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How to Choose a BI Data Visualization Tool in 2024

With every passing day, your business generates new data on marketing performance, sales revenue, inventory levels, customer interactions, staffing levels, production metrics, cost, and more. Sounds overwhelming, right? It doesn’t have to be. A well-designed BI data visualization tool will turn that granular data into business information that is useful, visually compelling, and – best of all – easy to understand. 

Essentially, data visualization programs make your job easier by collecting, manipulating, and analyzing raw data – and then converting it into visual charts, tables, and figures. These insights are made with little-to-no effort on your end. 

However, there are a large number of BI data visualization tools on the market: how can you separate the wheat from the chaff and decide which tool is best for you to use in 2022? Don’t stress about it; this guide will contain everything you need to make an informed decision about which BI data visualization tool to use. Let’s get started!

10 Features for Evaluating BI Data Visualization Tools

Business intelligence data visualization solutions come in many forms: from free, browser-based versions to comprehensive platforms that integrate with mainstream business programs. There are a ton of options! So, before shopping around for BI data visualization software, determine which features are essential. You can also make a separate list of features that would be nice to have but not mandatory. Some examples of features to consider include: 

- Forms of data visualization: Graphs, patterns, trends, charts, and correlations are all various forms of visualization that you may need. 

- Mobile user support: Can the software be used on mobile devices and provide access outside of the work environment?

- Role-based access management: Can you regulate a user’s access and usage of the software and restrict irrelevant functions?

- Historical snapshots: Do you need to create snapshots of your workspace and data samples at various points in time?

- Email reporting: Would you like to receive scheduled emails that contain up-to-date data and visual statistical reports?

- Template creation: Should the software allow you to save previously used visualization report forms for future projects?

- Prompt discovery: Does the software have direct memory access to accelerate complex data analysis and deliver real-time results? 

- In-place filtering: Is there a feature that allows you to filter out certain data by type, value, or category?

- Data cleansing: Is it easy to remove incomplete or inaccurate data from your database?

- Interoperability: How easily can the solution connect to diverse file types (CSV, .xls, .txt, and so on)

10 Expert Tips for Selecting Great BI Data Visualization Tools

Now that you’ve solved the meat of the problem (determining necessary features), let’s delve into other ways that BI data visualization tools might differ. 

  1. Pay Attention to the Type of Software

The two broadest classifications of BI data visualization software are on-premise and cloud-based. On-premise is software that is installed on your computer. The software utilizes your computer’s resources and is easily customizable to suit your needs. Cloud-based solutions are hosted by a third-party, and you use the internet to access them. Typically, such solutions are available on a subscription basis, and the payoff is that they don’t take up your computing resources. 

Both on-premise and cloud-based visualization solutions have their own advantages. Cloud-based tools allow for greater business agility and cooperation between partners. On the other hand, with an on-premise solution, you usually don’t have to pay a subscription fee. Furthermore, you don’t have to worry about cloud privacy breaches when you use an on-premise data visualization tool. 

  1. Consider Who Is Going to Be Using It

Is your data team going to use the software exclusively? Or does everybody get access to it? Your answer determines how easy-to-use the data visualization tool needs to be. Some tools are designed for experts and have a harsh learning curve, whereas others have a stronger focus on being more accessible. 

  1. Assess Exploratory vs. Explanatory Graphics

Explanatory (also known as presentation) graphics can be considered as evidence – they don’t make it clear how a result was obtained, but they offer clear support for data-driven conclusions. However, exploratory graphics go beyond just presenting results; they discover patterns, test hypotheses, check assumptions, and spot anomalies. 

Thousands of exploratory graphics may be created to support an analyst’s data investigations, whereas only one explanatory graphic is needed to present those results to thousands of viewers. Thus, you should be sure that your data visualization software has plenty of exploratory options – and that they are quick and informative, rather than precise and slow. 

  1. Make Sure Form Follows Function

The data visualization you create should depend on three things: 

- The questions you are asking

- Your data properties

- How you want to communicate your data insights to viewers

Let’s say, for instance, you want to show sales growth year-over-year. That would require a different kind of visualization than highlighting discounted items’ profitability. Figure out what you need to show – and then determining HOW you want to show it will be simpler. 

  1. Tailor Your Tool to Your Viewers

One of the most crucial skills for a data scientist to have is to clearly communicate insights to different stakeholders. Because data projects are collaborative and results are typically integrated into a larger product, a data scientist’s work will be more impactful if their audience can understand the data insights and take further action. 

  1. Pick Tools That Follow Grice’s Principles of Cooperative Communication

This goes hand-in-hand with tip #5. In order to communicate data insights to stakeholders as clearly as possible, assess how well the software lets you fulfill these principles: 

- Maxim of quantity: Present information without going into overload; extraneous details can detract from key insights.  

- Maxim of quality: Present truthful, high-quality data insights. 

- Maxim of relation: Stay on topic! Don’t present irrelevant data. 

- Maxim of manner: Be clear and orderly in data insight presentation. 

  1. Choose a Tool With a Concise, Customizable, and Clear Dashboard

A car’s dashboard gives you all the information you need to know about your vehicle – mileage, gas, speed, and so on. Similarly, your data visualization tool’s dashboard should be able to give you all key information with just a glance. 

First of all, the tool’s dashboard should look visually appealing. We recommend using one with plenty of whitespace and various pops of color. If there’s too much color, the visual experience will feel overwhelming. If there’s no color, it’s hard to differentiate the various parts of the dashboard at a glance. 

However, looking good isn’t enough – the dashboard must also accurately summarize all vital data: monitored trends, KPI’s you’re tracking, and other pivotal datasets. 

Furthermore, great dashboards must be customizable. After all, at any point in time, your business may be tracking 20+ various datasets. You should be able to choose which datasets are presented on the dashboard. Different teams in your company will have different priorities, so it is incredibly useful if each team can customize their own dashboard. 

  1. Look for Embeddability

In order to truly utilize the capabilities of data visualization, you need to seamlessly integrate the tool’s visual reports within other business applications you regularly use. This is especially important when teams are collaborating across different platforms. With an ideal BI data visualization tool, the quality of the visual reports should not decline when they are transported to other applications. 

  1. Check Out Interactive Reporting Capabilities

Data visualization tools should produce very interactive visual reports – this way, they facilitate accessible investigation into insights and trends. With interactive reports, you can tell a story through data, which makes a presentation to stakeholders more effective. So, when evaluating software, assess how the predictive trends change when you adjust one variable. Data analysts must be able to move, filter, and sort variables until they find answers to their hypotheses. 

  1. How Is Data Collected?

Companies must be able to determine how to import raw data to their BI data visualization tool – as well as how to export the reports to different forms. Some datasets need to be aggregated before being fed into the tool because of their sheer size. 

Others, however, can go into the tool in their rawest form. Another thing to consider: sometimes, data needs to be collected from various sources, whereas other times, it is taken from just one source. There are some tools that don’t have the capability to collect data from several sources and display results on the dashboard. Thus, make sure that a visualization tool’s capabilities meet your data collection needs. 

5 Data Visualization Trends in 2022

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Data visualization tools are ever-evolving! We’ve been closely studying the BI industry, and we have identified upcoming trends that will likely be incorporated into visualization tools. 

1. Visualization through VR/AR: 3-D data visualizations indicate huge upcoming changes in the way we interact with data. New VR chart types are emerging, which give data analysts and stakeholders a chance to have more organic interactions with data. You can touch data with your hands as if it’s real, thus helping you make faster and more invested decisions. 

2. Self-service will evolve to self-sufficiency: With self-service data visualization tools becoming more common, fast, and intuitive dashboards and functions are imperative. When users are empowered to access data in a more intuitive manner, organizations can save significant training resources. 

3. Data Storytelling: There is a huge global interest in conversations about data – now, it’s more important than ever for visualization tools to help analysts “tell stories” about data. Millions of people are becoming more data literate, and tools will evolve to give more accessible explanatory graphics. 

4. Business-ready Data: With the COVID-19 pandemic, the need for real-time, accurate data forecasts and alerts became apparent. Plus, as the data velocity improves, the speed of businesses must follow suit. 

5. Presentation of Advanced Analytics: If a critical input data point has not happened in the past, a predictive model won’t work well. Algorithms must have good backward-looking data in order to build accurate, comprehensive future data models. In order to include a focus on outliers, BI data visualization tools need to up-level their analytic approaches. 

What to Ask a BI Tool Vendor During a Consultation

At this point, you should have a narrowed-down list of BI tool vendors, dependent on the visualization features you need. When you schedule your consultation, it can be useful to have a list of questions. Here are some that we recommend asking: 

Data Connectivity: What data sources are supported? Is OLAP/multidimensional data supported?

Data Preparation: Is user-based security supported? Which data storage techniques are supported? Will your tool augment, clean, or transform our data? Is data input and writeback enabled? Is data correction supported? How about data governance?

Data Analytics: Can data be transformed with Python or scripts? Is there a plug-in model to add new formulas? Are ragged hierarchies supported?

Data Visualization: Which advanced visualizations are available? Can we add our own visualizations? Can period-over-period be added? Is report bursting supported?

Application and Administration: Can administration be done with API? How is the application configured?

Meet Improvado: The Best BI Data Visualization Tool for 2022

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Improvado is the best service for collecting marketing data. You can connect to it any of the modern visualization tools and make the process of data analytics fast and convenient! Improvado pulls from a huge number of data sources, including Facebook, Google Analytics, Instagram, MailChimp, Pinterest, Eventbrite, Amazon Marketplace, and dozens more. Furthermore, Improvado’s data insights seamlessly integrate within Google Sheets, Excel, Power BI, Qlik, Domo, Tableau, Looker, and more options. 

Improvado makes it easy to collect visual data insights for virtually every industry, especially healthcare, education, IT, and retail. If you’re interested in learning how you can start taking advantage of Improvado’s resources with zero data engineering experience, schedule a demo with us today. 

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