Some of you know that I am a big fan of mystery fiction.
There are many aspects of this genre that I enjoy, but what I find the most interesting is watching the detective look for clues to help identify the problem, investigate alternatives, and finally offer the solution. In past articles I’ve noted some of my favorite detectives --Louise Penny’s Armand Gamache, Philip Kerr’s Bernie Gunther, Michael Connelly’s Harry Bosch and Tana French’s various detectives, to name just a few. They are all flawed, but amazingly talented at solving the fundamental problem of “who done it.” What is it that enables them to put seemingly disparate puzzle pieces together to solve the case? Characteristics that are also needed to succeed at doing business analysis work.
In my past articles I have drawn comparisons between the BA and detective. I have explored the need for both to connect the dots and solve problems creatively. I have discussed the need to rely not only on their intuition, but also their rational mind. I have discussed the importance of recognizing patterns, creating structure from chaos, and feeling comfortable with ambiguity. However, one comparison I haven’t explored is perhaps the most obvious and relevant one—the ability to ask good questions.
As BAs we ask a lot of questions. As Penny’s Chief Inspector Gamache says, “The question that haunted every investigation was ‘why,’” also an important question for all BAs to ask in one form or another. But Gamache knew, as do BAS, that asking ‘why’ by itself is not enough. We need to ask contextual questions. Consider the quote from Dashiell Hammett’s famous detective Sam Spade: “Who shot him,” Spade asks a witness ... The witness “scratched the back of his neck and said, someone with a gun.” Experienced BAs know that when we ask vague questions, we’ll get vague answers.
Not only do we need to ask good questions, but we need to be able to understand the answers provided. What happens when we want to ask follow-up questions, but are absolutely “clueless” about what the stakeholder is saying? This can be particularly unnerving when that stakeholder uses highly technical language such as a data scientist describing the algorithms that will be used in the latest AI effort. Like our detectives, we need to clarify. And if we don’t understand, we need to admit so. And if that technical guru asks us questions that make no sense to us (what ETLs need to be developed, for example), we need to admit that we don’t know. One of the tips Chief Inspector Gamache gives each new recruit, is to get clarification when needed. He says that one of the most important things “that leads to wisdom” is saying “I don’t know.” We need to have the courage to say those words modified, of course, for our own situation.
Of course it gets trickier in the digital world. As BAs we cannot simply say, “I don’t know what you’re talking about,” or words to that effect. We can try the old standby, “Help me understand…” which is great, but we run the risk of still not understanding the explanation. What do we do when we don’t have experience even vaguely related to the stakeholder’s answer? As BAs we often find ourselves asking questions about all manner of things unfamiliar to us, but the world of AI can present new and unforeseen challenges. Yes, we can—and need to-- prepare questions in advance. But how can we ask good questions, not just the fundamentals like “why” and “what,” when we know nothing about the subject?
For example, let’s say I want to ask about algorithms, a subject that I know almost nothing about and therefore am terrified of. Sure, I do research, but when confronted with an answer that makes no sense to me, I might freeze. I want to ask about why one type of algorithm was used instead of another. I want to ask about built-in biases. But answers like “I chose a non-parametric algorithm which uses this method for classification and regression...” might give me pause. What helps me is to go back to the basics and start asking contextual questions, which provide a business context and can set the tone for other questions and answers. Once I establish a business perspective, I can put all my further questions into a business context as well. And importantly, it helps me say “I don’t know” without actually saying it. Remember those ETLS? We can rephrase our answer into a question about what the alternatives are and how each alternative provides the business what they’re looking for.
We can also start out at a high-level discussion about the AI effort, what business problem it solves, and how it aligns with the organization’s strategic direction. Even if we’ve heard the answers from sponsors and other business stakeholders, we can encourage technical gurus to frame their answers in business terms. Once we have established the business context, we can move to more detailed questions about how a chosen algorithm helps the business, risks of built-in biases, and, as needed, ask about the data, its source, when it was cleansed, and so forth. Or start with a lower level of detail and work upwards. As good detectives, BAs know that solutions rarely occur when we try to investigate in a straight line. Detectives and BAs know that a question that leads to unexpected answers is a source of a myriad additional questions that take us in unexpected directions, but ultimately solve the problem more quickly.
And that’s where some of the skills discussed in previous articles come in. These competencies. like the ability to connect the dots, help us solve problems and come up with creative solutions. These competencies allow us to follow unusual lines of questions, even if we have no idea what the outcome will be. They allow us to prioritize our work and the questions to ask each stakeholder. They allow us to uncover implicit and hidden requirements. And they enable us to make creative yet practical recommendations. In other words, they help us find “clues” that may seem meaningless at first, but which ultimately help us solve even the most difficult business problems.