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We refer to the underpinning architecture as a data pipeline. It encompasses everything that takes place before the actual data is visibly shown.
The pipeline's key parts are the Extract, Transform, and Load process and accompanying tooling. It can be viewed as the foundation of any BI system. The data is visually shown on the user interface. There are many unanswered questions regarding data visualization and the methods and tools that can be employed to accomplish this objective.
In this post, data visualization will be discussed from the perspective of business intelligence.
Data visualization is a potent tool for successfully and efficiently conveying a company's message to a large audience.
Organizations must rely on widespread data transmission in today's cutthroat business environment. Finding actionable insights through Inferential Statistical Analysis is one method to accomplish that.
In a situation when new solutions are needed to quickly and easily graphically visualize data, traditional data visualization techniques have lost their attraction. Pie charts and bar graphs sometimes show underlying patterns or other important insights. An enormous benefit in this field is sophisticated data visualization.
The significance of sophisticated data visualization for business intelligence will be covered in this essay.
What is Data Visualization?
The process of converting unformatted data (text, numbers, and symbols) into a visual representation is known as data visualization.
Data visualization has a specialized function. It helps to spot patterns, tendencies, and logical links between the various parts. Visualization can be done in any format, depending on the data's type and nature.
Every analytical report includes data interpretations, such as pie charts, comparison bars, and demographic maps, as is clear by looking at them.
Usually, similar software is used to create the visuals. However, analytics is where this program is most frequently used.
Users can show information using data visualization, also known as DataViz, through the BI interface (data representation instrument).
What is Data Visualization in BI?
As we have already mentioned, a data representation tool is only the user interface for the entire business intelligence system.
The data must go through several steps before it can be used to create visuals. This is a basic description of how BI works. We'll break it down into stages soon:
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First, define the data sources and types you will use. Next, you will need to determine the database qualities and transformation methods.
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The data is then sourced from the initial storage such as Google Analytics, ERP, or CRM.
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API channels allow data to be moved to a staging location where it can be transformed. Data transformation involves cleaning, mapping, and standardizing to a unified format.
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Cleansed data can be transferred to storage, such as a database or data warehouse. Rewriting the original language used to create data can make it easier for tools to read them.
Now you can see where data visualization takes place throughout the process. Modern BI interfaces offer many options for how data can be used to create visuals.
Most cases have a command dashboard that you can drag and drop to allow you to:
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Use API (or custom integration) to connect the data source to your system
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Select the dataset you wish to use
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Select the type of visualization you want
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Multiple visuals can be placed on the dashboard
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To manipulate data, create interactive elements
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Modify visuals when data is updated
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Enter information manually
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Reports to save
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Reports on shares
After selecting the source, the user can operate in the built-in sandbox with visual templates. These templates can be used as a single depiction or to fill in the information.
Visualization is not automatic, but it is still feasible to draw pictures by hand. You can utilise the templates that come with any BI interface.
These templates can be changed and edited by adjusting the necessary data parameters. Visuals may automatically modify graphs and tables in response to data changes. To do this, data visualization libraries are utilized.
Below, this subject is discussed in more detail.
Difference Between Business Intelligence and Data visualization
To show data and identify patterns within it, data visualization is utilized. Numerous formats, such as line graphs, pie charts, pivot tables, and line graphs, are possible.
Business users have limited access to metrics thanks to the technology that makes up business intelligence. In layperson's terms, describe what occurred and list the causes. A Business Intelligence solution might produce a visualization.
Data visualization and business intelligence will be covered in this topic.
The key differences
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BI is a way to get to the bottom of information in the business world. It also allows you to use that information to analyze data.
On the other hand, data visualization tells the story using the data. It provides the parameters to help you understand it.
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Data visualization is the conversion of raw data into insights in a graphical format. It is an integral part of many business intelligence tools.
BI is the analysis of data from sources to help make decisions and implement growth-oriented strategies.
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Two types of data visualization are used in Business Intelligence. Data visualization allows you to visualize the data and interact with the software.
It deals with structured data and focuses on reporting. BI uses historical data to predict future events and view Business Data.
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Business Intelligence is a process that collects data from many sources and transforms it into a database. This creates a pattern-rich dataset.
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Business Intelligence is a front-end dashboard that transforms data into a visual interface and algorithms at the back end.
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Experts agree that BI tools must be chosen to meet business needs and maintain data quality. They can add business value to all business areas.
Data visualizations cannot be used as a self-service BI.
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Power BI dashboard themes cannot be used with the REST API on mobile devices. This is a limitation. They're not designed for data visualizations.
Data Visualization for Telecommunications
Data visualization can help with real-time analytics for telecoms. Utilize the vast amounts of data that may be found in telecommunications to digitize key applications and develop new data-driven goods or services.
In Telecom IT Solutions, data analytics is essential for maintaining network performance and customer happiness. You might also want to consider long-term patterns. You might keep tabs on your overall turnover rate. You may also want to keep an eye on the daily analytics for your team.
You may, for instance, check the call success percentage for your network. In either of these scenarios, Cisin can help your team succeed.
Read More: Which Tools Are Involved In The Data Analysis In 2022?
Common Types of Data Visualizations
Information transformation into visuals is not a goal. Visual representations of data make it simpler to understand.
Because of this, concepts like % or quarters are referred to as pie parts.
However, a graphic is a tool that aids in illuminating the connections between components. In the song "Everything Counts," Depeche Mode sang, "The graph on a wall tells the tale of it all." Data interpretation and storytelling require visuals.
Depeche Mode, many thanks!
Every image illustrates the data it can understand and the kind of link it illustrates (relationships, comparisons, compositions, distributions).
Let's examine some of the most prevalent visualizations in business intelligence and data analytics.
Bar chart
One tool for easily comparing data units is a bar chart. Due to its graphic form, a bar chart is an interactive component of business intelligence.
Simple modifications can be made to bar charts displaying more intricate data models. You can aggregate or stack the bars to display distributions across market segments or subcategories.
Longer data labels can fit on horizontal bar charts.
Use for: Comparing things and numerical data. Use horizontal charts to accommodate long data labels. Put stacks in bars to compare things in more detail.
Pie chart
Another typical graphic that we frequently encounter is a pie chart.
This graph helps sales and marketing teams by making it simple to display object composition or unit-to-unit comparisons.
When to use: When comparing an object's pieces to its whole.
Line graph
The visual depicts the unit's value over time using horizontal and vertical axes.
Bar charts and line graphs can be used in conjunction to display data from various dimensions.
The timeline object value is when to use. This illustrates patterns of behavior across time.
Box plot
A box plot could appear challenging at first. The example depicts quarters in a horizontal orientation when we look at it closely.
Minimum, maximum, and median are the three primary components. The first and third quartiles are where they are situated.
The distribution of objects and the departure from the median are shown as boxes.
When to utilize it: Deviation from the median, distribution of complicated things.
Scatter plot
The axes in this graphic are labeled X and Y. They are separated by dots that serve as object definitions. The location of the graph indicates the attributes it possesses.
Similar to line graphs, dots positioned between axes can be seen instantly. One drawback to this depiction is its lack of axes.
When to use it: To display object distribution and describe the attributes of each object in the graph.
Radar or Spider chart
Essentially, this chart is a line chart that has been radially drawn. Several axes and variables create this graph's spiderweb-like shape.
It does the same task as a line graph. The quantity of axes enables you to compare units from various perspectives and visually display the inclinations.
When to use it: When describing data quality or comparing various things based on several dimensions.
Dot map, or Density map
A visualization might be layered on top of the map to display the geographic range of the data. By marking the locations of each unit on the map with dots, density maps may be made.
One unit, such as a dot (e.g., A dot might represent a single object (such as a market) or a collection of items at a certain location.
Although this format is simple to identify, it can also offer a zero value if precise numbers are required.
To illustrate the dispersion or density of items
Funnel charts
These are excellent for highlighting the reduction in correlations between variables. In most circumstances, funnels use color and geometric code to distinguish things.
When there are several steps, this diagram is helpful. The example above shows how the number of subscribers dropped off following the "Contacted help" stage.
When to use: when displaying process phases using objects or a narrowing percentage value.
The Data Visualizations Catalog has a list that describes each sort of chart, graph, map, and table. When selecting the right sort of imagery, bear the following in mind:
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Details of your data set: Domain of knowledge or department within your company
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Audience: People to whom you wish to communicate the information
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Connection logic: Comparison of objects, distributions, relationships, process description, etc.
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Output means the reason you are showing this information to someone.
We'll now discuss the tools you can use for data visualization.
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Data Visualization Tools and Libraries
The market for data visualization products is very large. The scale of your business and its analytical needs should be considered when selecting a vendor to offer scalable BI services.
Despite the similarity of each vendor's capabilities, there can be variations in the services each one provides. The supplier alternatives and the resources required to develop visualization software are covered in this section.
Remember that most tools can be purchased or used for free due to their intricate analysis methods, underpinning infrastructure, and clear functionality.
Desktop programs are powerful. You risk damaging your computer if you don't have the newest hardware.
Popular Data Visualization Tools
The most common method of using data visualization technology is through a desktop program that resembles a command dashboard.
APIs can be used for data source integration. Datasets will be uploaded automatically. The application will provide you with a canvas to build a future report once you've stated where your data is kept.
The canvas can then place visualizations and populate them with data.
Each template has unique correlation types and data attributes. A report can be made by fusing various graphics.
Depending on their usefulness, reports can be shared within the system with other users or exported as CSV files.
Data visualization providers offer the same functionality as any other technology market. We will focus on how they work rather than explaining each feature.
Our article on BI tools provides a detailed overview of the tooling.
Looker: A data analytics platform that connects to both web-based and SQL data sources. Check out these features:
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Drag & drop user interface
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Customizable dashboards
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Exportable reports
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Integration of APIs with third-party sources
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Data querying from a database
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Access across platforms
Zoho Analytics: The Zoho analytics platform includes this business intelligence product. You can segment Zoho services into departments on the solutions page to get valuable, targeted insights.
Zoho features:
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Drag & drop user interface
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Web application
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Integration with multiple data sources (Google products default)
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SQL Data query and API Support
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Report embedding
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Analytics sharing access
Tableau: This provider offers a complete ecosystem of products and services for business intelligence.
Tableau is a data visualization tool and preparation tool for transforming, cleaning, and mapping data without knowing any code. Tableau features:
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Drag & drop user interface
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Many native integrations can be made with different data sources.
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Analytics sharing access
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Report embedding
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Access across platforms
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Server REST API
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Continuously updated data flow
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An instrument for managing metadata
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Dashboard commenting and highlighting
QlikView: Qlik is another BI provider offering flexibility for teams looking to create custom software.
QlikView, a freemium version that can be upgraded to Qlik Sense, is available. Qlik features
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Reports that can be customized
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Access system based on permission and role
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Open API
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Open database access
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Access across platforms
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Analytics sharing access
These products offer demo access, a trial period, and scalable products that businesses of all sizes can use. We recommend that you begin with the free tools if you're starting to explore data visualization.
Free Data Visualization Tools
On the market, there are only so many entirely free data visualization solutions.
You can always upgrade to a paid service or product. Consider the following vendors to find out more about data visualization.
Microsoft Power BI - In a few circumstances, Microsoft provides its software without charge. There are some traps, though.
All generated reports will be accessible through the Microsoft Gallery, and full functionality can be gained without spending a penny. Your entire body of work will be made public. Power BI features include:
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Drag & drop interface
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Desktop application
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Many native integrations can be made with different data sources.
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Reports that can be customized
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Incremental data updates
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Power BI Pro offers a full BI ecosystem as a Service
Our dedicated post contains more information about Power BI.
Tableau Public - Tableau Public is the same - shared functionality and all data made public on the public service.
While we won't duplicate the features, this must be included on the list for free.
Google Data Studio - This is the best option for anyone who wants to create visual reports. Data Studio features:
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Web application
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Drag & drop interface
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Integration with Google Analytics and other products of the Google Marketing Platform
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Reports that can be customized
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Data transformation tools
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Analytics sharing access
The next section will provide a list of open-source resources and libraries if you plan to use your technical knowledge.
Libraries, packages, Open-source Tools, and Open-Source Tools for Data Visualization
You can also utilize other tools to produce a certain kind of picture. They demand proficiency in programming languages (and sometimes frameworks).
D3.JS: A JavaScript package called D3.JS enables you to make visualizations. It uses an API to modify documents as objects and connect data to the document object models.
Dygraphs: A free JavaScript library for data visualization in browsers is called Dygraphs. It may be used to create interactive graphs and charts from huge datasets.
For further details, consult the data format and API reference documentation.
Chartist.js: Another JavaScript-based application that lets you style graphs and charts using CSS is Chartist.js.
Gleam: You can make scatter plot visualizations using CSS and HTML with Gleam, a Python library.
Leather: A Python module called Leather produces charts in the most basic format and stores them as SVG files for sharing.
Matplotlib: There is also Matplotlib, an additional open-source Python package designed to produce 2D graphics.
To generate new data graphics and templates, you can utilize any of the libraries above and tools in conjunction with currently available software for free.
Data Visualization: Pitfalls
You must be aware of the potential problems when adding visuals to your analytics. It is easy to use the tools. Even products that are large in ecosystems can be used easily.
Suppose you are a manager or responsible for implementing BI within your organization. In that case, some things will need to be considered before you can visualize the data.
Below is a list of the most common data analytics issues in the problem domain.
Challenges Concerning Data Preparation
Making the right assumptions is the biggest problem at the data preparation stage. The assumption that you can get data from any source is the key to big data development.
This is also true for determining whether you require a data warehouse or wish to transform data into different formats.
Testing at every data processing stage is a simple way to find a solution. Data visualization requires us to test assumptions that directly affect the visualization process.
These are:
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Initial data types
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Chosen sources
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Types of data sourcing: querying, constant updating, and ad-hoc reporting
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Architecture for your database/data warehouse
These structural elements can all be tested by an expert in the field, an ETL developer. However, assumptions can be discussed with data analysts/data engineers.
Visualization Process Challenges
Technology plays a smaller role during the visualization phase. Although semi-AI-driven business intelligence solutions are available, the user is still in charge of deciding what visualization and data attributes will be used on a canvas.
This implies:
Pitfall 1: Using the improper visualization format is the first pitfall. The sea of maps, graphs, and charts might need to be clarified.
Spending time learning the bare minimum of DataViz needed for your company is crucial. If an object has just one trait, a spider chart won't work. The opposite is true: A line chart comparing multidimensional units, such as seasonal sales across three countries and ten provinces, will not work.
Pitfall 2: Using incorrect data Similar issues may arise, but it may take some time to determine which data type can be used with your tried-and-true DataViz.
Pitfall 3: The third mistake is that you produce reports, not DataViz tools. Even though it might seem surprising, only a small portion of the data can be interpreted using a few pricy tools.
Pitfall 4: Mischoosing improper tooling is the fourth pitfall. Any tool, whether it's free or available through a library, is yours to use.
When you take into account vendor selection, things become more challenging. Vendors of data visualization offer various services to make your life as a user of the reports easier. It is important to determine if the service can scale, which means it will cover your data and the frequency with which it is updated.
You should also consider visualization capabilities, as industry-specific analytics might include unusual forms of DataViz.
Visual analytics can be implemented if you consider all possibilities and obstacles. Before you even enter the user interface, data processing is at its most critical stage.
This is the best way to start.
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Conclusion
This post will concentrate on two technologies that let us evaluate massive volumes of data and are closely related.
By contrasting the contents of this article, we can now establish which phrase functions the best in decision-making. Both are necessary for a profitable business.
