Data Visualization in Business Intelligence: Problems, Libraries, and Tools to Integrate, Free Data Visualization Tools

Tech companies aren’t the only ones concerned about data analysis – any kind of business is. Analyzing business information to facilitate data-driven decision-making is what we call business intelligence or BI. Simply put, BI is a set of tools and methods for extracting raw data from its source, transforming it, loading it into a unified storage system, and presenting it to the user.

Everything that happens before the actual data is presented in a visual form can be considered as the underlying infrastructure, what’s called a data pipeline. The major part of the pipeline is the Extract, Transform, Load (ETL) process and its tooling. You can think of it as the backend of any BI system. Then the front end of the system is the user interface, where data is finally presented to a user in visual form.

How is data visualized in BI?

As we’ve mentioned, a data representation tool is just the user interface of the whole Business Intelligence system. Before it can be used for creating visuals, the data goes through a long process. This is basically a description of how BI works, so we’ll break it down into the stages shortly:

1. First things first, you should define data sources and data types that will be used. Then transformation methods and database qualities are determined.
2. Following that, the data is sourced from its initial storages, for example, Google Analytics, ERP, CRM, or SCM system.
3. Using API channels, the data is moved to a staging area where it is transformed. Transformation assumes data cleaning, mapping, and standardizing to a unified format.
4. Further, cleaned data can be moved into storage: a usual database or data warehouse. To make it possible for the tools to read data, the original base language of datasets can also be rewritten.
5. So, now you can see where data visualization actually takes place in the whole process. Most modern BI interfaces have a wide number of options concerning the choice of how to use data for visuals. In most cases, there is a command dashboard with a drag-and-drop interface that allows you to:
6. Connect the data source to the system via API (or custom integration)

• Choose the dataset to work with
• Choose the type of visualization
• Place multiple visuals on the dashboard
• Create interactive elements to manipulate the data
• Modify visuals as the data updates
• Type information manually
• Save reports
• Share reports

 

Common types of data visualizations

Turning information into an image is not an objective. On a higher level, the data is easier to grasp when it’s represented as a visual: That’s why we explain things like quarters or percent as pieces of the pie.

But, on a lower level, a visual is a tool to convey the connections between different units. As Depeche Mode sang in their song “Everything Counts”: “The graph on the wall tells the story of it all.” And, that’s correct – visuals are used to tell the story through data interpretation. Thanks, Depeche Mode!

Each type of visual corresponds precisely to the idea of what data it can interpret, and what type of connection (relationship, comparison, composition, or distribution) it shows better. Let’s look at the most common types of visualizations you encounter in business intelligence and data analytics in general.

Bar chart

A bar chart is one of the basic ways to compare data units to each other. Because of its simple graphic form, a bar chart is often used in BI as an interactive page element.

Bar charts are versatile enough to be modified and show more complex data models. The bars can be structured in clusters or be stacked, to depict distribution across market segments, or subcategories of items. The same goes for horizontal bar charts, fitting more for long data labels to be placed on the bars.

When to use: comparing objects, numeric information. Use horizontal charts to fit long data labels. Place stacks in bars to break each object into segments for a more detailed comparison.

Pie chart

One more common type of chart we see everywhere, is a pie chart.

This type of chart is used in any marketing or sales department, because it makes it easy to demonstrate the composition of objects or unit-to-unit comparison.

When to use: composition of an object, comparing parts to the whole object.

Line Graph

This type of visual utilizes a horizontal axis and a vertical axis to depict the value of a unit over time.

Line graphs can also be combined with bar charts to represent data from multiple dimensions.

When to use: object value on the timeline, depicting tendencies in behavior over time.

Box plot

At first glance, a box plot looks pretty complicated. But if we look closer at the example, it becomes evident that it depicts quarters in a horizontal fashion.

Data visualization tools and libraries

There are a lot of products in the data visualization market. In choosing a vendor, look for a scalable BI service that will fit your business size and analytical needs. While the capabilities are pretty similar, the ecosystem each vendor is offering may or may not include important services. In this section, we’ll cover provider options as well as tooling to create your own visualization software.

Also, keep in mind: Because of the complex analytical techniques, underlying infrastructure, and explicit functionality, most tools are paid or freemium. Desktop applications themselves are really heavyweights (Tableau 1.6 GB, Sisense about 5 GB). Without up-to-the-minute hardware to operate them, you risk frying your computer.

Popular data visualization tools

In most cases, tooling for data visualization is a desktop application represented by a command dashboard. The integration with data sources is done via API, so datasets will be uploaded automatically. After you point out where your data is stored, the application will provide you with a canvas for the future report where you can place visualizations and fill them with data.

Each visualization template has its settings with data properties and correlation types. So, combining different types of visuals, you will be able to construct the report. Depending on the functionality, reports can be further exported as CSV files or shared within the system with other users.

As is the case with any technology market, data visualization providers offer similar functionality, so we will talk more about separate features rather than explaining how it works. For a more detailed overview of tooling, read our article dedicated to BI instruments.

Looker.

A data analytics platform offering connection both to web-based and SQL data sources. Looker features:

• Drag & drop user interface
• Customizable dashboards
• Exportable reports
• API integration with third-party sources
• Data querying from database
• Cross-platform access

Zoho Analytics.

This business intelligence product comes as a part of the Zoho analytical platform. On the solutions page, you can find segmentation of Zoho services by department to provide valuable targeted insights. Zoho features:

• Drag & drop user interface
• Web application
• Integration with many data sources (Google products by default)
• SQL data query and API support
• Report embedding
• Shareable access to analytics

Tableau.

This provider has the whole ecosystem for business intelligence with various products. Tableau offers not only a data visualization tool itself, but also data preparation instrument for transforming, cleaning, and mapping information without code knowledge. Tableau features:

• Drag & drop user interface
• Wide list of native integrations with data sources
• Shareable access to analytics
• Report embedding
• Cross-platform access
• Server REST API
• Constantly updated data flow
• Metadata management instrument
• Inbuilt dashboard commenting and highlighting

QlikView.

Qlik is another BI provider that offers a lot of flexibility to teams willing to build customized software. QlikView is a freemium version of their product, which can be upgraded to Qlik Sense. Qlik features:

• Customizable reports
• Role & Permission-based access system
• Open API
• Open database access
• Cross-platform access
• Shareable access to analytics

Each of the products mentioned offers demo access or a trial period for their tool, as well as scalable products for businesses of different sizes. If you are about to try out data visualization and it’s your toe in the water in BI, we recommend you start with free tools.

Free data visualization tools

There are not many totally free options on the market. One way or another, there is some paid functionality or product(s) to upgrade to. That said, here are some vendors you can try out to understand how to deal with data visualization:

Microsoft Power BI.

Not a rare case for Microsoft to offer its software for free, but there are some pitfalls. You can obtain full functionality without paying a dollar, but all the produced reports will be published to the Microsoft Gallery. So basically, all your reports will be publicly visible. Power BI features:

• Drag & drop interface
• Desktop application
• Wide list of native integrations with data sources
• Customizable reports
• Incremental data updates
• Full BI ecosystem as a service in Power BI Pro

Tableau Public.

The same goes for Tableau Public – shared, full functionality with all your data published on the public service. We won’t copy the features here, but this has to be on the list of free options.

Google Data Studio.

Perhaps, this one is the most accessible option for any user that wants to create visualized reports. Data Studio features:

• Web application
• Drag & drop interface
• Inbuilt integration with Google Analytics and other products from the Google Marketing Platform
• Customizable reports
• Data transformation instruments
• Shareable access to analytics

If you’re going to use your technical background and make use of the open-source resources and libraries, the next section will list a few.

Libraries, packages, and open-source tools for data visualization

There are also separate tools that can be used to construct a certain type of visuals. Most of them require programming language knowledge (and sometimes frameworks as well).

D3.JS is a free JavaScript library for creating visuals by connecting data with the document object model via API, manipulating documents as objects.

Dygraphs is a free, open-source JavaScript library that works for building dataviz in browser. It is suitable for use with huge datasets for building interactive charts and graphs. For starters, check API reference and data format documentation.

Chartist.js is another tool based on JavaScript used to build graphs and charts by styling them with CSS.

Gleam is a Python package for building scatter plot visualization with the help of CSS and HTML.

Leather is a Python library for creating charts in their simplest form and saving them as SVG files to be shared further.

Matplotlib is another open-source Python library aimed at creating 2D visualizations.

All the mentioned libraries and tools can be freely used and integrated with the existing software to create new types of data visuals as templates.

Pitfalls of data visualization

If you decide to introduce visuals to your analytics, you have to learn a bit about their potential snags. Using the tools is not a big deal. Generally, even ecosystem-sized products are pretty intuitive to use.

But if you’re a manager or responsible for implementing BI in your organization, be aware of some consequences before the actual data will be available to visualize. Here’s a breakdown of common issues in data analytics by the problem’s domain.

Challenges concerning data preparation

The main pitfall at the dataset preparation stage concerns making the right assumptions. As during any process in software development, defining the required data and feasible sources is based on assumption. The same goes for deciding whether you need a data warehouse and if you want to transform data into multiple formats.

A simple solution to it can be found through testing at all stages of data processing. In terms of data visualization, we have to test those assumptions that will directly impact the visualization process. These items are:

• Initial data types
• Chosen sources
• Types of data sourcing (querying, constant updating, ad hoc reporting)
• The architecture of your database/data warehouse

All these structural elements can be tested by a domain expert in that field — an ETL developer – while assumptions can be discussed with data engineers/data analysts.

Challenges concerning visualization process

The visualization stage is somewhat less technology-driven. While there are semi-AI-driven business intelligence tools, the user is still the one who decides what format of visualization will be placed on a canvas, and what will be the data properties. So that suggests:

Pitfall 1:

Using the wrong visualization format. It’s very easy to get lost in the forest of graphs, charts, and maps, so it will take some time to study the required minimum of dataviz for your business. For example, using a spider chart when the object has only one characteristic to compare will make everyone scratch their heads. And vice versa: A line graph applied to compare multi-dimensional units, like seasonal sales across 3 countries, each with 10 provinces, is doomed to failure.

Pitfall 2:

Using the wrong type of data. A very similar issue, but it takes a couple of times to understand what type of data can be applied to your tried-and-tested DataViz.

Pitfall 3:

Dataviz tools don’t generate reports, you do. It might be surprising, but, at the current state, only a few really expensive tools can interpret some part of the information for you.

Pitfall 4:

Wrong tooling choice. Well, if you choose to use a free tool or decide to mess with a library, perhaps it can’t be wrong for you. But when we talk about the choice of vendor, things get more serious. Vendors of data visualization offer the whole service to make your life easier as the one who will use the reports. The main point here is to understand whether the service is scalable, so it covers the amount of data you have and the frequency of its updates. Visualization capabilities should also be considered, because industry-specific analytics may include exotic forms of DataViz.

 

Considering all the opportunities and blockers, you are free to implement visual analytics. The most important stage of data processing comes before you enter the user interface. With that in mind, you’re good to go.