A quick search of “data visualization tools” yields hundreds of different solutions. Additional considerations, such as where the data lives, what information needs to be displayed, who can view and create the reports, and cost, all factor into choosing the “best fit” tool for a company.

Today’s executives, analysts, and stakeholders recognize the need to quickly and accurately communicate insightful information.

As the number of data sources and requests for reports from internal parties grow, analysts can easily spend the majority of their days pulling numbers instead of offering actionable information to stakeholders.

Oftentimes, analysts lose sight of the data when they have to deal with an abundance of information. Maintaining the data in a database categorically constitutes a prudent backup strategy. Investing in databases can be a good investment no matter what, for any organization. In addition, you might want to take a look at this website https://www.linode.com/docs/guides/how-to-back-up-your-postgresql-database/ to determine how you can protect your firm from data loss.

However, when it comes to data visualization tools, many of these issues are solved by the interactivity and automation they provide.

When choosing a data visualization tool, or deciding if one is needed at all, focus on these key areas to help your organization objectively evaluate different tools and find the best fit.


How many data sources? Do they all need to be available in one place?

Many companies want a data visualization tool in order to view multiple data sets and reports in a single place with a unified look and feel. Most data visualization tools allow data from multiple data sources to display on the same dashboard.

However, not all tools allow dashboard elements from two different data sources to be controlled by one single filter.

For example, if the goal is creating one report that shows how both social media and paid search perform in a certain month, it’s ideal to only have one dropdown that controls the date.

Does data need to be transformed in any way?

Data visualization tools allow for data transformation, including joining, pivoting, and custom calculations. This gives you the flexibility to create new fields, combine multiple data sources, or update misspelled values without relying on a data engineer. However, companies that do not have this tool would first require it services Boise (if that is where they are based out of) to integrate such a tool into their workflow before they can try to be independent of them. .

Most tools support custom calculations, but the number of functions available varies.

Additionally, not all tools allow data sources to join together on a common field.

How will the tool access data? Are built-in connectors available?

An important factor in choosing a data visualization tool is knowing how data will import into the tool.

Regardless of whether data lives in Google Analytics or an internal SQL database, you should confirm that the data visualization tool will connect directly to your data sources whenever possible. This allows for optimal automation, since clicking “refresh” will pull in your latest data.

Each tool differs on its available, prebuilt connectors, but almost all tools support data imported by spreadsheet.

How large are your datasets? Does your data need to be aggregated first or imported in its rawest possible form?

The size of a dataset can impact the speed and performance of the data visualization tool.

Some tools have limitations on the number of rows you can upload, so it is important to make sure data will not be truncated when you import.


Do reports need to be automated?

Reports often need to be updated frequently to allow stakeholders to make timely decisions. Many data visualization tools can connect directly to a live database or automatically refresh every hour, day, week, or some other scheduled interval.

However, some tools keep the function separate from the software used to build reports.

Do reports need to be shared out with others?

Reports often need to be viewed by many people. Data visualization tools allow your reports to be shared by a link or by logging into a portal for direct access.

However, some tools charge additional fees for each report viewer.

Do viewers need the ability to export data out of reports?

Stakeholders commonly want access to the underlying data of data visualizations, such as downloading as an Excel spreadsheet. Stakeholders might also want to know How to lock conditional formatting in Excel so the data can be visualized consistently for all the shareholders that will be viewing the data. A flowchart provides a simple and effective way of illustrating processes in a way that anybody can understand. These diagrams are often used to show relationships between causes and effects, particularly in everyday workflows. The most effective tools for presenting flowcharts are Word documents, PowerPoint presentations, and even Excel spreadsheets (a Excel Flowchart Maker, for instance). Plugins can help with this, but there is a range of approaches to this issue.

Different data visualization tools handle exporting differently, and some are easier to set up than others.

Some tools also have limited capabilities in this area based on the product’s focus on displaying data in a more visual (and less tabular) fashion.


Who needs the ability to create reports?

The complexity and ease-of-use of data visualization tools vary. The more features and capabilities, the steeper the learning curve.

If your company’s goal is for all stakeholders to connect to data, create visualizations, and draw their own conclusions, then a tool with a simpler, and perhaps fully online, interface is the best option.

However, if only a few specific groups, like analysts, are responsible for report development, then investing in a more complex tool will give them more flexibility in visualization and analysis.

Is flexibility needed to create custom chart types?

Bar charts, line charts, pie charts, and tables are basic features of every data visualization tool.

However, out-of-the-box graph types do not always provide the best view of the data, especially when dealing with multiple dimensions or variables.

Some tools allow users to upload custom chart types, and others have communities of people devoted to creating and sharing custom solutions.

What level of interactivity and filtering is desired?

Most tools offer some type of interactivity. This allows users to engage with the report and customize to what they are interested in, such as a particular date range or segment.

Some tools allow clicking on an element of a chart to filter other areas of a dashboard. For example, you might click on “West” in a sales by region bar chart to filter the daily sales trend line for just that region.

Another common data viz feature is tooltips, which appear when hovering over certain points of a chart. Tooltips work like situationally relevant mini-legends, providing additional information about data points in a chart. They are useful for showing exact numbers or definitions of different metrics. However, each tool allows different types of data points to be shown in tooltips.

Do statistical features need to be included?

More sophisticated data viz tools allow integrations with advanced statistical tools like R and Python to allow statistical scripts to run inside the data visualization tool. This opens up opportunities to add sentiment analysis, hypothesis testing or scoring models directly in the visualization tool.

Additionally, some tools have statistical features such as clustering, correlation, and forecasting already built into the tool.


How much budget is available?

One of the biggest factors involved in choosing a tool is weighing the benefits with the cost.

Answering the above questions will help a company balance budget with business requirements, maximizing return on investment in data visualization tools.

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This post is curated content from the Evolytics staff, bringing you the most interesting news in data and analysis from around the web. The Evolytics staff has proven experience and expertise in analytics strategy, tagging implementation, data engineering, and data visualization.