Chapter 2 Analytics Adoption

This is a data-driven world. It’s a really exciting time. Industries, both public and private, have been telling us for years that data analytics can make a real difference. With all the buzz around data science and its potential to drive insights and improve outcomes, the role of data analytics has transitioned from backroom operations to having its own seat at the leadership table as a strategic partner.

Before we get into how to use data science, let’s explore data analytics adoption, which is a very common challenge we encounter in this type of work.

We will answer three main questions in this chapter.

  1. How to translate business pain points into problem-solving opportunities.
  2. How to use change management to guide business understanding and stakeholder support.
  3. How to use continuous improvement as a framework to drive analytics adoption.

You can use the answers to these questions to make a case for analytics, start your own program, or take your existing program to the next level.

2.1 Journey from Operations to Strategic Partner

I (Rodger) have worked in higher education for over 15 years on projects ranging from enterprise IT infrastructure and software engineering to financial systems delivery and, most recently, advancement operations, information strategy, and decision support.

My graduate studies were primarily focused on search engine technology, and my main areas of technical expertise are information retrieval, natural language processing, and network analysis. Specifically, I researched document ranking functions and language models used by search engines to return the most relevant document matches relative to a given search query. Examples include Okapi BM25 shown in Figure 2.1, as well as other state-of-the-art retrieval-ranking function variants.

BM25 document ranking

FIGURE 2.1: BM25 document ranking

My additional areas of study and research included the network, structure, and diffusion of information across social systems. Figure 2.2 shows the structure of my social network connections in 2011, as well as four distinct groups of friends or communities I belonged to at that time: High School, Work, Ann Arbor, and Graduate School. I applied similar techniques to various kinds of data, ranging from healthcare and disease-symptom networks to research-citation networks, social media activity, and email communication logs to identify information structure, meaningful patterns, and actionable recommendations.

Rodger's social network map circa 2011

FIGURE 2.2: Rodger’s social network map circa 2011

During graduate school, I also participated in the Ann Arbor Data Dive, which is an open platform designed to share data analytics expertise with community partners, organizations, and non-profits. Figure 2.3 is a heat-map calendar visualization I created to summarize resource usage to help a non-profit identify homeless meal-demand planning trends and improve resource-allocation opportunities and programming for the next year.

Heat map calendar to help the hungry

FIGURE 2.3: Heat map calendar to help the hungry

Prior to working in fundraising analytics and information strategy, I used to work in an office “cube” writing client-server management tools and accounting software. This work involved storing, analyzing, and moving high volumes of transaction data across different types of financial database systems.

Backroom software engineer

FIGURE 2.4: Backroom software engineer

During my knitted-hat days in Michigan (OMG, it gets cold!), I created software to store, analyze, distribute, and audit the delivery of accounting information to over 50,000 users in real-time across a diverse set of computing systems, dependencies, and quirks. This infrastructure role required a unique combination of business process mapping, enterprise systems, and software engineering delivery that challenged me to listen, critically think, solve, and translate complex real-world problems into practical and scalable solutions at both the test prototype, staging, and production-ready levels.

I didn’t realize it at the time, but I now understand this role is where I learned how to gather, prioritize, and translate business requirements into project road maps or “blueprints” for solution delivery to stakeholders on time and within budget.

Business process map

FIGURE 2.5: Business process map

Enterprise systems

FIGURE 2.6: Enterprise systems

During my decade of working in IT operations and fixing broken things, I was also responsible for troubleshooting server software onsite in the “backroom.” And when I say I was in the “backroom,” I was literally working inside server closets to analyze, patch, and keep systems up and running with a high degree of availability (that is, 365 days a year) for faculty, students, and staff.

Hanging out in the server room

FIGURE 2.7: Hanging out in the server room

I love data analysis, applied statistics, and problem solving. Nowadays, I speak about analytics adoption, predictive modeling, and strategic information-management metrics at conferences and workshops to share my knowledge, expertise, and enthusiasm with professional communities of practice, research, and leadership.

My message is that you too can join the exciting field of data analytics right now and discover new ways to solve problems, accomplish your goals and make a real impact on your organization in more creative, innovative, and efficient ways than previously imagined.

2.2 Translating Problems into Opportunities

When we begin any data analytics project, the goal usually is to overcome a challenge, identify an opportunity, or add value to your business. In fact, the problems we encounter in our organizations are often the same drivers that motivate us to create and explore new solutions, tools, or products in the first place.

Before you jump into data analytics, it is crucial that you identify a key business problem that motivates a solution, recommendation, or decision support for your organization.

Business problems or “pain points” play an important role in defining and shaping the landscape and purpose of our work. Data analytics projects require identifying business problems (inputs), translating them into road maps (means), and delivering solutions (outputs) to your stakeholders.

What are the current business challenges in your fundraising organization today? Donor retention? Finding prospects? Increasing revenue? Operational efficiencies?

Here are some additional questions to explore and help you get started:

  • What are the “pain points” or perpetual obstacles that your organization faces?
  • What are some of the key business questions that remain unanswered?
  • What kinds of information or analysis would help improve decision making?
  • What kind of tools or solutions would help uncover new opportunities or untapped potential?
  • What areas would benefit most from increased efficiency and productivity?

2.3 Create a Road map

Now that you’ve identified a business problem, you will need to begin translating the problem into a solution-oriented road map.

A road map is a project specification that clearly outlines and defines the following components:

  • Goals and objectives (ROI)
  • Context and business drivers
  • Audience
  • Scope, size, and scale
  • Deliverable requirements
  • Timeline and key milestones
  • Dependencies
  • Resources (personnel, budget, staff time, systems, tools, and so on)
  • Potential risks
  • Development plan
  • Testing plan
  • Communication plan
  • Training plan

Road maps will obviously vary in depth and complexity based on the size and scale of your project.

To avoid analysis paralysis, I recommend first starting with a small project and documenting your current state and desired project outcomes. You can always, and will inevitably have to, go back and refine your original project plan.

Once you have a created your baseline project specification, it’s time to find support.

2.4 Identify Your Champions

Data analytics projects of any size, tiny or mighty, need leadership support as these projects require organizational resources, business alignment, and stakeholder buy-in to be successful.

So, who can help you convince others that your project is a worthwhile effort?

Ideally, you will be able to quickly identify at least one or two “champions” or individuals who:

  1. believe in the problem you are trying to solve,
  2. understand what you are doing, and
  3. can help promote and socialize your potential solution to others within and/or across your department, organization, or institution.
If you are currently unable to identify any champions or supporters, it would be advisable to return to the original problem you identified and evaluate whether the problem warrants the investment of time, energy, and resources.

If you determine the problem is still worth exploring, it would be best to identify stakeholders who are most directly affected by the issue since these individuals:

  1. are most likely to benefit from a working solution, and, therefore,
  2. are most likely to be inclined to discuss requirements, participate in a pilot effort, and offer critical feedback.

Start paying attention to which of your colleagues are “go-to” individuals, domain experts, and data stewards, since these stakeholders are likely well positioned to play an “ambassador” role in driving analytics adoption.

Other factors to keep in mind when seeking potential champions are leadership, managers, and colleagues who are quantitative, inquisitive, and passionate with strong connections across multiple departments as they can help provide valuable organizational insights, as well as promote, advocate, and diffuse solutions within the organization.

When a development officer or a senior leader gets excited about an analytics solution, you’re now starting to move the needle on analytics adoption. Three development officers who are excited about a new report or a data analytics tool’s ability to work more efficiently will quickly translate to an entire team of development officers who are standing in line to request more solutions.

2.5 Defining Business Purpose

Now that you’ve identified your champions, let’s build on the analytics introduction in the previous chapter and clarify what we mean by “data analytics.”

For some, data analytics might refer to a business report or an interactive leadership dashboard. For others, data analytics might refer to trend analyses, data visualization, cluster analysis, or predictive modeling.

All of these are correct answers.

For you, the answer will depend on the current level of analytics maturity within your organization.

2.6 Analytics Maturity

By this point, you’ve hopefully identified a business problem, outlined a road map draft, and identified some potential supporters. The next step is to assess the current level of analytics maturity within your organization and start where you are.

Analytics maturity refers to the degree to which your organization has invested, implemented, and integrated a set of systems, tools, processes, and technology to solve business problems using data analytics.

As outlined in Section 1.3, Analytics Maturity Model, analytics maturity is a continuum that organizes potential solutions into three main categories:

  • Descriptive
  • Predictive
  • Prescriptive

2.6.1 Descriptive Analytics

Descriptive analytics offers us views of the past. These views, in hindsight, help provide answers to diagnostic questions such as “What happened?” or “When and where did this happen?” and, in some cases, even “Why did this happen?”

2.6.2 Predictive Analytics

Predictive analytics shifts toward future insights and helps answer questions such as “What will happen?” For example, we can use past data to fit a regression model to make educated guesses and predictions about future outcomes using training data and variables with specific parameters.

2.6.3 Prescriptive Analytics

Finally, we have prescriptive analytics. Prescriptive analytics focuses on foresight, scenarios and decision support to help answer questions such as “What should we do right now?” or “Which option would be best if X or Y happens?”

2.6.4 Data Pyramid

The analytics maturity model builds on the foundational concept of the data pyramid (aka wisdom hierarchy), which refers to the systematic process of extracting actionable knowledge from data:

2.6.5 Pathways to Wisdom

Let’s explore the pathways to wisdom from a data perspective.

If you’re not familiar with database terminology, it might be helpful to think of a spreadsheet (two-dimensional) organized into rows and columns.

First, we begin with collecting and storing data values. This is our raw data.

Next, we transform raw data into information by organizing data values into rows (facts) and columns (features), which provide additional context and meaning.

Subsequently, we analyze information to identify important trends, patterns, and insights to generate knowledge.

Finally, we evaluate and synthesize knowledge in an organizational context to create wisdom.

Wisdom ultimately helps us understand what makes our organization unique relative to its markets, competitors, assets, capabilities, and opportunities.

2.6.6 Wisdom is Competitive Advantage

Why does wisdom matter?

Because wisdom is a form of competitive advantage.

“The only thing that gives an organization a competitive edge, the only thing that is sustainable, is what it knows, how it uses what it knows and how fast it can know something.”

— Larry Prusak, researcher

While your leadership may recognize that wisdom is valuable, we still face many challenges when we seek to introduce new tools, products, and solutions.

Examples include:

  • Lack of resources
  • Competing priorities
  • Change resistance
  • Information overload
  • Aversion to details
  • Preference for big-picture ideas
  • Preference for narrative

In the next section, we will explore adoption challenges in greater detail and some specific ways to approach and overcome them.

2.7 Adoption Barriers

As you prepare to launch your own analytics project or program, it is advisable that you directly ask your stakeholders what kind of adoption barriers they expect to encounter during the launch of your proposed analytics tool, product, or service.

While you solicit feedback, it is important to listen carefully, objectively, and openly as well as documenting any anticipated barriers.

Let’s take a look at common adoption barriers you may encounter during the project planning process.


  • Lack of champion support, early adopter participation, and advisers
  • Lack of resources to acquire talent and build capacity
  • Lack of stakeholder engagement, interest, and commitment

Business Requirements/Specification

  • Lack of domain understanding or business context
  • Unclear definition of success and requirements
  • Failure to define or manage expectations


  • Trying to do too much, too quickly, too soon
  • Struggling to translate results into actionable insights
  • Fear of failure during the prototype phase and learning process
  • Lack of a feedback loop


  • Lack of trust required to build, enable, and foster adoption across organization
  • Lack of staff training in how to interpret meaning and use of analytics in work context
  • Lack of perception of analytics as a strategic partner

2.7.1 Know Your Audience

As you seek to identify, document, and manage analytics adoption barriers, it’s also important to understand your audience’s preferences (and differences) as you gather requirements, prioritize features, design solutions, and communicate with your stakeholders.

While it is often a useful starting point to divide your audiences into managerial and individual contributors, it is also important to recognize technical versus relational preferences.

Here’s a simple and generalized way to think about these different preference types:

  • People with technical orientation prefer spreadsheets over people

  • People with relationship orientation prefer people over spreadsheets

Our organizations comprise individuals with highly variable and complementary skill sets.

For example, a development officer with exceptional relational skill sets may not find the new database reporting features as intuitive as an operations staff member with highly developed technical skills.

While some individuals are comfortable with technical details, “nuts and bolts,” and “the weeds,” others may tend to prefer the “big picture,” “takeaways,” and “recommended action.” Another term for this type of preference is a means orientation (how) versus an ends orientation (what).

For example, if you’re working on an analytics project designed for development officers, we advise you to focus on executive summaries, workflow, recommended actions, and relational tasks to help them in the context of their individual portfolio, keeping in mind that the end goal is to help them get out of the spreadsheet and maximize activity and connectivity with prospective donors.

In contrast, an analytics solution for an operations staff member, such as a reporting tool, would likewise benefit from shifting focus from high-level constituent summary information to delving into a high degree of technical detail, granularity, and drill-down such as giving history, pledge payments, pledge balance, and soft credit.

Keeping your audience in mind is an effective way to (1) ensure your analytics project will be useful, actionable, and well received by your organizational audiences; (2) be aligned with their business needs; and 3) add organizational value.

“Two basic rules of life are: 1) Change is inevitable. 2) Everybody resists change.”

— W. Edwards Deming

2.7.2 Adoption is Change

Analytics adoption refers to the process of introducing a new tool, product, or solution to your organization. Simply put, adoption means change. And change is difficult for most individuals, let alone an entire organization.

2.7.3 Competency and Motivation

Adoption requires change, which requires persuasion and stakeholder buy-in. Some of your stakeholders may be very resistant to change and, as you dig deeper, you might uncover various forms of fear (expressed as resistance) such as fear of disruption, fear of job displacement or, simply, fear of the unknown.

The most common barriers to change are (1) a lack of competency (ability or capacity) and (2) motivation (willingness or inclination).

For example, change barriers may be evident when your users say, “I don’t know how to use the new system” (competency) or “I just want to use the old report” (motivation).

So how do we overcome the adoption barriers we encounter in our organization?

Once we identify the adoption barriers and root cause, we need to create an action plan tailored to the specific underlying issue in the context of the users’ goals, workflows, and preferences.

Competency-based barriers may require skills training and professional development, whereas motivational barriers may require a revised clarification of purpose and outcome.

In either case, elevating staff skills is a recommended strategy for facilitating and accelerating analytics adoption. One immediate suggestion would be to read (or have someone read) this book!

2.7.4 Building Trust

You can inspire your audiences to embrace analytics adoption by winning hearts and minds.

To accomplish this goal, you need to consistently present your stakeholders with solutions with a balanced blend of (1) positivity and enthusiasm and (2) logical thinking and rational problem-solving.

Regardless of the sophistication of your analytics adoption plan, you will need to quickly establish trust and credibility to improve receptivity to your proposed solutions; otherwise, your ideas may get tuned out despite the best of intentions and efforts.

To build trust with stakeholders, you need to build your brand by presenting results (proof, facts, data) with accountability (plan, action), and purpose (objectives).

2.7.5 Quick Wins

Ideally, your quick win, while small in size and scope, will generate immediate positive feedback and good will towards future successes. Moreover, you can use this “quick win” as a discussion point to solicit feedback and elicit requirements for your next analytics project.

To quickly establish credibility, you need to identify a “quick win,” which is a highly visible problem you can quickly solve to build momentum and make the case for additional analytics projects.

Example feedback questions might include, “How did this solution work out for you?” or “Glad we resolved that issue. What else would be useful or helpful?”

2.7.6 Build Your Brand

To build your brand, you need to lead with purpose and socialize your success. You can increase the visibility and exposure of your brand by taking on more of a consulting role in your work, building partnerships, and promoting solutions.

For example, if you currently work in advancement operations, when was the last time you sat down with a development officer to check in and ask what was going well, or which tools could help them be more effective in their roles?

Questions such as “Do you know how to pull that prospect briefing report?” or “Do you know how to pull giving details for that donor?” can help establish strategic partnerships and build a culture of data-driven decision support.

2.7.7 Purpose

You won’t be able to persuade others to adopt analytics without clarity of purpose.

What is the top-priority project that you need to work on? Maybe it’s portfolio analysis or building a predictive model to identify new prospects. But ask yourself, “Why does that matter right now? Who are the stakeholders of this work? What will this do for them? And if they don’t apply your insights, what will happen?”

All of these questions are focused on the power of purpose. Their thinking is: “What are we going to do? We’re going to build an analytic model. How are we going to do that? We’re going to find a data analytics minded person, a statistician, or a graduate student. We’re going to find consultants.”

What’s missing is the why. However, if you can answer the question of why, you make the value of the proposition more compelling and help guide a more strategic conversation. Your purpose highlights your intention, which should ideally align with your organizational mission. For fundraising professionals, there is a common and shared purpose of trying to increase donors and dollars raised.

Suppose you want to create a best-in-class analytics program and you declare “We’re going to hire the best people. We’re going to create dashboards.” You’ve articulated the “what” and “how,” but there will likely be skepticism and resistance with valid questions such as “What’s the return on investment?” or “How will this add value to the organization?”

By clarifying the “why,” you can strengthen your proposal and minimize resistance by explaining your purpose:

“We want to use state-of-the-art data analytics as our competitive advantage to maximize our fundraising results and promote learning for the long-term health of the organization.”

2.8 People, Talent, and Culture

We need both top-down and bottom-up management, but you have to start where you are.

If you’re in an organization in which there’s no appetite for analytics, then you have an uphill battle. Maybe you’re at an organization that appreciates data analytics, yet they say, “I don’t really need that stuff.” That’s a bottom-up challenge. In both cases, start identifying the people you need to get on board. When I began my new role, my first three development officers said, “I like numbers and want more data” and I said, “I’d like to partner with you on some new data analytics solutions. Would it be OK to circle back with you as an early tester to get your feedback?”

Let’s explore how solutions are delivered within your organization.

How are you building and deploying solutions? How are you collecting feedback? How are you integrating feedback into your analytics projects, tools, and services?

In the next section, we will discuss how to use change management to drive business understanding, audience alignment, and stakeholder support.

2.9 Change Management

Change management provides us with a framework to 1) organize the planning, development, testing, and delivery of solutions; and 2) communicate these changes to our stakeholders in a meaningful way that fosters accountability, transparency, and trust.

Here are some change management questions to guide your analytics adoption planning process:

  • Who are your target audience and stakeholders for this project?
  • What do you specifically hope to accomplish with this project?
  • How will stakeholders and the organization benefit from this effort?
  • What are the business requirements and ROI?
  • What is the project deliverable schedule and timeline?
  • What does success look like for this project?
  • How are the stakeholders supposed to interpret and apply results?
  • Who are your project testers for the pilot or testing effort?
  • What is the communications and staff training plan?
  • Where is the documentation available for self-service or instructor-led support?

2.10 Managing Hype, Priorities, and Expectations

You might often hear “I want a dashboard and I want it on mobile. I want a predictive modeling score.” Somebody else says, “I want an engagement score.”

All of a sudden you may find yourself pulled in multiple directions at the same time. You might receive requests for a data visualization or RFM tool, but it will be difficult to realize success without sufficient context, business requirements, and feedback.

Even if you deliver the most sophisticated data analytics solutions, your stakeholders are often most concerned with how to interpret these solutions and apply them in the context of their own work.

Just as we are familiar with the importance of being donor centric, we need to keep a similar user-centered focus with solution delivery, as our stakeholders often question “How do I take action on that?” or “You created some nice RFM scores, but what’s RFM and why does that matter to me?” While keeping your stakeholders in mind, it’s also important to recognize the Hype Cycle, made popular by Gartner®:

Perhaps you’ve thought, “I’m excited about this new technology solution!” However, when you go to your leadership, they may not necessarily share the same level of enthusiasm in the product, saying “That technology is limited” or “The platform is still in development, so perhaps we should explore other options”. The notable decline in interest in a once-popular technology, platform, or solution is also known as the “trough of disillusionment,” which is a common and predictable phase in the development life cycle of any new technology.

2.11 Critical Mass

To survive the Hype Cycle, you must stay focused on the business drivers that motivate your project’s innovation, adoption, and solution delivery.

Most organizations invest in tools such as database systems, CRM applications, and reporting platforms with two primary goals: (1) increasing production or results, and (2) gaining operational efficiencies.

We then expect lift and a boost in dollars, as well as operational efficiency.

To be successful, an analytics project will require organizational investment and buy-in to reach a critical mass of user adoption.

How do you translate the enthusiasm for new tools and technology into user adoption? How do you create an action plan to put some life into your idea or innovation?

2.12 Diffusion of Innovation

The Diffusion of Innovation rule suggests that any major change or innovation needs to be adopted by at least 16% of your target user base. This 16% represents the critical mass required to make a proposed innovation widely adopted and self-sustaining within your organization. In other words, it is critical to reach at least 16% adoption of your potential audience to ensure long-term stakeholder adoption and integration of your solution.

First, you will need to enlist the support of your champion and stakeholders to help identify early adopters, testers, and pilot users.

Key questions to keep in mind when engaging your baseline of pilot users:

  • What are the key business drivers (purpose) for implementing this data analytics solution?
  • How will this solution or innovation be used in the context of your stakeholder’s work?
  • How will this solution help make a difference?

2.13 Continuous Improvement

Continuous improvement offers a philosophy of iterative design and user-centered problem-solving that values stakeholder feedback and efficiency.

Awkwardness is the prerequisite of learning.

If you’re going to learn how to walk, you’re going to fall down. As you deliver analytics solutions within your organization, you will eventually create a model that fails or produces unexpected results. For this reason, it’s important to promote a culture of learning with your stakeholders and teach them early on that testing, experimentation, and interpretation are part of the inevitable learning curve and discovery processes associated with data analytics.

But also recognize that there are many different options. If you wait for the perfect tool to come along, five years could go by quickly and you still won’t have done anything about it. There’s a cost to inaction.

Philosophically we’re seeking to create a shift from a reactive orientation to a proactive one.

Maybe right now in your organization you’re creating reports because someone asked you to, but in an organization with analytics maturity, you’re also going to be proactively exploring predictive and prescriptive types of questions such as “We analyzed the data and found the top five prospects of interest that you should reach out to right now”.

2.14 Wrapping Up

Data analytics is a strategic partner that can provide competitive advantage and solutions that are understandable, memorable and, most importantly, actionable.

To get started, translate your current business problems and questions into opportunities to create an analytics road map. Next, identify your champions, early adopters, and testers to reach critical mass for your proposed analytics project. As you build solutions and implement these changes within or across your organization, you will need a framework to facilitate analytics adoption.

Change management provides a useful framework for developing, testing, delivering, and communicating solutions with your stakeholders. In addition, continuous improvement offers a practical philosophy and approach to managing quality and enhancement requests.

To support analytics adoption, it’s important to promote a culture of learning within your organization since analytics frequently generates actionable insights that may challenge conventional or prevailing wisdom.

You do not have to (nor could you ever possibly) know everything. Data analytics is a field that is constantly evolving, so seek to become a learn-it-all instead of a know-it-all. In addition, there are free open-source tools available like R (covered in this book) that can be a powerful asset on your data analytics journey.

Even if you don’t consider yourself technically skilled, you can still invite others on your data analytics journey to create solutions and add value to your organization. And who knows, you might be curious and motivated enough to learn R yourself or connect with a colleague who can partner with you to build and deliver data-driven solutions!