figure 3.4%17%19% 25MM and over 5MM to under 25MM 600K to under 5MM20%Under 600kDon't know/Not sure40%Figure 4: Responding organizations raised from under 600K to over 25MIn 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 Intelligence9

Fundraising budgets ranged from 10K and below to over 25M, with frequenciesshown 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 25M5%9%13%Data and AnalyticsCorporate/Foundation Giving12%12%Planned GivingAnnual Giving/MembershipStewardship/Donor Relations9%12%Special EventsMajor GiftsMarketing and Public (Alumni) Relations14%14%Technology and Information ServicesFigure 6: Survey respondents by job responsibilityFigure 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 andpresent a model for nonprofits to follow as they move up the maturity continuumtowards achieving full business intelligence. We will also share examples fromorganizations that are pushing boundaries at one level or another and provideguidance for those who are looking for next steps to increase their maturity level.10

Five Stages of Maturityof Business IntelligenceIn the survey we asked numerous questions about databased analysis, decisionmaking, data sharing mechanisms, reporting and technicalenvironments. Among the1,126 responses, we were ableto identify five distinct stagesof maturity in terms of the useof data, reporting and gOptimizing31%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 bestruggling with the demands of day-to-day existence, and are therefore obliviousto 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 informdecisions. These organizations are aware of the innovations taking place in theindustry, 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 basicprocesses, like developing budgets and creating goals. They are usinginformation as available to support decisions, but have not yet fully embracedthe data revolution and are not systematically practicing data based decisionmaking. 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, makedecisions about resource deployment, and have embraced the advantages of ananalytic decision making culture. These organizations are investing in the toolsand competencies needed to navigate the world of Big Data and analytics.TT Optimizing: 3% of our survey respondents were “very data-informed,” on thecutting edge of the data revolution, pushing the boundaries of what theymeasure and analyze. These organizations are investing in data, reporting andanalytics, and have a structure in place to leverage these competencies and usethem throughout their division and/or organization.Business Intelligence11

Key Characteristics of Data-Informed OrganizationsMost of the organizations we identified in our survey as being Investing andOptimizing organizations shared characteristics relating to their data, technical,reporting and analytic environments. They also faced similar challenges thatdiffered markedly from their Oblivious and Aware counterparts.DataWhen asked about enriching data through wealth screening or data appends, afull 78% of Investing and Optimizing organizations updated their constituent dataeither 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 Awaregroup either never screens or screens only once every five years. These results arepresented in Figure 7.Investing and Optimizing Organizations Screen Data More Frequently100%90%80%70%Anually60%Peridocially in small batches50%Every 3 years40%Every Five ngOptimizingFigure 7: Screening frequency by BI maturity levelEnriching data with wealth, demographic, biographic and lifestyle/behavioralappends allows organizations to glean more insight on their constituents, andhave a richer set of data for more robust analytics.Organizations were asked what sources they use for data collection, with freeinternet based sources, wealth or asset screening, and subscription based sourcesbeing the most commonly used. Other sources used are indicated in Figure 8.12

Optimizing organizations were apt to use a wider variety of sources than Obliviousor Aware organizations, and more apt to use focus groups, surveys and predictivemodeling to generate data. Using data from a variety of sources provides aspectrum of information that contributes to a more accurate overall picture, aswell as preventing any reporting bias which occurs when the same data and datasources are overused to the exclusion of others.Free Internet, Wealth Screening and Subscriptions Are Most Frequently UsedFor Data cesng0Figure 8: Resources used for research and data enhancementTechnical SupportIn terms of technical environments, a strong support system of IT and ISappears to be a critical component of successful Business Intelligenceoperations. 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 Intelligence13

Optimizing Organizations Express Most Satisfaction with Technical Support100%90%80%70%60%Very SatisfiedSomewhat Satisfied50%Neither Dissatisfied nor Satisfied40%Somewhat DissatisfiedVery tingOptimizingFigure 9: Satisfaction with technical support by BI maturity levelAs illustrated in Figure 10, the satisfaction level of respondents was somewhat correlated to the type of support available, with Optimizing and Investing organizationsmore likely to have dedicated technical support or organization-wide support whileOblivious and Aware organizations were more likely to have no technical support.Optimizing/Investing Organizations More Likely to Have Dedicated Tech Support100%90%80%We outsource our technical support,obtaining support when and whereneeded70%We have technical supportspecifically dedicated to development/fundraising and research60%50%We have technical support coveringthe entire organization, but notechnical support dedicated todevelopment/fundraising research40%30%We have no technical re 10: Types of technical support by BI maturity level14Optimizing

More dramatically, in Figure11 it is quite evident that organizations that arehigher on the maturity scale are planning strategically for technology needsand investments. It stands to reason that organizations who are optimizingtheir investment in data would want to ensure they have the plans in place tosustain that investment into the future. Organizations on the lower end of thescale have a noticeable lack of strategy planning around technology, and shouldconsider bringing this need forward in their planning cycles. There is certainlyno evidence to suggest that reliance on technology will abate in the future.On the contrary, essentially all predictions indicate an increased dependenceon technology.Optimizing and Investing Organizations are Much More Likely to Have aTechnology Strategic Plan100%90%80%70%We have a well-crafted, up-to-datetechnology strategic plan60%50%We have some technologyconcerns included in ourorganizational strategic plan40%We have no technology stingOptimizingFigure 11: Strategic planning for technology by BI maturity levelReportingIn addition to data and technical support, organizations that are higher on thematurity scale of Business Intelligence have more sophisticated systems of datareporting in place than those who are lower on the scale. When asked to describetheir reporting environment, answers ranged from “We don’t have time for reports”to “We have dashboards with real-time reporting.”Business Intelligence15

Getting Started withReportingReporting does not need to be the burden of time and drain on resources manyless sophisticated organizations see it as. Here are a few tips for keeping reportingin 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 performanceindicators, design reports to show the metric (performance indicator)against a benchmark – your goal, your past performance, a peer ornational standard benchmark.TT One-time setup. Once the report is designed, work with your databaseadministrator, 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 isprogrammed, run it regularly and distribute to a predetermineddistribution list.TT Stay on course. Do not allow “ad-hoc” report requests to derail your effortsto keep reporting simple and streamlined. Put all but the most urgentad-hoc report requests in a folder and consider how some of the morefrequent requests can be accommodated in periodic revisions of yourstandard reports.16

As Figure 12 indicates, reporting capabilities are an essential ingredient of thebusiness intelligence environment, and those organizations that have no time forreports, or have trouble getting needed information in a timely way, are much lesslikely to be investing or optimizing their use of data, information and knowledge.Mature BI Organizations are More Likely to Have Dashboard Reporting100%90%80%70%No time for reports60%Dashboards with real-time reportsPull own reports from DMS50%Static Rpts issued periodicallyRequest as needed from imizingFigure 12: Type of reporting by BI maturity levelRespondents were able to select multiple answers for this question, so it isinteresting to note also that Investing and Optimizing organizations are morelikely to use multiple methods of reporting, with the most frequent combinationbeing 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 pullreports on their own, and get access to real-time KPIs through dashboardreporting, would be a step in the right direction.AnalyticsInvesting and Optimizing organizations were far more likely to express satisfactionwith analytic output than other categories, and Oblivious and Aware organizationswere significantly more likely to be very dissatisfied or somewhat dissatisfied withanalytic output. These results are illustrated in Figure 13.Business Intelligence17

Mature BI Organizations are Much More Likely to Be Satisfied or Very Satisfiedwith Analytic Output100%90%80%70%Very Satisfied60%Somewhat SatisfiedNeither Dissatisfied nor Satisfied50%Somewhat Dissatisfied40%Very tingOptimizingFigure 13: Satisfaction with analytic output by BI maturity levelAnalytics projects range broadly among all levels of BI maturity. Figure 14 showsthe types of projects that are benefitting from predictive modeling and otheranalytic output.Predictive Model Applications for FundraisingSocial media planningCorporate and foundation givingEvent list developmentEvent strategy and planningPlanned giving programDirect mail or emailProgram strategy and planningAnnual fundMajor giving program050Figure 14: Applications for predictive models18100150200250

Characteristics of DataInformed OrganizationsOur survey indicates that data-informed organizations:TT Enrich their data with screening ona regular basisTT Use a variety of other sources fordata enrichment when needed,such as surveys, focus groups,and demographic appendsTT Are much more likely to have aData and Analytics department ordesignated individual conductinganalytics than other respondentsTT Are actively planning for technologyneeds into the future either as partof their strategic plan or with aseparate technology strategic planTT Have a satisfactory or very satisfactoryopinion of their technical support,with a slight preference for dedicatedtechnical support or organizationwide technical support over outsourced support or no supportTT Have access to information in theform of reports, preferably by pullingreports directly on their own and/orby accessing real-time data throughdashboardsTT Have leadership that supports andencourages measurement of ROI andaccountability for resultsThe most frequent conductors of analytics within nonprofit organizations arethe annual giving department, the prospect research department, executiveleadership, prospect management and major giving departments. As i

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

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