Speak Loudly, Speak Visually
The power of visualization in Data Science…….
When the Vision is clear, strategy is easy!
Not long ago, the ability to create smart data visualizations, or data viz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.
Data is the primary force behind this shift. Decision making increasingly relies on data, which comes at us with such overwhelming velocity, and in such volume, that we can’t comprehend it without some layer of abstraction, such as a visual one.
Without visualization, detecting the inefficiencies hidden in the patterns and anomalies of that data would be an impossible slog. But even information that’s not statistical demands visual expression. Complex systems — business process workflows, for example, or the way customers move through a store — are hard to understand, much less fix, if you can’t first see them.
Thanks to the internet and a growing number of affordable tools, translating information into visuals is now easy (and cheap) for everyone, regardless of data skills or design skills. This is largely a positive development.
The What and Why of Data Visualization:
Data visualization means drawing graphic displays to show data. Sometimes every data point is drawn, as in a scatter plot, sometimes statistical summaries may be shown, as in a histogram. The displays are mainly descriptive, concentrating on ‘raw’ data and simple summaries. They can include displays of transformed data, sometimes based on complicated transformations. One person’s statistics may be another person’s raw data. As with other aspects of working with graphics, it would be useful to have an agreed base of concepts and terminology to build on. The main goal is to visualize data and statistics, interpreting the displays to gain information.
Data visualization is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining to check data quality and to help analysts become familiar with the structure and features of the data before them. This is a part of data analysis that is underplayed in textbooks, yet ever-present in actual investigations.
Graphics reveal data features that statistics and models may miss: unusual distributions of data, local patterns, clustering, gaps, missing values, evidence of rounding or heaping, implicit boundaries, outliers, and so on. Graphics raise questions that stimulate research and suggest ideas. It sounds easy. In fact, interpreting graphics needs experience to identify potentially interesting features and statistical nous to guard against the dangers of over interpretation. Just as graphics are useful for checking model results, models are useful for checking ideas derived from graphics.
A Picture Is Worth a Thousand Words:
Famous sayings have a way of developing a life of their own. A picture is not a substitute for a thousand words; it needs a thousand words (or more). For data visualization you need to know the context, the source of the data, how and why they were collected, whether more could be collected, the reasons for drawing the displays, and how people with the necessary background knowledge advise they might be interpreted.
The potential synergy of text and graphics can be appreciated by talking through your own graphics, explaining them to others. Why have you drawn those graphics? How have you drawn them? What can be seen? Are there interesting patterns? What could be changed and improved? Which other graphics might be drawn? How can conclusions be checked? There should be more talking about graphics and less relying on the graphics to speak for themselves.
When it comes to graphics you have not drawn yourself, the same kinds of questions are still relevant, although they may be more difficult to answer.
Presentation and Exploratory Graphics:
Presentation and exploratory graphics are quite different animals. In presenting your results, you may have space for only one graphic and no idea how many people may see it. If it appears in a newspaper or on television or the Web, your audience could be millions of people. The graphic should be well-designed and well-drawn with an effective accompanying explanatory text. On the other hand, if you are exploring data, then you need many, many graphics and they are for an audience of one: yourself. The individual graphics need not be perfect, but they should provide alternative views and additional information. Presentation graphics are used to convey known information and are often designed to attract attention. Exploratory graphics are used to find new information and should direct attention to information.
Published graphics tend to be graphics for presentation, partly because they are for publication and partly because no one wants to see hundreds of quick graphics that may or may not have been helpful. It is rather like mathematical proofs: articles contain the elegant and concise final versions, not the scribbled notes and random ideas that came before.
Exploratory graphics take advantage of how easy it is now to draw and redraw graphics. What used to be a slow and wearisome process, even including having to print out displays, has become fast and flexible. At the same time, new, additional skills are required. Identifying interesting features and knowing how to check them in more detail among a myriad of possible graphics is not just a matter of drawing many graphics, you need interpretative skills and an appreciation of which graphics will provide what kinds of information. There is so much that can be varied: the variables displayed, the types of graphics, the sizes of graphics and their aspect ratios, the colors and symbols used, the scales and limits, the ordering of categorical variables, the ordering of variables in multivariate displays. Selecting from the wide range of graphics wisely, and understanding how to gain insights, are not trivial tasks. The lack of a theory of data visualization to guide and build on is a key issue.
Rules of Thumb-
Here are some rules that one visualizer should always follow while drawing a viz.
1. Clean Your Data:
Take the time to clean your data before you try to build your visualization. Make sure only the first row contains labels, and each cell contains a single data point. This may seem like a tedious step, but it will make the rest of your analysis a smoother experience. One can use the Data Interpreter of Tableau to lighten the load.
2. Choose metrics that matter:
Choosing the right metrics to include in your dashboard is crucial. Above all, the metrics should help get to what you’re trying to learn and what you’re trying to convey. That doesn’t mean every metric should be included — far from it. You should be highly selective in choosing the metrics that earn a spot on your dashboard.
In order to find the right set of metrics to include, consider the following:
How does each metric contribute to the objective of your project or research?
Can you design a meaningful metric that measures those contributions?
Is this metric truly necessary, or does it distract from more important information?
Litmus test: Can you clearly explain how every metric on your dashboard connects to your objectives?
3. Use visual tools & add interactivity:
Dashboards are meant to be fast and easy to read. Number-based tables and spreadsheets are usually the opposite. When it comes to data, a picture really is worth a thousand words. We comprehend insights faster when data is displayed visually as graphs and charts on a dashboard.
And do add interactivity to allow other users to ask and answer their own questions. That way, your exploration can go beyond the questions you anticipated when building your dashboard.
4. Ditch PowerPoint for Tableau Story Points:
Tableau Story Points helps share the story behind your data, and makes for a great presentation tool. With Story Points, you can include interactive dashboards in your presentation in place of static graphs or images. This means you can explore data and answer questions you may not have anticipated when putting together the presentation.
If you like the design functionality of PowerPoint, try using blank dashboards as slides. You can insert images, text, and even web pages into dashboards to create a visually compelling presentation. Enter presentation mode to flip through slides just like you would with PowerPoint. And use the data to ask and answer questions that rise during the discussion.
5. Share your work and build your portfolio:
Sharing information is what dashboards are all about. And sharing your skill set with potential employers is what college is all about. Publish your dashboards to Tableau Public, then access and share them using any web browser.
Build up your profile to showcase your analytical capabilities; in today’s marketplace, it’s a critical skill for your next step, whether you’re planning for grad school or your first job.
Conclusion:
As we conclude our brief study on data visualization, it is clear that the field is rich in potential applications in diverse disciplines, at the same time we need to be aware of its practical and ethical complexities. We have discussed several examples of data visualizations, learning common pitfalls and helpful tricks along the way. As we have seen, developing an effective and ethical data visualization is a complex process.
The Future of Data Visualization:
Data visualization is entering a new era. Emerging sources of intelligence, theoretical developments and advances in multidimensional imaging are reshaping the potential value that analytics and insights can provide, with visualization playing a key role. The principles of effective data visualization won’t change. However, next gen technologies and evolving cognitive frameworks are opening new horizons, moving data visualization from art to science.
Looking back, much attention has been given to the principles of effective data visualization, such as substance, context and action-ability.
Vision without execution is just hallucination!
By: Mahima Rathod
September 2020
