Data Visualization Tools: 10 Trends For 2019

How Enterprises Are Realizing Data’s Potential

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Steven Lerner

data visualization tools

Enterprises are doing incredible things with data, thanks in part to numerous solutions. One of the leading examples includes data visualization tools, which are utilized to help users understand data by visualizing it. Although these tools have been in the enterprise for some time, there are some recent trends that are allowing organizations to gain a competitive edge. Here are 10 examples.

1. More Enterprises Are Leveraging Data Visualization Tools

Solutions involving data visualization are quickly becoming one of the most popular tools for efficiently comprehending complex datasets. In recent years, more enterprises have been jumping onto the data visualization bandwagon. The global data visualization market, which was valued at $4.51 billion in 2017, is expected to increase to $7.76 billion by 2023.

2. Data Cleansing Is A Clever Use Of These Tools

Although there are separate data cleansing tools that can be leveraged to clean up inaccurate or duplicated data, data visualization tools can also be accessed to help with some of this. The tools can help data scientists quickly spot any anomalies that might be error-ridden. Most people are visual learners, so it makes sense that these solutions can also be used as part of a strategy to ensure that the data is more accurate.

3. Integrating Artificial Intelligence/Machine Learning

Artificial intelligence (AI) and machine learning are already integral to an enterprise’s data/analytics initiatives, which include visualization. Just as AI can quickly analyze complex datasets, it can also quickly express the results in a visual model. Many tools are already leveraging machine learning to enhance data sets that are present in visualization, which is often referred to as visual analytics.

4. Increased Accessibility To Internal Data

It used to be that data scientists were essentially the only employees who received access to critical data. Today, enterprises realize the true potential of data, and are expanding access of the data to more employees. Whether someone works in operations, sales, marketing, logistics, human resources, or any department, data access is now part of those roles.

5. Highly Interactive

Gone are the days when simple charts and graphs would serve as a sufficient visual form of data. Today’s tools can present the data in more sophisticated ways, including geographic maps, scatterplots, bubble charts, and heat maps. All of these images are becoming more interactive, which allows users to easily explore the data for advanced analysis.

6. Virtual Reality Components

Virtual reality is impacting every end of the enterprise, including with data analysis. 2D visualization is being supplanted by 3D data visualization thanks to VR. Enterprises are providing employees with a VR presentation of contextual data that is accessed with VR helmets. The technology literally puts users in the middle of the data, and it allows them to connect with the data in a whole new way.

7. Blending Internal Data With External Data

Traditionally, visualization models were built with data that was collected from internal methods. Recently, organizations have integrated data from external sources, such as open data sources and marketplaces. This includes data that is from public institutions (such as governments and agencies), as well data from some private sources. The blending of data sources allows for data visualization models to be more insightful.

8. Leveraging Historical And Real-Time Data

Some of the latest data visualization tools are able to weave both historical and real-time data into models. For historical data, organizations should try to add as much of it as possible. Real-time data integration is usually achieved via a unique code that collects data and quickly categorizes it into visual models.

9. Mobile-First

With the rise of mobile workers, enterprises are developing a mobile-first strategy around data visualization. The best tools should be mobile-friendly, meaning that it should be easy to build data visualization models on mobile devices, and it should be easily accessible to view them on mobile. This important component provides a competitive edge when it comes to data.

10. Fine-Tune A Data Strategy With Visualization

Instead of relying on data visualization as means of analyzing data, leading organizations are also using it to improve overall data strategies. Real-time data capabilities and user-friendly tools provide IT leaders with immediate insights that can be leveraged to tweak data collection and storage strategies. By visualizing the data, enterprises are able to quickly fine-tune data strategies.

Are you an IT leader tasked with managing data tools and strategies? Read our exclusive report, Big Data: The Ultimate IT Guide.