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Data Visualization Techniques and Tools for Various Data

Apr 05, 2020 4 min read

Good data visualization can answer all of the important questions, great data visualization answers the questions you did not know you had.

Think about it, weather forecast, medical equipment, vehicles, house utilities, etc. all improved over the years and data visualization techniques are what contributed to more positive user experience.

However, let us narrow down the focus to data visualization methods in companies, which are mainly used in status reports. If done correctly the key information is successfully delivered, and it makes the whole decision-making process far less painful.

Different Data Visualization Techniques

Some of the most common data visualization methods or techniques are:

  • Tables
  • Charts
  • Maps
  • Graphs
  • Infographics
  • Dashboards/Reports

Clearly, these are not always interchangeable and the type of data, as well as the type of audience, determines which of the data visualization steps will be implemented.

For tracking performance, expenses, ROI, user engagement and other important business-related metrics people typically use charts, infographics, graphs, and reports as data visualization methods.

That being said, maps can also be used to display user engagement in different geographies, if a company operates on a global scale for example.

create reports

Goals and Audience

The data visualization approach mainly depends on your goals and audience. If you want to present data in an engaging way and ensure that viewers stick around, you will probably go with an infographic.

Whereas, if your audience is a board of busy people with a lot to discuss, you will go with something much easier to digest like pie or bar chart. In other words, always know your audience prior to deciding which of the data visualization techniques you are going to use.

Your goals, on the other hand, dictate the elements that will be included or KPIs which you report. Also, your goals will influence the decision on which colors to use. If you are creating a graph that details carbon emission you will likely use colors that show the state is alarming like red for example and different shades of red for comparison.

Furthermore, you should be consistent with the selected color scheme and utilize contrasting colors when displaying different or opposing elements (typically positive values are green and negative red).


Within a corporate world among all of the data visualization techniques charts seem to be the most dominant ones. However, there are different sub-categories of charts and each of these has a specific use.

  • Line charts - One of the most visually appealing ways to demonstrate change or impact over multiple periods of time. Furthermore, lines allow you to add multiple elements and compare their performance within a specific time frame.
  • Bar charts - Very similar to line charts these are used to compare different elements and their performance over time.
  • Number charts - Whenever specific numbers play a major role in KIPs like profit and cost centers, site visits, impressions, CTP, etc. then always go with number charts and percentages.
  • Pie charts - These are easy to follow when comparing multiple metrics, assets and their performance. If you need to illustrate proportional comparisons this is a way to go. That being said, you should avoid using them to display important numerical values.
  • Gauge charts - A great way to represent a single data point and how it relates to other performance indicators. For example, you can show the expanse ration and how it correlates to turn around profit, or how much you rely on outsourcing to meet production demand, etc. 

    create reports

Big Data Visualisation Techniques

When it comes to representing big data, there are a few key aspects you need to have in mind. May also help you create a decision tree analysis

  • We live in the age of information, yet not all sources are reliable. You need to figure out what relevant and available dates can be trusted in order to incorporate it into the display.
  • Make sure that your audience is aware of how data is aggregated and where it comes. This should not be a central focus of your data visualization, but still, it should be visible to the viewers.
  • The key takeaways should be at the top, as these provide the most valuable insights.
  • When creating the display you can go the extra mile and make it interactive. If people can filter out some of the components and see how they affect the results, you can create a whole new level of the learning experience.

In general, your displayed elements should have a hierarchy, and there should always be a breakdown of derived results, and on how data was obtained. Appealing design is as worth as its credibility, never forget that. 

Tools for Data Visualization     

Designers rely on a few different tools when they a company's presentation, white paper, product road map and so on. Typically they use photoshop, or adobe illustrator, or similar tools.

However, these design tools are only useful if you already have the data and just want to present it in a more digestible way.

There are specific tools for data visualization nowadays and anyone can use them regardless of whether they are a designer or not.

Whatagraph for example, allows you to build multi-source reports, and you don’t have to do it from scratch. Below is an example of a Facebook account report.

facebook ads data visualization

The platform boasts a variety of integration options with most of the major analytic tools, so if you are already using some of these you can easily generate aesthetically pleasing reports.

In case, the software you are using is not supported, just upload the relevant CSV/Excel files, and you’ll be good to go. Best of all you can white-label those reports, so it will align with the established brand.       

Data science and data analytics are growing at an astronomical rate and businesses use them to sift through the goldmine of data and help them make better-informed decisions.
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