Conversing using data

In my experience, working with data is primarily about helping a decision maker make his decision. Initially, the job is to understand what data is available and then figure out how to transform that data into something meaningful and relevant to the actual decision. It is easy to approach the problem from the data and get to that point that proclaims that this is the best that the data can support. The data itself is often far from relevant to the question. Enriching the data with other data types helps to fill in some gaps but there is always a leap between what the data supports and what the decision maker actually needs.

In one job, I was quite content for a few months just pushing the data through processes that resulted in a presentable form. The presentation was adequate for someone to show to an audience and then point out the key points that need attention. The presentation would also expose data that can solicit audience questions and the presenter would be able to drill into that data point and answer the question.

It was that second point that came as a revelation to me at the time. I originally assumed that the presenter would present his case and just need supporting information for his case. I assumed including the other data would give context to add credibility to the data that we were focusing on. I am aware of the art of argumentation, I just assumed that data was outside of rhetoric. I quickly learned my mistake.

Decision makers operate in the rhetorical arts. Arguments backed by data are an essential part of his arsenal, but the data then becomes subject to cross-examination. Providing additional information in a presentation helps to give credibility to the primary data, but that additional information is then exposed for questions. The audience may simply ask what about that other data point.

In my most recent job, I remember making the same mistake when I was first starting off. My task was to answer some question. The resulting presentation included the backup material to support the primary conclusion. That backup material included other data to provide context. When I made the presentation, the audience picked up something I didn’t even bother looking at. The busiest day in terms of workflow occurred on a day when everyone should have been off. The point is that the audience accepted my primary conclusions until they saw an opportunity to ask what about that day. I probably could have saved myself had I had more time to develop the tool to allow me to dive into that one particular day to get a reasonable explanation for what was happening. Unfortunately, I was left speechless and this opened the floor to all sorts of theories, most of them I was certain were wrong.

When this happened, I should have known better. I already knew that a presentation fails when I need to leave a promise to get back to everyone with an answer later. This is a rhetorical defeat. The time to win the argument is the time when you have the floor.

Perhaps modern education is different, but in my education and formal mentoring, I was always told to focus on the data and not on the argument. I specifically recall starting college with a freshman required course in Rhetoric, but the general sentiment in the engineering school is that such a course was one that should not be a requirement. I didn’t take the course seriously. I don’t remember my grade, but I assume it was barely above passing. Later in college, I began to appreciate rhetoric more, but I continued to think it was largely irrelevant to engineering. In engineering course work, the answer was either the one right answer or it was one of the countless possibilities of being wrong.

Rhetoric opened the possibility of even the right answer being vulnerable to a counter argument. All it needs is a single data point that begs for a reasonable explanation. The presenter’s inability to quickly provide a plausible answer undermines his primary case even though the question concerned an irrelevant data point.

The key point in rhetoric is that the questions need answers during the same session when the presenter has the floor. I do not think I have worked under anyone trained as a lawyer, but I imagine many have self-trained at least in the skills of presenting a case in a very specific window of time. The skilled ones would never do what I did. They would never voluntarily present any piece of information that is not specifically relevant to their case. They know that any extraneous information gives the opponent the opportunity to challenge that information. The presenter strives to be confident in explaining anything he brings up.

Modern data practices use a lot of data and from a lot of different sources. Given this abundance, the presentations are able to show a lot of different metrics that ultimately can not fit on a single page or even a single presentation. The abundance of information challenges the normal rhetorical approach. It is inevitable that some information will be exposed that will give the audience the opportunity to ask a question the presenter can not answer. This is especially true when the data is updating in real time, or at least has been updated after the last time that the presenter reviewed the data.

From the start of my data career, last minute surprises happened to me so frequently that I assumed it would be inevitable. In the early phase, I had to give a lot of demonstrations to sell the concept to different groups. Instead of doing the smart thing and make a static report with snapshots of data I could explain in the slide notes, I would use the tool live and talk through it. It was almost a certainty that by the time I got to the third screen something would come up that was surprising and yet unrelated to the main point I was making. In those cases, I managed to win the day by exploring the anomaly and then coming up with a plausible explanation in the meeting. I rarely had to promise to get back to them with a follow up analysis.

This ability to be able to recover from a diversionary question is essential part of rhetoric. Going back to the court case analogy of avoiding the introduction of any information that is superfluous to the case, there is the counter benefit that cases with more data have a strong argument than cases with less data. There is a need to include as much data as possible and yet still exclude the data that is not relevant. I think the key determinant is the presenter’s confidence that he can defend any information he offers. The admirable skill is where the presenter able to answer every question asked and still remain within the rules or the ethics.

That said, I highly doubt any lawyer would use a live dashboard to present evidence to a court case. That is a completely different level of confidence that the decision maker has to master. Decisions need to consider the most recent information, and that would include information that arrived after the last rehearsal or review. In the presence of any controversy or objection to a proposal, the proposal would fail if a question can be raised that cannot be immediately answered. There is also an element of procrastination involved in making very difficult questions. The presenter’s goal is to make the decision immediately. That requires him to be prepared to answer any questions during the presentation itself.

Ultimately there is a time limit on decision making. For the presenter, the constraint is the opportunity he has to make his case. For the audience, the constraint is some external pressure to make some decision.

I enjoy working with data for the joy of just seeing how I can combine or massage the data to make presentations that are relevant to some topic. The abundance of data technologies across the full process makes it almost like playing some game. I can spend endless time applying different technologies or approaches even when working with the same data. I would explore opportunities to link one kind of data to a different type of data. I would explore different ways to arrange the data to make some process run faster. I would try different visualizations to see if I can make subtle signals more obvious.

Despite that, if I would probably be bored with a job that only asked me to do this kind of work even if they provided me unlimited data and resources. The real appeal of my work is the rhetorical aspects. There must a specific target meeting or decision deadline. When the time comes, there is a presentation of some proposal backed by data. That presentation is open to questions. Even in this context, there are different styles where some would insist on delaying questions until after the presentation is completed. The more powerful presenters will welcome questions in the middle of the presentation and he would have confidence of answering the tangential questions while still being able to finish the presentation.

While data is essential to an argument, the effectiveness of the presentation is actually a rhetorical skill. The ultimate goal is to persuade the audience. The audience certainly needs good data. Ultimately, they need the assurance of a confident presenter. The presenter needs to earn that confidence in real time during the meeting. He does so by being able to field any question about anything that shows up. Even more powerful is his invitation for interruptions that may challenge his message.

I won’t claim to have that kind of courage, especially for really big questions, but I have had a number of times when I managed to do this. I would allow someone to ask a question and that question clearly surprised me, but I managed to explore the data in real time to find an explanation that satisfied the questioner. The presentation was a success. I think a large part of that success was the rhetorical element of confidently attempting to answer a surprising question and then actually coming up with a plausible answer backed by data I hadn’t seen prior to the question being asked.

I enjoyed my jobs because of that pressure. To be clear, my role was never a very high level position. I mostly dealt with mid-level type questions in terms of confidence. I fantasized about being in a higher level position, and at some points I had the opportunity to join into an advancement path in that direction. I declined.

There was an element of self-harm in this joy. I did enjoy the thrill of getting data to cooperate so that I can present some result in time for some scheduled meeting. I enjoyed the thrill of watching the time run out with still a lot more work to do, and yet I was confident I could complete it in time. My confidence was correct. The problem was the pace becomes relentless. There is always another question that needs to be answered. The questions started to overlap. I was trying to answer multiple questions at the same time, and often succeeding.

As successful as I may have been, I lacked a crucial skill of how to do this without ruining my health. I knew I needed breaks and I took them even though it would mean even more pressure when I returned. The skill I lacked was the ability to decline new tasks, or to cancel previously agreed upon commitments for a higher priority task.

The sudden appearance of a high priority task does not lessen the priority of the earlier tasks. Soon the high priority task will be completed and I will be left with the remaining tasks that will then rise to top priority again. There is no easy way to curb the flow of tasks.

That flow of tasks was a consequence of the approach I took to my work. That approach is to turn a data task into a data conversation. A data task could have been a static presentation with a short question and answer period at the very end of the presentation. A data conversation is open the data for conversation from the very start of the presentation and still keeping the presentation on pace to reach its conclusion within the given time window.

The problem with this approach is that conversation is addictive. Experiencing a good conversation provides motivation to seek out more conversations. This is true for both the presenter and the audience. The audience demands more conversations in the form of asking new questions. The presenter agrees to enter the conversation.

This conversation involves a lot of data instead of just words. Normal rhetoric involves a lot of preparatory work, but at least most of that is research using human language. Modern rhetoric with data involves using the language of data instead of human language. It is doable, but it is unnatural.

Evolution has not had enough time to develop the ability the argue in data instead of human words. The better skilled at this are also least likely to leave offspring who might allow evolution to prepare future generations to be better at handling this kind of challenge.

I do equate the activity to be rhetorical rather than merely technical. There is conversation and debate. The skills to make these successful are rhetorical skills. I think it is powerfully addictive to engage in this level of conversation.

As I age, my respect for rhetoric has grown, certain far beyond the dismissive attitude I had when I started college. There is a lot of work preparing an argument. That work is hard and the initial assignments have low consequence or engagement. The fault of the rhetorical courses was to not addict the early students to the process. Perhaps it is an inevitable barrier. In order to have a truly addictive experience, you need to present something you put a lot of work into. That initial work offered only boring tedium with no hint at the experience that will come later when the argument is successful.

I discovered this by accident much later in life. I enjoyed the preparatory work of arranging data into a presentable form. The surprise was when I was then put in a position to present this to a critical audience. I accidentally experience the ability to handle surprising questions. I was addicted. The addiction was to the conversation. The conversation happened to involve the language of data, a vast collection of individual datum.

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