How can you incorporate data and research to write about complex topics?

One thing many disregard or rather hide, while publishing their research work, is negative data. Negative data or insignificant data is also as useful as the significant one's. It helps identify research gaps, formulate modified hypothesis, and provide insights into relevancy of the particular topic.

Minimize Jargon: Readers would easily give up if they had to look up each and every word of your writing. Therefore, minimising technical and specialised terminology and making the right use of analogies would make it more engaging Use Graphics: One of the best ways to make your data is by incorporating visuals like graphs, charts and infographics. Make sure the visuals are clear, labelled appropriately and easy to interpret. Be selective about the data: Too much data could lead to confusion and dilute the overall message conveyed. Share only what is necessary, and focus only on the most relevant and impactful points. Include a summary section: This helps distil complex information into digestible, actionable points.

It's easy to make assumptions about your audience, especially if you operate in a complex or niche industry. You might assume that everyone consuming your content speaks the same industry "language" (uses the same jargon / has a clear knowledge of the concepts etc.) but that's often not the case. Before you start on any piece of Thought Leadership content, especially longer-form pieces e.g. Industry reports / whitepapers) then you need to understand your audience's existing level of understanding. It's about getting the balance right between universal understanding and dumbing down.

The art of incorporating data and research information when writing about a complex subject is really quite simple. Keep the text as it is but use graphics e.g. clipart and/or icons to highlight the data. Make sure that the graphics relate to the data and research and make it relatable that is don't just use any old graphic e.g. if you have data about a change of income use a chameleon or research about how to aim high use a rocket-ship. Try and instill some humour. Remember the saying "a picture paints a thousand words".

Incorporating data into complex topics starts with understanding your audience. Know their pain points and what resonates with them to select the right data. Ask yourself: Will comparison data clarify my point, or would an infographic be more impactful? Tailoring data presentation to your audience's preferences amplifies your message. For instance, if your audience struggles with a specific issue, pinpoint data that speaks directly to that challenge. By doing this, not only do you strengthen your arguments, but you also make your content more relatable and easier to digest.

When incorporating data into your content, the credibility of your sources is paramount. Using sources that lack credibility not only weakens your individual arguments but could undermine your standing as a thought leader. Always opt for high-quality, reputable sources like peer-reviewed journals, established industry reports, or government statistics. Double-check the data's relevance to ensure it aligns with your audience's concerns and your objectives. In essence, the credibility of your information directly influences the credibility of your overall message, so choose your sources wisely.

When writing about scientific research data, if you do not have time or training to dig deeply into and analyze dozens of individual studies or reports and then, crucially, also have time to distill the conclusions of such research into approachable content for your intended audience, consider focusing on references to high-quality, big-picture reports like systematic reviews and meta-analyses.

As a long-time journalist, understanding the quality and reliability of my sources was a vital part of the content creation process. I'd argue that a majority of available data and research comes with an "agenda", even if that agenda isn't malign and it's just a researcher's natural bias. That's why it's important to source information from more than one channel (no matter how reputable) and look for areas where your second source supports the first. Ask relevant questions, like "Who's sponsored this research?" "Are there commercial interests involved?" "Who benefits from this conclusion of the study?"

I often find one thing missing when folx search and integrate sources into their work: systemic context. Contextualizing sources is not the same as assessing bias or conflicts of interest. I look for systemic context by immersing myself in information and then, pulling back from individual references, I sit with what I know and how I am biased. Finally, I look to see who has the power not just in today's work, but throughout time. Who was included in decision making and data design? When were excluded communities brought in? Has there been enough time for marginalized persons to assess the state of knowledge and speak out against unidentified operations of oppression? What could I be missing as a cishet assumed white guy?

Data is the base of crafting a solid argument in your research. It is the culminating point upon which the authenticity of your research rests. Using prism of multiple sources to get to a top-notch data research, you much be open to different ways of gathering it. Furthermore, using a third-party source to authenticate your literature review after incorporating the data is also a credible element in your research leading to a more justified and strong argument.

No one wants to read your content as though it's an academic paper. However, it's easy to fall into that trap. You can publish the best researched piece of content but if it doesn't say anything new or interesting then it won't resonate. That's why it's important to start with a unique perspective in mind, and then keep asking yourself: "What's my point of view on this?" "What do I agree with, or disagree with?" Your research may challenge and evolve your original perspective, and it's important to be open to that and not just look for data that backs up your original hypothesis.

What makes a research paper look authentic? Claims that you made based on the wide range of data supported by different case studies, previous/ongoing research, historiography, news, articles, debates on the same topic - unlimited things you just name it. Relying on only few deep researched database won't make a significant impact on your research and won't attract more readers than using multiple, authentic facts/statistics/arguments/recommendations that you put in your research.

No one wants to read lengthy text full of facts and figures. The engaging way to catch your audience and give them an insightful read is by using multiple visualizations like infographics, maps, tabulation etc. If your article is going to publish online then you can also embed a video or podcast to expand your audience who are unable to read through it. I'm pretty sure there is a genius pool of people who can contribute to your research by listening. Than why only relying on full boring texts?

It is often the case that, pinned by the complexity of the challenge, we overstate the data research and understate the story that the data points fit into. Storytelling is the method for presenting data I have the best results with. Data points are gems in a crown that the story builds. The audience will listen and actively participate, driven by the story and drawn by the gems. And as the best crowns have some gems in the front, some in the back, and many all around, we should make sure the story can be followed from more angles than one.

Contextualization is key - you risk to lose your audience otherwise. Remember, the attention span of people is rather limited, even in a professional environment, and people are driving by what matters to them, so make that clear. A great tool to do so is storytelling. It can be very helpful when trying to make raw data or dry research relatable.

Do your best to attempt to explain what the data says to an 8 year old. It's easy to explain things in a complicated, jargon filled manner. It's just not helpful to most.

Literary theft is something a notable research never wanna do. You being a credible researcher with a huge data-driven research wants to make an impact through your strong arguments and backing it through fast facts an figures should always cite back your references. This is an authentic way of crafting the ideas you borrow from someone else's research and crediting them in your research to show your bona fide attitude towards literacy.

Something that often happens in research is a citation chain that often turns into a game of "telephone". This article cites that article which cited this other article. When citing work, a lost practice is following the chain to the original citation. Those that cite the original can of course be cited, but it becomes a nightmare for the reader/audience to try and find the original data when you cite the end of the reference chain.

One thing thag might be interesting for this article is that no matter how complex the topic is, you should try to reach everyone and be understood. Some professors know to say that if you present your work in kindergarten and children understand what are you talking about, then are you a good presenter. Regards