2 Audiences and challenges
2.1 Audiences
Sometimes in analytics we write for ourselves, part and parcel to analysis. In another context, writer Joan Didion captured this well:
I write entirely to find out what I’m thinking, what I’m looking at, what I see, and what it means (Didion 1976).
But our introspectives are not optimal for others.
2.1.1 Analytics executives
Let’s imagine, for a moment, we share the mind of Citi Bike’s Chief Analytics Officer. We know what the public experiences with our bike sharing program as has, too, been reported in the news. The newspaper West Side Rag, for example, quoted Dani Simmons, our program’s spokeswoman. She explained: “Rebalancing is one of the biggest challenges of any bike share system, especially in … New York where residents don’t all work a traditional 9-5 schedule, and … people work in a variety of other neighborhoods” (Friedman 2017). As a Chief Analytics Officer, one of our jobs includes analyzing, and overseeing the analyses of, data that inform decisions for solving problems for our organization (Zetlin 2017), like rebalancing.
Would it interest us if we opened an email or memo from one of our data analysts that began:
Citi Bike, a bike sharing program, has struggled to rebalance its bikes. By rebalance, I mean taking actions that ensure customers may both rent bikes and park them at the bike sharing program’s docking stations….
Are we — Citi Bike’s Chief Analytics Officer — motivated to continue reading? Do we know why we should, or whether we should spend our time on other matters? What might we think of the data analyst from whom we received that communication?
Returning, now, to our own minds: for what audience(s), if any, might such a beginning be interesting or helpful? How can we assess whether the communication is appropriate, even optimized, and if not, adjust it to be so? That’s the focus of this text.
As we hone our skills for communicating with intended audiences, we’ll consider other minds, too: executives in analytics, marketing, chief executives, both individually and mixed with secondary or more general audiences. Consider Scott Powers, Director of Quantitative Analytics at the Los Angeles Dodgers, who earned his doctorate in statistics from Stanford University, publishes research in machine learning, codes in R, among other languages, and has worked for the Dodgers for several years.
2.1.2 Marketing executives
A Chief Marketing Officer shares some responsibilities with the analytics officer and other executives, while other responsibilities are primarily her own. Meet David Carr, Director of Marketing Strategy and Analysis at Digitas (a marketing agency) who has written to, and about, his marketing colleagues and their uses and misuses of data. Broadly, he or she leads responses to changing circumstances; shapes products, sales strategies, and marketing ideas, collaborating across the company.
Carr (2019) describes three main types of value that marketing drives:
- business value: long and near-term growth, greater efficiency and enhanced productivity
- consumer value: attitudes and behaviors that effect brand choice, frequency and loyalty
- cultural value: shared beliefs that create a favorable environment in which to operate and influence
He illustrates his research of, and experience with, these values graphically, as a central circle, and in concentric rings identifies various characteristics and details related to these values.
Relatedly, Carr (2016) has mapped out the details for designing and managing a brand, and explained its interconnections:
The brand strategy should be influenced by the business strategy and should reflect the same strategic vision and corporate culture. In addition, the brand identity should not promise what the strategy cannot or will not deliver. There is nothing more wasteful and damaging than developing a brand identity or vision based on strategic imperative that will not get funded. An empty promise is worse than no promise.
We can tie many aspects of brand building and marketing value to measurements and data. Carr (2018) explains how marketing does — and should — work with data. His article suggests how we should craft data-driven messages for marketing executives.
2.1.3 Chief executives
Typically, the analytics and marketing executives report, directly or indirectly, to the CEO, who has ultimate responsibility to drive the business. Bertrand (2009) reviews empirical studies on the characteristics of CEOs. They write, while “modern-day CEOs are more likely to be generalists,” more than one quarter of those running fortune 500 companies have earned an MBA. The core educational components of the MBA program at Columbia, for example, include managerial statistics, business analytics, strategy formulation, marketing, financial accounting, corporate finance, managerial economics, global economic environment, and operations management (Columbia University 2020). This type of curricula suggests the CEO’s vocabulary intersects with both analytics and marketing. Indeed, Bertrand explains that “current-day CEOs may require a broader set of skills as they directly interact with a larger set of employees within their organization.” If they are fluent in the basics of analytics and marketing, their responsibilities are both broader and more focused on leading the drive for creating business value. Our communications with the CEO should begin with and remained focused on how the content of our communication helps the CEO with their responsibilities.
In communicating, we should keep in mind that audiences1 have a continuum of knowledge. Everyone is a specialist on some subjects and a non-specialist on others. Moreover, even a group of all specialists could be subdivided into more specialized and less specialized readers. Specialists want details. Specialists want more detail because they can understand the technical aspects, can often use these in their own work, and require them anyway to be convinced. Non-specialists need you to bridge the gap. The less specialized your audience, the more basic information is required to bridge the gap between what they know and what the document discusses: more background at the beginning, to understand the need for and importance of the work; more interpretation at the end, to understand the relevance and implications of the findings.
Exercise 2.1 Identify a few analytics, marketing, and chief executives, and research their backgrounds. Describe the similarities and differences, comparing the range of skills and experience you find.
2.2 Challenges
2.2.1 Information gaps
One challenge in communicating data analytics is understanding what we see. For that, we might consider again Didion (1976) thoughts as part of our project. We generally revise our written words and refine our thoughts together; the improvements made in our thinking and improvements made in our writing reinforce each other (Schimel 2012). Clear writing signals clear thinking. To test our project, then, we should clarify it in writing. Once it is clear, we can begin the processes of data collection, further clarify our understanding, begin technical work, again clarify our understanding, and continuing the iterative process until we converge on interesting answers that support actions and goals.
More overlooked, to be explored here, is communicating our project effectively to others. Consider the skills typically needed for an analytics project. The qualities we need in an analytics team, writes Berinato (2019), include project management, data wrangling, data analysis, subject expertise, design, and storytelling. For that team to create value, they must first ask smart questions, wrangle the relevant data, and uncover insights. Second, the team must figure out — and communicate — what those insights mean for the business.
These communications can be challenging, however, as an interpretation gap frequently exists between data scientists and the executive decision makers they support, see (Maynard-Atem and Ludford 2020) and (Brady, Forde, and Chadwick 2017).
How can we address such a gap?
Brady and his co-authors argue that data translators should bridge the gap, address data hubris and decision-making biases, and find linguistic common ground. Subject-matter experts should be taught the quantitative skills to bridge the gap because, they continue, it is easier to teach quantitative theory than practical, business experience.
Before delving into the above arguments, let’s first consider from what perspective we’re reading. Both perspectives are written for business executives, Berinato writes in the Harvard Business Review, Brady and his co-authors write from MIT Sloan Management Review. According to HBR, their “readers have power, influence, and potential. They are senior business strategists who have achieved success and continue to strive for more. Independent thinkers who embrace new ideas. Rising stars who are aiming for the top” (“HBR Advertising and Sales,” n.d.). Similarly, MIT Sloan Management Review reports their audience: “37% of MIT SMR readers work in top management, while 72% confirm that MIT SMR generates a conversation with friends or colleagues” (“Print Advertising Opportunities” 2020). Further, all authors are in senior management. Berinato is senior editor. Brady and co-authors are consultants focusing on sports management. Why might it be important we know both an author’s background and their intended audience?
Perhaps it is not surprising for a senior executive to conclude that it would be easier to teach data science skills to a business expert than to teach the subject of a business or field to those already skilled in data science. Is this generally true? Might the background of a data translator depend upon the type of business or type of data science? Is it appropriate for this data translator to be an individual? Berinato argues that data science work requires a team. Might the responsibility of a data translator be shared?
Bridging the gap requires developing a common language. Senior management do not all use the same vocabulary and terms as analysts. Decision makers seek clear ways to receive complex insights. Plain language, aided by visuals, allow easier absorption of the meaning of data. Along with common language, data translators should foster better communication habits. Begin with questions, not assertions. Then, use analogies and anecdotes that resonate with decision makers. Finally, whomever fills this role, they must hone their skills, skills that include business and analytics knowledge, but also must learn to speak the truth, be constantly curious to learn, craft accessible questions and answers, keep high standards and attention to detail, be self-starters.
2.2.2 Multiple or mixed audiences
Frequently we encounter mixed audiences. Audiences are multiple, for each reader is unique. Still, readers can usefully be classified in broad categories on the basis of their proximity both to the subject matter (the content) and to the overall writing situation (the context). Primary readers are close to the situation in time and space. Uncertainty of the knowledge of a reader is like having a mixed audience, one knowing more than the other. Writing for a mixed audience is, thus, quite challenging. That challenge to write for a mixed audience is to give secondary readers information that we assume the primary readers know already while keeping the primary reader interested. The solution, conceptually, is simple: just ensure that each sentence makes an interesting statement, one that is new to all readers — even if it includes information that is new to secondary readers only. Thus, make each sentence interesting for all audiences. Let’s consider Doumont (2009)’s examples for incorporating mixed audiences. The first sentence in his example,
We worked with IR.
may not work because IR may be unfamiliar to some in the audience. One might try to fix the issue by defining the word or, in this case, the acronym:
We worked with IR. IR stands for information Resources and is a new department.
But that isn’t ideal either because those who already know the meaning aren’t given new information. It is, in fact, pedantic. The better approach is to weave additional information, like a definition, into the information that the specialist also finds interesting, like so:
We worked with the recently launched Information Resources (IR) department.
We’ll consider these within the context of effective business writing.
2.3 The utility of decisions
For data analysis to grab an audience’s attention, the communication of that analysis should answer their question, “so what?” Now that the audience knows what you’ve explained, what should come of it? What does it change?
To get to such an answer we need to think about the expected utility of the information in terms of that so what. This idea, is a more formal quantification driving towards purpose for a particular audience. We’ll just introduce a few concepts without details here as this topic is advanced, given its placement in our text’s sequencing, but it’s important for future reference to have an awareness that these concepts exist.
We can combine probability distributions of expected outcomes and the utility of those outcomes to enable rational decisions (Parmigiani 2001); (Gelman et al. 2013, chap. 9). In simple terms, this means we can decide how much each possible outcome is worth and multiply that by the probability that each outcome happens. Then, we, or our audience, can choose the “optimal” one. Slightly more formally, optimal decisions choose an action that maximizes expected utility (minimizes expected loss), where the expectation is computed using a posterior distribution.
Model choice is a special case of decision-making that uses a zero-one loss function: the loss is zero when we choose the correct model, and one otherwise. Beyond model selection, a business may use as a loss function, say, for its choice of actions that maximize expected profits arising from those actions. In more general, mathematical notation, we integrate over the product of the loss function and posterior distribution,
\[ \begin{equation} \min_a\left\{ \bar{L}(a) = \textrm{E}[L(a,\theta)]= \int{L(a,\theta)\cdot p(\theta \mid D)\;d\theta} \right\} \end{equation} \]
where \(a\) are actions, \(\theta\) are unobserved variables or parameters, and \(D\) are data. The seminal work by Neumann and Morgenstern (2004) set forth the framework for rational decision-making, and Berger (1985) is a classic textbook on the intersection of statistical decision theory and Bayesian analysis. Of note, the analyses of either-or decisions, and even sequential decisions can be fairly straight-forward. Complexity grows, however, when multiple objectives or agents are involved.
Communicating our results in terms of the utility for decisions will help us bridge the gap from analysis to audience. Again, utility is an advanced topic for its placement in this text. Just have some awareness that we can inform decisions through such a process and, to the extent these details are vague, don’t worry. Move on for now.
Note the plural. While we have identified a single person in the memo examples, discussed later, those memos may be passed to others on his team — it may have secondary audiences.↩︎