Logo

THINK BIG

Analytics

Best Data Visualization software of 2020

#1

Klipfolio

48%
#2

Tableau Desktop

48%
#3

Looker

48%
#4

Databox

48%
#5

Mode

47%
#6

Visme

47%
#7

Chartio

47%
#8

Grow

46%
#9

Qlik Sense

46%
#10

Google Charts

45%

Related to Data Visualization Software

Data Visualization Software Trends

Data visualization software is specifically made to help turn your company’s raw data into insightful and visually appealing charts.

Data visualization spans everything from basic bar charts for a report to live dashboards that are constantly and automatically updated as new data comes in.

The right data visualization software can make it easy to quickly and effectively communicate the insights from your data, even to people who aren’t data analysis or business intelligence specialists.

Why use data visualization software?

If you’ve been in business long enough, you’ve seen some bad charts. Haphazard, poorly designed charts can hide, obscure, or even misrepresent the story told by your data.

The rise of data analytics and business intelligence are supposed to pave the way for making better decisions using the insights from your data, but if these insights aren’t communicated the right way, your company could be on its way to making terrible decisions.

Working with rudimentary charts, like those available in Microsoft Excel or Google Sheets, just doesn’t cut it—you’re deeply limited in how much data you can show, and in what formats you can present your results. Data visualization software can vastly expand the range of tools you can use to analyze and interpret your data, and can interface with much larger and more varied datasets.

Data visualization software can help you make sense of huge datasets. Many companies are overwhelmed by the amount of customer and sales data they collect; even as it piles up in data warehouses they aren’t sure how to make sense of it.

Data visualization software can interface directly with your databases, pulling data through automated SQL queries to help you visualize and analyze data that would otherwise be unmanageable. One of the biggest strengths of data visualization software is that it often does not require strong database management skills to use on a regular basis.

True, you will need to enlist the help of your IT team to plug in your data visualization software to your databases when you set everything up, but once your data visualization system is connected, you don’t need to be manually writing data queries day after day.

With data visualization software, it’s easy to make live dashboards and interactive visualizations. One of the most exciting capabilities of modern data visualization software is creating dynamic charts that automatically update when new information becomes available.

Using this feature, you can create dashboards for your sales team, marketing team, or other users so they can immediately know how effective the latest sales or marketing campaigns are. Dashboards can also be made interactive, allowing a user to drill down into specific areas (for example, examining trends in sales over time of a specific product), or pull up different sources of data for the same plot.

These interactive dashboards can help users ask their own questions from your data, helping them draw their own insights using their expertise combined with the data visualization dashboard that you give them.

A case study on Seven Eleven Japan from Stanford’s business school gives an example of how interactive dashboards combined with subject matter expertise can improve a business (1): By providing local store managers with up to date dashboards on item sales, each local manager could monitor inventory and trends, using their best judgement to restock or order new items based both on their experience with the local market and the data available on their interactive data visualization dashboard.

Data visualization software can make it easy to iterate or replicate a certain style of chart. Any good data visualization software will enable you to make templates or use themes to apply a universal design scheme (color palette, fonts, and so on) across all of your plots and charts.

These templates and themes make it far easier to maintain a consistent brand across all of the various domains in which you might be preparing your data visualizations. Basic infographics can draw from the same color palette and font arrangements as your sophisticated interactive dashboards.

Likewise, you can reuse the same general template, swapping out different data sources to make the same style of plot for many different applications. This can be a huge time saver compared to having to remake the same plot from scratch every single time you need a new version.

Who uses data visualization software?

Since data visualization software makes it so easy to draw insights from your company’s data, a wide range of your team members will end up using it. The specific needs will vary from job to job:

Data analyst. People in this position will be putting together charts, plots, and dashboards on a daily basis. They’ll be the ones who work with your data visualization software on a regular basis, so they’ll likely end up being the most well-versed in using it.

Data analysts will work in close communication with team members that need data visuals, whether that’s sales, marketing, or the CIO.

Information Technology Specialist. Your IT team won’t be doing much with your data visualization software once it’s up and running, but they’re critical for the initial set-up.

When data visualization software is installed in your system, you need to provide it with the back-end access to your company’s databases. Information Technology Specialists will know how to do this, and they’ll be the first ones to call if something isn’t right with the data that’s getting fed into your data visualization software.

While most users won’t have to bother with database queries, this hinges on the database connections being set up correctly, and that’s what your IT team is equipped to do.

Business intelligence. Your business intelligence team will be primarily interested in using data visualizations to make better business decisions. They’ll typically have a very specific question to ask, like whether more spending on marketing drives more sales, or which of your products is selling the best.

Business intelligence often requires the latest information, so your business intelligence analysts will want to look specifically at using live dashboards and other automatically updating data visualization tools.

Data scientist. Data science is different from business intelligence and data analysis in that it is more focused on asking new questions of your company’s data, or exploring and uncovering new relationships in data that were previously unknown.

Your data scientists may prefer to use their own data visualization tools, which are integrated into their data science software tools, but may call upon your data visualization software for presenting final results from their analysis.

They will be most likely to take advantage of features not as readily available in off-the-shelf data science packages, like animations, live updating graphs, and customizable interactive dashboards.

Sales team lead. At a modern business, the sales team relies heavily on key performance indicators, or KPIs, to keep them oriented towards success.

Whether it’s number of leads pursued, number of contracts signed, or value of products sold, a sales team lead needs to keep a close eye on his or her team’s KPIs. So, your sales team may spend less time making data visualizations but will spend a lot of time looking at them.

For these team members, a dashboard with KPIs that updates every day (or more often) is a huge help for increasing revenue and efficiency.

Social media director. Social media is data-driven: impressions, interactions, likes, and shares are the key performance indicators for a social media campaign. Being able to track and compare these over time is a vital part of the job of a successful social media director.

While many social media software tools have integrated analytics data, you can often set up a data pipeline from your social media analytics to your data visualization software, possibly using a SQL database as an intermediary.

Data visualization software can also help your social media director take a step back and look at longer-term trends in social media usage and engagement among your potential customers.

Marketing director. Much like your social media director, your marketing director will turn to your data visualization software to track the success of different marketing campaigns via their key performance indicators.

Unlike social media, where success is easy to measure using metrics like impressions and clicks, marketing has a more diverse range of performance metrics, so you’ll either have to work with your marketing director to choose the right data to visualize, or get your marketing director trained in on the more sophisticated features of your data visualization software so he or she can set up exactly the right visualizations that are needed.

Chief information officer. Most CIOs are too busy to be setting up sophisticated data visualizations in their free time, but they’ll be using your data all the time to make decisions, so you’d bet that they are going to be at least passingly familiar with the basics of your data visualization software.

CIOs often have specific questions that can be answered visually, and because they report on their findings to the company as a whole, you should go the extra mile to make sure the data visualizations used by your CIO are perfect. This might require some advanced configuration in your data visualization software, but it will definitely be worth it.

Features

Look for the ability to set themes or use templates to achieve a consistent brand image with your data visualizations. From a design perspective, one feature that defines a high quality data visualization tool is its ability to let you make charts and plots that aren’t obvious default-theme creations.

Adding your brand logo, color palette, and fonts to your data visualizations helps create a consistent brand image, as well as making it easier to interleave data visualizations into company reports, emails, and infographics.

These themes and templates can be saved, making reuse or generating a new version of a chart with updated data a cinch.

Live-streaming data dashboards are a feature you’ll find on most high-end data visualization tools. Applications like Tableau, Plotly, and other high-end enterprise-oriented data visualization software let you build graphs and dashboards that aren’t just static images.

You can plug them in to live data streams, like the number of products sold in a particular week. These live dashboards are incredibly useful for people who need the most up to date information when making decisions.

If keeping costs low is a priority, several good data visualization tools are free and open-source. Moreso than with other business software, it’s very possible to make professional-level data visualizations at no cost. Plotting libraries for R and Python, for example, can make exceptionally good visualizations, and with add-ons like Shiny, you can even make interactive dashboards.

The drawback is the expertise level needed: it requires far less technical skill and programming abilities to make a dashboard in something like Tableau, so though open-source tools are lower in cost than proprietary tools, they do have a much steeper learning curve, so it may take longer for your team to get up to speed with these data visualization tools.

Top-end data visualization tools are increasingly turning to interactive plots and charts as a primary area of focus. Interactive charts offer many more layers of depth for the same amount of screen space. By clicking on a bar in a bar chart, for example, you can get more detailed information on that specific datapoint, or by selecting a specific key performance indicator from a drop-down menu, you can quickly compare trends across different metrics.

This is a huge advantage compared to traditional static charts, where you’d have to publish pages and pages of nearly identical charts to provide the same information. If concise and dense information presentation is important to you, definitely look for interactivity in your data visualization software.

You can even embed these interactive tools on your website, to allow internal or external users to check up on their dashboard from anywhere in the world.

FAQ

Q: Is Excel a data visualization tool?

A: Technically yes, you can use Excel for data visualization in the simplest sense, but it lacks just about every powerful feature you’ll find in a modern data visualization tool. It’s possible to connect Excel to large databases, but it can be a real pain.

Additionally, the types of plots available to you in Excel are a sliver compared to what’s available in software like Tableau, Sisense, Visio, and other specialty software.

Customization is also a slow and arduous process in Excel compared to the ease with which you can apply themes and templates in other data visualization software. Knowing Excel already is an often-used excuse to avoid learning modern data visualization, so be sure you don’t fall into that trap.

Q: What software offers interactive data visualization?

A: Several of the top data visualization software tools offer interactivity, like Tableau, Plotly, Visio, and Chartio, to name just a few.

These interactive elements take dashboards and charts to the next level, allowing you to increase the density of information with no increase in screen clutter. Data science tools like R and Python also offer interactivity, though these require a lot more technical skill up front to implement.

Q: How do you get into data visualization?

A: Most people who specialize in data visualization are data analysts, business intelligence analysts, or data scientists. All of these career paths involve varying degrees of training in statistics, data analysis, and programming.

Gaining good data visualization skills requires some education in design, too, because understanding elements like color palettes, proper scaling, and how people interpret different types of plots. Finally, any good data visualization specialist will have expertise with more than one type of data visualization software.

Q: What are data visualization techniques?

A: Data visualization techniques can be broadly sorted into two categories: exploratory and explanatory. In the initial phases of data analysis, you may spend much of your time trying to understand the structure of your underlying data.

Here, you might find yourself using box plots, scatter plots, and histograms to examine the underlying structure of your data. But once you have a result to present, you need to transition to using exploratory data visualization techniques.

These are plots that are easily interpreted by people who are not data analysis experts, and require a different set of design characteristics to make sure the data are visualized in a way that accurately represents the underlying relationship.

Q: Can you use data visualization tools for big data?

A: Yes, many of the top data visualization tools have recently added compatibility with tools like Hadoop and Spark, which are big data specific tools.

You may need to rethink some of your data visualization practices when working with big data, though: you can’t be sure you’ll be able to plot all your data in one chart anymore, because there’s just too much of it. Instead, you may need to resort to plotting samples of your data, or averages across your large dataset.

While you will need to adapt some of your traditional data visualization techniques, big data can uncover relationships and patterns you wouldn’t have seen in smaller datasets.

Q: What free open source tools are there for data visualization?

A: By far the most popular open source tools for data visualization are R and Python, which are both programming languages that have become very popular for data science and machine learning.

Both offer great data visualization abilities, and can even be extended to produce interactive reports and dashboards. The drawback is that, since they are still programming languages, you need a lot more technical skill to use these open source tools. Fortunately, there are lots of great resources for learning both R and Python, also available at no cost.

Q: Can you use Python for data visualization?

A: Yes, Python is a very popular tool for data visualization, especially in the data science and machine learning communities. Toolkits like Matplotlib, Bokeh, and Seaborn are particularly useful for making professional quality data visualizations. However, as is the case with R, using these tools requires strong coding abilities, so the learning curve can be quite steep.

Q: Can data visualization software use structured database software?

A: Yes, in fact a defining feature of a data visualization tool is its ability to interface with structured databases built using various types of SQL.

While even Microsoft Excel supports SQL integration, higher-end data visualization tools enable easier and more abstract use of structured databases.

You won’t have to be writing SQL queries by hand, for example, once you’ve connected your data visualization software to your structured database.

Q: What are the best free data visualization software packages?

A: When it comes to free data visualization software, it’s hard to beat libraries for R and Python like ggplot, Bokeh, Shiny, and Seaborn.

These libraries produce fantastic visualizations, and can even be used for interactive web pages with data visualizations built in. However, there’s one drawback: they are much more difficult to learn up front, especially if you aren’t a programmer. So, while you’ll save on money, you do need to invest the time to learn how to use these tools.

Q: What data visualization software is HIPAA compliant?

A: HIPAA compliance has more to do with your data storage than your data visualization: usually, you’ll need to make sure that the structured database that you are storing data on is compliant with HIPAA (which protects the privacy of medical records).

If that’s the case, you should not have any additional issues with compliance if you connect your data visualization software to that database, though you should always check with your business’s HIPAA compliance experts to be sure. Different specific applications may require access to different levels of protected data.

Recap

If you want to create powerful and visually appealing charts, plots, and interactive dashboards, specialized data visualization software is the way to go.

A good data visualization software package can help you make data visualizations that present the latest data in an accurate and easily understandable way, ultimately leading to better business decisions.