AI Data Visualization Techniques and Tools

Data is very powerful in driving business globally. In order to make reasonable business decisions, we have to rely on both humans and AI tools. Datasets can seem like an alien language to many in an organization outside of the analytics team. This is where data visualization comes into play. Using data visualization, professionals can take raw data and turn it into something easy to interpret.

DATA VISUALIZATION TOOLS FOR BUSINESS

1. Microsoft Excel (and Power BI)

In the strictest sense, Microsoft Excel is a spreadsheet software, not a data visualization tool. Even so, it has useful data visualization capabilities. Given that Microsoft products are widely used at the enterprise level, you may already have access to it.

According to Microsoft’s documentation, you can use Excel to design at least 20 types of charts using data in spreadsheets. These include common options, such as bar charts, pie charts, and scatter plots, to more advanced ones like radar charts, histograms, and treemaps.

There are limitations to what you can create in Excel. If your organization is looking for a more powerful data visualization tool but wants to stay within the Microsoft ecosystem, Power BI is an excellent alternative. Built specifically as a data analytics and visualization tool, Power BI can import data from various sources and output visualizations in a range of formats.

2. Google Charts

For professionals interested in creating interactive data visualizations destined to live on the internet, Google Charts is a popular free option.

The tool can pull data from various sources—including Salesforce, SQL databases, and Google Sheets—and uses HTML5/SVG technology to generate charts, which makes them incredibly accessible. It offers 18 types of charts, including bar charts, pie charts, histograms, geo charts, and area charts.

Members of the Google community occasionally generate new charts and share them with other users, which are arranged in a gallery on Google’s website. These charts tend to be more advanced but may not be HTML5-compliant.

3. Tableau

Tableau is one of the most popular data visualization tools on the market for two main reasons: It’s relatively easy to use and incredibly powerful. The software can integrate with hundreds of sources to import data and output dozens of visualization types—from charts to maps and more. Owned by Salesforce, Tableau boasts millions of users and community members, and it’s widely used at the enterprise level.

Tableau offers several products, including desktop, server, and web-hosted versions of its analytics platform, along with customer relationship management (CRM) software.

A free option, called Tableau Public, is also available. It’s important to note, however, that any visualizations created on the free version are available for anyone to see. This makes it a good option to learn the software’s basics, but it’s not ideal for any proprietary or sensitive data.

4. Zoho Analytics

Zoho Analytics is a data visualization tool specifically designed for professionals looking to visualize business intelligence. As such, it’s most commonly used to visualize information related to sales, marketing, profit, revenues, costs, and pipelines with user-friendly dashboards. More than 500,000 businesses and two million users currently leverage the software.

Zoho Analytics has several paid options, depending on your needs. There’s also a free version that allows you to build a limited number of reports, which can be helpful if you’re testing the waters to determine which tool is best for your business.

There are many other tools that work similarly to Zoho Analytics and are tailored to sales and marketing professionals. HubSpot and Databox are two examples, both of which include powerful data visualization capabilities.

5. Datawrapper

Datawrapper is a tool that, like Google Charts, is used to generate charts, maps, and other graphics for use online. The tool’s original intended audience was reporters working on news stories, but any professional responsible for managing a website can find value in it.

While Datawrapper is easy to use, it’s somewhat limited, especially compared to others on this list. One of the primary limitations is that it doesn’t integrate with data sources. Instead, you must manually copy and paste data into the tool, which can be time-consuming and liable to error if you aren’t careful.

Some common outputs include scatterplots, line charts, stacked bar charts, pie charts, range plots, and a variety of maps and tables. Free and paid options are available, depending on how you intend to use the tool.

6. Infogram

Infogram is another popular option that can be used to generate charts, reports, and maps.

What sets Infogram apart from the other tools on this list is that you can use it to create infographics (where its name comes from), making it especially popular among creative professionals. Additionally, the tool includes a drag-and-drop editor, which can be helpful for beginners.

Visualizations can be saved as image files and GIFs to be embedded in reports and documents, or in HTML to be used online. Like most of the other tools on this list, Infogram has tiered pricing, ranging from a free to enterprise-level version.

Microsoft Excel:
Microsoft Excel is used to displays the data in horizontal and vertical rows. The data are usually stored in the cells. We have an option of formulas in the Excel that can be used for data, data analysis, statistical analysis, and its place of storage.

• You can even add any charts, graphics, etc. to make it more presentable.
• Excel locks the whole spreadsheet once it is accessed.
• An Excel document is referred to as a workbook and each of these workbooks must contain at least one worksheet.

Microsoft Access:
Microsoft Access is a database program, it uses unique ID numbers and an editable list of data to store details on large amounts of items, i.e., you could use this program to store large amount of data.

• Access is designed to have multiple users working in the same DB files along with the various safety precautions items to help protect the data such as record level locking.
• The database created in Access is saved with a .mdb extension.
• Data is stored in tables.
• Each field of a table can be associated with certain constraints like only allowing an alphanumeric value or different datatypes.
• Like any other relational database, it works on the principles of tables, fields, and relationships. It supports different kinds of datatypes – numbers, dates, texts, etc.

MySQL:

MySQL is an open-source relational database management system based on SQL – Structured Query Language. The application is used for a wide range of purposes, including data warehousing, e-commerce, and logging applications.

• Data is stored in tables.
• Each field of a table can be associated with certain constraints like only allowing an alphanumeric value or different datatypes.
• Like any other relational database, it works on the principles of tables, fields, and relationships. It supports different kinds of datatypes – numbers, dates, texts, etc.
• MySQL is easy to use.
• It is secure.
• Client/ Server Architecture.
• Free to download.
• It is scalable.
• High speed.
• High Flexibility.

Cloud ML:

The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale. The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.

The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science. AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

• The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.
• The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
• The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
• AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

Cloud AI:

The AI cloud, a concept only now starting to be implemented by enterprises, combines artificial intelligence (AI) with cloud computing. An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.

• Data Mining.
• Agile Development.
• Reshaping of IT Infrastructure.
• Seamless Data Access.
• Analytics and Prediction.
• Cloud Security Automation.
• Cost-Effective.

MS Excel	MS Access	MySQL	Cloud ML	Cloud AI
Microsoft Excel is an application that uses spreadsheets to create charts, graphs, tabular models.	Microsoft Access is an application that acts as a database program. Access deal with database program by collecting, sorting, and manipulating data.	MySQL is an open-source relational database management system based on SQL	The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale.	An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.
It is used for spreadsheets,  statistical and financial calculations.   Excel helps in performing sophisticated what-if analysis operations on your data, such as statistical, engineering, and regression analysis.	It is used for storing and manipulating large amounts of information.   Access do not perform what-if analysis.	The application is used for a wide range of purposes, including data warehousing, e-commerce, and logging applications.	The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.	Enterprises use the power of AI-driven cloud computing to be more efficient, strategic, and insight-driven. AI can automate complex and repetitive tasks to boost productivity, as well as perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows.
Microsoft Excel is easy to learn.	Microsoft access is quite hard to learn.   Access that needs programming knowledge for some part.	MySQL is easy to use.	The pay-per-use model further makes it easy to access more sophisticated capabilities without the need to bring in new advanced hardware.	Cloud AI Platform is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
The storage capacity is less since excel isn’t built for storing data.	The storage capacity is more since access is mainly built for storing, sorting, and manipulating databases.	MySQL stores data in files in your hard disk. The maximum size of MySQL table is 65536 terabytes.	This storage service provides petabytes of capacity with a maximum unit size of 10 MB per cell and 100 MB per row. 1024 Petabytes of data.	1024 Petabytes of data. The larger the RAM the higher the amount of data it can handle hence faster processing. 16GB RAM and above is recommended for most deep learning tasks.
Excel is less flexibility as compared to access.	Access has more flexibility as compared to excel.	High Flexibility.	High Flexibility and Cost Effective.	Seamless Data Access. High Flexibility and Cost Effective.
It works on the data model of a non-relational or flat worksheet.	It works on the model of multiple relational tables and sheets.	MySQL is a Relational Database Management System (RDBMS). The logical model, with objects such as databases, tables, views, rows, and columns, offers a flexible programming environment.	Cloud ML Engine is used to train machine learning models in TensorFlow and other Python ML libraries (such as scikit-learn) without having to manage any infrastructure.	In Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the data from past decisions.
It locks the entire spreadsheet.	It locks data at the record level.	MySQL uses table-level locking in all storage engines except InnoDB meaning that table-level locking is used for tables running the MyISAM, MEMORY and MERGE storage engines, permitting only one session to update tables at a time.	Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.	Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.
Excel is good for short term solutions and small-scale projects	Access is good for long term solutions and large-scale projects.	MySQL is ideal for storing application data, specifically web application data. As MySQL is a relational database, it’s a good fit for applications that rely heavily on multi-row transactions.	The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.	The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
Microsoft Product	Microsoft Product	Oracle Product	Google, Amazon, Microsoft, and IBM	Google, Amazon, Microsoft, and IBM
			ML’s aim is to improve accuracy without caring for success.	The goal of AI is to increase the chances of success.
			ML is the way for the computer program to learn from experience.	AI is a computer program doing smart work.
			The ML’s goal is to keep learning from data to maximize the performance.	The future goal of AI is to stimulate intelligence for solving highly complex programs.
			ML allows the computer to learn new things from the available information.	AI involves decision-making.
			ML looks for the only solution.	AI looks for optimal solutions.