After a few years of working in biomedical research and a philosophy degree from college, I know a few things about writing and thinking systematically. Unfortunately, I see a lot of people stumbling in their writing when they try to create complex abstract or technical materials– writing is tough, and accurate, succinct, detailed, and logical writing is even harder.
To me, systematic writing is a method of writing which seeks to transmute the complex relationships between raw or parsed data into a coherent, readable narrative that can be effectively understood and analyzed by someone who is generally knowledgeable on the topic, but who didn’t gather or prepare the data. Systematic writing is part of a greater family of writing that includes scientific writing, technical writing, and financial writing, along with other types I probably haven’t even thought of.
While this definition may seem overly abstract, I’d like to point out that most of our received and sent communications are not systematic; a news anchor is not relaying systematically prepared information to the public, even though the reporters have gone through the trouble of parsing raw data (events that happened) into a narrative (what the anchor says). The quantity of technical detail and data referencing in a news report is slim, as news reports are designed for a very wide audience who have little previous context for the event that happened (the data). An email we send to a colleague referencing data or analysis is not necessarily systematic writing, as it’s entirely possible for a certain context to be inferred between two people; systematic writing provides its own context and content explicitly to the audience.
Systematic writing is typically intended for a small, already-savvy audience, and should only offer the minimal viable context. A reader with general knowledge on the topic of the piece should be able to acquaint himself with a systematically written piece in short order, but a layman should not, because establishing the amount of context required for a layman would involve a lot of background information which falls outside of the scope of a particular instance of systematic writing. We don’t want our systematic writing to sprawl, because systematic writing is intensely purposeful and detail-heavy writing, and lots of background information and tangents dilute the factual details we’re trying to communicate.
So, the title promises 11.5 bites describing the process of writing systematically, and without further ado here’s a primer on how to write and think systematically:
- Define your goal. What kind of narrative do you want to make, and what data are you planning on using? Who is going to read the report, and how much context will be required?
- Put on your white thinking hat. To use the terminology of the fantastic thought guide Six Thinking Hats, the white thinking hat is purely unbiased and factual thinking used for establishing a common ground among readers. If you’re going to be writing a systematic document which refers to data, you need to make sure that you don’t take any liberties with the data without explicitly qualifying them as speculation or partially supported. No spin!
- Assemble your data. You can’t write systematically without having data. Ensure that your data is collated/parsed/charted in a non-deceptive and easy to understand way– the only person you’re trying to inform at this step is yourself, so it behooves you to be honest about the quality of your data and what knowledge we can actually extract in analysis. If there are computations or manipulations required of your data, now is the time to do them.
- Determine the limits of what your data can tell you. Soon, we’ll analyze our data, but first, we need to vaccinate ourselves against narrative mistakes. Though it seems simple, it’s easy to slip up and attribute facts to your data that aren’t actually there. Explicitly state the variables which your data depicts (sales, months). Remember that going forward, all of your statements should be in terms of the variables which you outline here. If you’re not talking about information within the purview the data that your variables describe, you’re not being systematic.
- Extract verbal information from your data. Write down simple statements to these effects, such as, “the data for November showed 42 sales.” If you computed averages or other values in your data assembly step, now is the time to introduce it as a simple phrase. If you expect that handling the data in this way will be confusing, document your process simply and clearly so that your audience will understand. Do not introduce any explanation at this point, merely state what the data say, and, if necessary, state how the data were processed. Remember not to speculate, the point of this step is to establish purely factual statements.
- Analyze your data at a basic level. Now that you have a series of simple statements depicting your data in an unbiased way, comparisons between data statements can begin. Are the sales from November higher than the sales from October? Write that comparison down if it’s relevant to your originally stated goal, and make sure to directly reference the values in your new synthesis statements. The point of this step is to explicitly state simple relationships of the data, independent of any narrative.
- Analyze your data deeply. Stay focused on your original goal during this step. What questions can your impartial data statements answer explicitly? Implicitly? What trends in your data are noteworthy? What points of data are outliers? Can you explain the outliers? In this step, writing more complex statements is necessary. “The sales data from November (42 sales) are higher than October (30 sales), following the upward trend of the fall season. These data tell us that the fall season is our strongest selling period, despite the high sales in December.” Don’t try to speculate or hypothesize about “why” yet, just tease out the more complex relationships in your data, and write them down in a clear way. As always, reference your data directly in order to build context for your audience and keep them on the same page. Don’t worry about over-analyzing at this point, we’ll prune our findings later.
- Ask Why. Why did we see the data that we saw in our analysis? What are the general principles governing our data? Address each piece of relevant data with this question, and ensure to answer it briefly. The outliers that were previously identified need special attention at this point. Keep explanations of your data concise and factual, though remember that your explanations are not actually within your data set, so you should draw in outside proof to support your explanations if necessary. It’s okay to hypothesize if you don’t know exactly why certain data turned out the way that they did, but be sure to explicitly label speculation.
- Build a narrative using your data, analyses, and explanation. Consider your starting goal, and how to marshal the data, analyses, and explanations in order to accomplish that goal. Your narrative should proceed first with the data, then with a simple factual explanation of the data, then with a more complex analysis of the data, and finish off with an explanation of the data if it’s required. The narrative step of systematic writing is where you put all of the pieces together and put it into one attractive package for your audience. Don’t neglect graceful segways between different portions of the data set. The final product of this step can be considered a first draft of your systematic writing effort, and may take the form of a PowerPoint presentation, meeting agenda, technical report, or formal paper.
- Anticipate questions and comments from your audience. Look for areas in which your explanation, analysis, or data prompt a response, and plan accordingly. Questions regarding your narrative are typically the easiest to address by clarifying what you’ve already written explaining why your data appears the way it does. Questions regarding your analysis can get a bit technical depending on the audience, and so you should be prepared to refer back to the source data in your responses. Questions regarding the data itself or the parsing of the data are the most difficult; typically, the outliers will be under the most scrutiny, and their data quality may be called into question. I find that it helps to get out in front of questions regarding outliers, addressing them to your audience before taking questions.
- Prune non-critical information. This is the step where most of the data-statements and analysis statements meet their demise. Which analyses, explanations, and narrative elements aren’t strictly serving your original goal? Remove extraneous information to create a hardened product. Ensure that the relevant context and core data analysis remains, and don’t build a misleading narrative by omitting contradictory relevant data.
The final half-step is, of course, crossing the t’s and dotting the i’s for your final draft– and make sure it’s perfect! A missed detail on something not mission-critical will still distract your audience from your data and analysis.
I hope that my readers have a better idea of how to write and perhaps think systematically after reading this piece. I think that many non-technical people struggle with systematic writing because of how data-centric it is; communicating in the style of referencing data and withholding speculation can be quite difficult for people accustomed to relating written concepts intuitively and emotionally.
If you have any questions, leave em’ in the comments and I’ll respond. I know that the 21st century will have the highest demand yet for systematic thinkers and writers, so I’m also considering forming a consultancy in order to help organizations with training their employees and executives to think and communicate in systematic ways, so expect more on topics like this in the future.
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