Answers without data are just opinions. But, even if you have an ample amount of data but fail to present its value or meaning, you’ll never be able to convince someone to take any action or decision.
Thus enters the art of data presentation – the art of revealing the story (basically, storytelling) behind data – irrespective of its quantity. How effective you are with presenting the data directly affects how impressive and valuable the audience finds the same data.
The term “data presentation” may seem something of the 20th and 21st century. But the reality is, cave paintings and inscriptions on rocks were all “visual tools”, used by our ancestors, thousands of years ago. The only variation is that we now have better tools that enable us to present enormous volumes of data (big data) with just a few clicks on the button.
Thanks to technology, especially our beloved smartphones, the average attention span of humans is just 8 seconds. The number is expected to fall, and the fact that it took just a few years to drop from 12 seconds to 8 seconds seems to be intriguing and horrifying at the same time.
Data presentation is all about the aha moment. It’s making the most out of that 8 seconds of undivided attention. It’s about translating data into digestible nuggets of information.
If you’re eager to master the art of data presentation, then this article is a perfect starting point. If you are considering a career in big data analytics, the need to know how to create and present data-driven presentations become even more crucial. Let’s scan the entire process in detail.
Why Is Data Presentation Important?
If you’re stuck at wondering “what is data presentation?”, it is the use of various graphical techniques to visually present data and for narrating a compelling story behind it to the audience, in order to emphasise their importance.
To understand the importance of data presentation, imagine what you would be doing if not for presentation tools. You would probably have to present the data sitting in thousands of rows on a spreadsheet.
Now, imagine being an audience member and being presented with a spreadsheet containing endless rows of data. That would be the most boring thing you ever encountered.
The raw data sets you collect have no meaning unless you process it into something meaningful using various types of analytics. None of the audience will be interested in the raw data sets, but their meaning and interpretation – hence data presentation.
Humans are visual creatures. We tend to look at the spot that shines the brightest or has the highest contrast. Our visual memory is far superior to auditory recall – meaning, we’re more likely to remember things that we see than we hear.
By using vivid graphs and other visual representation techniques, you can grab the attention of the audience (and sustain it) and explain the results, meaning, or importance of the data collected and analysed.
Types of Data That Require Presentation
1. Numerical Data
They are measurable data such as time, amount, weight, and height, recorded in the form of digits. Although numerical data has meaning on its own, it’s often coupled with textual data.
2. Textual Data
Textual data are either written for a specific purpose or transcribed from speech. They are materials that include written, printed, or electronically published words.
3. Pictorial Data
Pictorial data, also known as pictographs or image data, consist of data presented in the form of images. Depending on the object of which the image is presented, it can be raw or processed.
4. Spatial Data
Also known as geospatial data, spatial data refers to the geographic information about a physical object which can be represented using numbers, such as places, monuments, or planets.
5. Map Data (Or Maps)
Maps are used to show the geographical boundaries, along with other attributes such as the demographics, topography, climate, and pollution levels.
Apart from the types of data mentioned earlier, there are several others such as encrypted data, symbols, and combinations of multiple types.
What Are the Methods of Data Presentation?
There are mainly three different methods of data presentation – Text, Table, and Graph. The data presentation methods are determined by the data format in question. We’ll discuss that in detail shortly. Before that, let’s look at how the three methods vary.
1. Text Presentation
Test presentation is used to offer contextual information (for example, the relevance of a survey in a presentation) or as an explanation of results and trends. They are generally written language in the form of sentences or paragraphs that can be used to emphasise or interpret data.
If the data to be presented is minimal, and the use of a table or graph would unnecessarily take up space, then text presentation is a good choice. Here’s an example of how Steve Jobs uses text presentation.
In this presentation, only a few numbers are presented, and so, going for text presentation makes more sense. However, if you plan to include the download or purchase data of more than ten months or so, going for a table or graph would be ideal.
2. Table Presentation
Information presented in the form of rows and columns has been around for centuries. If you’re confused about how tables and graphs differ, think of the accuracy of both. Something as accurate as “5.632541” can be presented on a table, and not on a chart.
Tables are ideal for presenting multiple types of information together and for comparing the differences of certain variables. Unlike graphs, interpretation of data may take more time with tables. Also, tables are inefficient when it comes to studying trends. However, you can use heatmaps to increase the scannability of tables.
3. Graph Presentation
A presentation aid which is a pictorial representation of statistical data is called a graph. While tables are ideal for presenting accurate information, charts are beneficial to present complex information in a simplified and visually appealing manner.
Charts are essentially images that are capable of presenting large volumes of data and emphasising their trends or patterns. There are various types of graphs, ideal for different purposes.
For big data presentation, graphs can be a better choice as otherwise, tables or texts may require lots of screen space to present as there will be humongous volumes of data. Here are some of the most common formats of graph presentation.
i. Scatter Plot
A scatter plot graph presents data using the x and y axes and is ideal for uncovering the relationship between two variables. A point in the scatter plot will represent the association between two variables and patterns can be analysed by studying multiple points.
ii. Line Chart
Line charts (or graphs) are the most basic type of graphical representation and are beneficial for studying data, known as time-series data, collected over a period of time. Rise (or fall) in unemployment rates and the increase in demand for a product over the months are examples.
They are also useful to demonstrate the growth of multiple data sets over time or distance. By doing so, you can compare and analyse the patterns across those data sets.
iii. Bar Graph
Bar graphs are one of the most extensively used graph types. They are ideal for comparing multiple attributes, for example, the increase (or decrease) of revenue contribution from multiple sources over a stipulated time.
They can be created both horizontally and vertically, and the height of the bar represents the level of information. Multiple bars can also be stacked together to form stacked bar graphs.
iv. Pie Chart
Pie charts are used to represent data classified into multiple categories and together forms a 100%. For example, pie charts can be used to describe the population of different states of a country.
v. Combo Chart
Combo charts are combinations of two or more types of charts (or graphs). These charts are ideal if you want to save screen space and also to compare and analyse the patterns across multiple data sets.
If you’re confused about which graph to choose, here’s a flowchart that lets you decide with ease.
How to Choose the Right Data Presentation Type?
The goal of your presentation is to convey the information and convince the audience, and so, more thought must be put into choosing the right presentation method. Choosing the right method of data presentation is dependent on,
- Data format
- Method of analysis
- The information you intend to emphasise
Wrongly chosen presentation methods can nullify the importance of data and of course, your efforts. Even if you’re talking about the same set of data, you may have to use different data presentation techniques depending on the information you wish to emphasise.
Data Presentation Examples
The Good Ones
The Bad Ones
What Are the Tips for Better Data Presentation?
1. Label Components Clearly
Even if you worked on a particular graph or chart for days, your audience would be exposed to it for only a couple of seconds. And you have to make the best use of those seconds and ensure that your audience clearly understands each component.
Also, refrain from using abbreviations – especially the jargons. The key is to make your presentation scannable, understandable, and memorable for 100% of your audience.
2. Don’t Scare Your Audience With Numbers
Too many digits and decimals can be overwhelming for the viewers. Even though data analysts love numbers, the audience may not. Here are some tips to make the numbers more presentable.
- Remove decimals, if the difference between numbers doesn’t depend on them.
- Use commas for any number beyond a thousand. (100000000 is scary; 100,000,000 is approachable).
- Align your numbers to a specific side (preferably right).
3. Make It Easy on the Eyes
Your presentation might be clear and readable on your laptop screen. But the projector may have a different story to tell. Instead of being too conscious about saving the screen real estate, try increasing the size of elements so that they offer a comfortable viewing experience.
4. The Presentation Must Have a Specific Purpose
Your data sets will have a specific story to tell, and your presentation will be the medium. Suppose you’ve measured the ROI of a group of marketing campaigns and want to showcase why a particular campaign outshined the others.
If so, it is highly recommended you cut straight to the point without explaining something like how other campaigns could be perfected. To make sure you always craft purpose-driven presentations, remember the title of your presentation and the reason why the audience shared with you their valuable time to listen.
5. Refrain From Mixing Different Chart Types, Unless Unavoidable
Generally, the reason for introducing a particular chart is to tell the audience a story about a data set. If you want to show the correlation between two variables, for instance, the relationship between click-through rate (CTR) and visits, then here’s a bad and a good (better) example for it.
However, here’s an example of fitting two series into the same chart.
The point here is that mixing charts is something to be refrained from, as the audience gets to see it for only a couple of seconds and so, it can be more confusing than informative.
But if mixing two series is integral to present your findings, then go for it, but make sure you creatively address the confusion aspect.
6. Be Thoughtful While Using 3-D Graphs
3-D graphs look amazing on the screen, especially when it’s a pie chart. But at times, especially with bar graphs, the information conveyed won’t be accurate. Here’s an example.
7. Pies Can be Harmful
If you have three or four, or at max five categories to represent, then a pie chart is an excellent data visualization technique. But for anything more than that, pie charts can be confusing to the viewer.
Pitfalls to Avoid While Presenting Data
1. Presenting Irrelevant Data
If a particular data set has nothing to do with your research question, then it is highly advised you remove it – even if you did spend some time gathering it. Irrelevant data will confuse the viewers and distract them from the critical ones.
2. Presenting Too Much Information at Once
The problem with info overload is that it does more harm than good. The excess of information will confuse the audience and force them to miss the primary intention behind your presentation.
The key is to break down your presentation into digestible chunks, free of clutter and irrelevant information that doesn’t complement the big picture. Instead of dumping too much information, you must give the audience the chance to think for themselves.
3. Using Acronyms, Jargons, and Abbreviations Thoughtlessly
Jargons and acronyms are cool – if the listeners are aware of them. However, there will always be some who aren’t accustomed to the majority of jargons you use.
Remember, most of your data presentations would be presenting your findings (which is of a technical nature) to a non-technical audience. Even if it costs you a couple of seconds more, try using exact and simple words.
4. Using Footnotes to Increase Credibility
Of course, footnotes are essential to back your research and its “correctness”. But they can occupy a significant part of your slides and make it overwhelming for the viewers. Instead, try sending out the sources to the ones who request them or dedicate the final slide for the same.
5. Relying Solely on Colour to Show Differences
Around 1 in 200 women and 1 in 12 men are colourblind. This means, if you solely rely on colours without numbers, at least a few of your audience won’t understand your intention. Even black and white printers are “colourblind”, and so, anyone printing your presentation will have a hard time.
It’s Aha vs Nah Moment
Data presentation is all about inciting the “Aha moment”. If you fail to do so, it becomes the “Nah moment”. If you desire to become a data scientist or a data storyteller (or planning to take up jobs in data analytics), knowing how to develop and deliver data-driven presentations will be a significant plus.
Undoubtedly, the different methods of data presentation discussed in this article will help you convey significant amounts of information to the audience without losing their interest. Always remember – you just have 8 seconds to convey “the information”.