Computing start-up Plotly has produced several data visualization libraries, mostly built using Python. Their various products—from Dash to Chart Studio—are open-source and highly customizable. So, if you know how to code, Plotly streamlines the creation of graphics, charts, and dashboards. As is often true for open-source tools, Plotly has limited support documentation.
Like Plotly, D3. While, like Plotly, it requires coding knowledge, D3 offers great visual outputs. This includes diagrams and charts, product roadmaps, and much more. A core principle of D3 is that it adheres to web standards, meaning its web dashboards operate on any browser. While D3 has a steep learning curve, once mastered it offers full control over your visualizations. This means you can tweak them to interact in any way you want.
This makes it excellent for nuanced reporting. It does have a great support community though. This has led to many books and online tutorials becoming available, to help you upskill. While best-suited to scientific data visualization tasks, even a cursory understanding of D3. If nothing else, it teaches best practices in data visualization. Want to practice creating your own data visualizations in Google Sheets? Try this free, introductory tutorial to data visualization. Qlikview produces real-time, custom dashboards that offer great analytics features as well as visualizations.
Primarily a business intelligence tool, users can create interactive pie charts, tables, graphs, and more. Crucially, Qlikview integrates with other analytics tools in the Qlik ecosystem. This allows for more integrated analytics functions than some tools on the market.
Although Qlikview is aimed at developers as opposed to non-technical users this offers greater flexibility and control over its functions. This allows you to pull data from a wide range of different sources with ease. This includes relational databases, Excel spreadsheets, text files, web services, and apps such as customer relationship management systems, like SAP or SalesForce. By using in-memory databases, Qlikview performs calculations far quicker than most visualization tools.
It also offers other useful functionality, like data sharing, for team-working. As a general rule, the more user-friendly a tool is i. This problem is minimized in Tableau, one of the most popular, easy-to-use data visualization tools on the market. For a commercial product, Tableau has a great array of interactive visualizations. Unlike some commercial tools, it also handles large amounts of data well.
The trade-off is that it is not suited to in-depth tasks like exploratory data analysis. However, it retains enough functionality that data analysts can create dashboards for non-technical staff without impacting the integrity of their work. Your target audience should be able to understand whatever type of chart you create. And Datawrapper solves this issue automatically.
All the charts, tables, and maps you create with DataWrapper are easily readable on all devices. The free plan also allows you to export your chart, tables, and maps into png format. Plotly not only helps you with creating graphics, but it also provides in-depth analytical reports to help you keep track of the data.
It also comes with an easily customizable user interface. On top of that, it helps you export the reports easily and works superbly well in the business intelligence field. If you require basic features, then you can subscribe to the freemium plan.
Visually is a community platform for data visualization and infographics. It permits users to search for various images through tags and descriptions. You can also publish and embed graphics directly to your social network profiles. It works in three simple steps. The pricing depends upon the number of users. Also, the tool is open to use but for a limited duration. It supports declarative programming and helps you manipulate documents based on data.
Other than this, it provides unique functions, such as code reusability, a wide variety of curve generating functions, help in associating data to an element in the HTML pages, and more. It is best suited for those firms that need to create web pages and websites. You can also build excellent apps with the help of this data visualization tool. The Ember Charts is more like a charting library built with the Ember.
With Ember Charts, you can make graphics like time series, scatter, pie, and bar charts. Moreover, it helps you to easily extend and modify the charts since it offers great customizability options. Ember Charts is suitable for your organization if you require statistical graphs pretty often. The NVD3 allows you to build reusable charts along with chart components for d3. If you prefer neat and customizable data charts, then you might want to try out NVD3.
The Google Charts is another great data visualization tool on this list. It also gives you more control over the charts you create and will enable you to zoom the charts. FusionCharts is a JavaScript-based data visualization software that comes with an extensive charting library.
It helps you extract raw data from numerous databases and turn it into meaningful reports. Moreover, it provides over 90 inbuilt charts and more than maps to help you process the data into interactive dashboards. FusionCharts is best for organizations that need to create dashboards within their project or product. The good part is that you can get a free trial that you can use before making the final purchase.
Highcharts helps you set up JavaScript-based charts in your web pages. It makes data visualization decidedly easy by providing high customizability. Other prominent features include the ability to visualize data in eight different ways, excellent scale granularity, and more. Leaflet is an open-source JavaScript library that provides mobile-friendly, interactive maps.
All in all, this tool is designed with simplicity, performance, and usability in mind. The Leaflet is best for organizations that need specialized big data mapping solutions. It also helps organizations save plenty of time and resources and get more things done.
You can check out this article from ScienceSoft on using Microsoft Business Intelligence to drive analytics and reporting. Handling such a humongous amount of data and interpreting it is a challenge. The right tool can attack any complex dataset by their horns, and break them down into simplified steps that even a layman can understand easily.
The technique graphically depicts the relationship between data. For that, it uses elements like charts, tables, histograms, bar graphs, and maps to correlate between different sets of data. It also enables us to identify the outliers among the data. The significant part about data visualization is that it lets you use colors to separate two or more datasets visually.
Humans can identify nearly 7,, different colors , and data visualization techniques make use of this feature. You also have the option to use different shapes and sizes to distinguish trends and facilitate decision making. Basically, data visualization techniques use our inherent nature to look for outliers and hidden trends in data, even subconsciously.
Interactive data visualization tools help you visualize a huge amount of data by transforming numbers into diagrams within minutes.
Since humans interpret and understand diagrams better than numbers, these tools are externally useful. So how do you use these tools?
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