Data Analytics and Visualization: A Comprehensive Guide
Technology is fast becoming a key element in today’s world. Everything from Business to Education to Medicine is now driven by technology. This has gone a long way in automating previously manual tasks and making everything seamless and efficient. However, technology is known to generate vast amounts of data, which has led to the emergence of numerous fields, all reliant on data and its ever-growing importance. Among them are Data Analytics and Visualization.
Table of Contents
It is no secret that the age of Big Data is upon us, and data drives everything, especially in business. This is where Data Analytics and Visualization come into the picture. While they may be relatively new terms to most people, investing in Data Analytics and Visualization makes the difference between a successful corporation and a failing one. With this in mind, this blog is meant to acquaint you with the knowledge of Data Analytics and Visualization. Let’s dive in!
Table of Contents
- What is Data Analytics?
- What are the Types of Data Analysis?
- What are the Components of Data Analytics?
- What is Data Visualization?
- What are the Types of Data Visualization Techniques?
- What are the Advantages of Data Analytics and Visualization?
- What are the differences between Data Analysis and Visualization?
- Conclusion
What is Data Analytics?
As mentioned earlier, the emergence of technology as a critical driving force in every sector in today’s world has led to massive Data Generation. Without Data Analytics, companies cannot make sense of what this information means. Analytics is a broad term that encompasses numerous subfields. It refers to all the tools and activities involved in processing data to develop valuable insights and interpretations. It is worth noting that Data Analytics is dependent on computer tools and software that help extract data and analyze them for business decisions to be made accordingly.
Data Analytics is highly adopted in the commercial industry since it helps companies to better understand their customers and boost their advertising campaigns. This is a highly dynamic field due to the numerous innovations we keep on hearing every once in a while. Data Analytics has been converted into a mechanical sector reliant on Computer Algorithms that process raw data to come up with sensible conclusions at the time of this writing.
What are the Types of Data Analysis?
The Data Analysis technique you decide to go with depends on the kind of information you have. Accordingly, there are two main Data Analysis techniques, namely Qualitative and Quantitative. Let’s see what each means and how they can be of use to your business.
1. Quantitative Data Analysis
This type of Data Analysis leans more toward the statistical nature of your data. It generally tells you what is happening and whether the trends are showing a rise or fall. Below are the types of Quantitative Data Analysis:
- Descriptive Analysis: This type of Data Analysis lets you see the patterns and trends in a particular set of data. It includes processes such as calculating frequencies, percentages, and measures of central tendency, including mean, mode, and median.
- Inferential Analysis: It is used when the differences and correlations between particular data sets need to be examined. The processes involved include ANOVA, t-Tests, and Chi-Square.
2. Qualitative Data Analysis
While Quantitative Analysis focuses on numeric data, Qualitative is the complete opposite, dealing with non-numeric data such as audio, video recordings, images, texts, and transcripts. In addition, Qualitative Data generally tells you how your data is changing.
What are the Components of Data Analytics?
Data Analytics components refer to the different techniques you can use for processing any set of data. They include:
- Text Analytics: This is the technique used in autocorrect in phones and software such as Microsoft Word. It involves analyzing large amounts of text to come up with Algorithms. Applications include Linguistic Analysis and Pattern Recognition.
- Data Mining: One of the most critical Data Mining applications is determining behavioral patterns in inpatient data during clinical trials. As the name suggests, Data Mining breaks large chunks of data into smaller pieces that fit a specific purpose.
- Business Intelligence: This is one of the essential processes for any successful business. It involves transforming data into actionable strategies for a particular commercial entity. For example, this is the process behind product placement and pricing in most companies.
Simplify your Data Analysis with Hevo’s No-code Data Pipeline
A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate data from 100+ data sources (including 30+ Free Data Sources) to a destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.
Check out some of the cool features of Hevo:
- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
You can try Hevo for free by signing up for a 14-day free trial.
What is Data Visualization?
While Data Analytics is more involved in bringing some form of structure into unorganized data, Data Visualization deals with picturing the information to develop trends and conclusions. In Data Visualization, information is organized into charts, graphs, and other forms of visual representations. This simplifies otherwise complicated information and makes it accessible to all the involved stakeholders to make critical business decisions.
What are the Types of Data Visualization Techniques?
Like Data Analytics, the type of Data Visualization technique you choose will largely depend on the type of data to be modeled and the purpose. It is worth noting that some Visualizations are manually created while others are automated. Below are some of the popular Visualization techniques:
- Histograms: This is a Graphical Visualization Tool that organizes a set of data into a range of frequencies. It bears key similarities to a Bar Graph and organizes information in a way that makes it easy to interpret.
- Graphs: These are excellent tools for analyzing the time series relationship in a particular set of data. For instance, a company’s annual profits could be analyzed based on each month using a graph.
- Fever Charts: A Fever Chart is an indispensable tool for any business since it shows how data changes over time. For instance, a particular product’s performance could be analyzed based on its yearly profits.
- Heatmap Visualization: This tool is based on the psychological fact that the human brain interprets colors much faster than numbers. It is a graph that uses numerical data points highlighted in light or warm colors to represent high or low-value points.
- Infographics: Infographics are effective when analyzing complex datasets. They take large amounts of data and organize it into an easy to interpret format.
These were some of the most popular Visualization you can leverage to level up your Data Analytics and Visualization workflows.
What are the Advantages of Data Analytics and Visualization?
Data Analytics and Visualization are a crucial elements of the business decision-making process. It helps the stakeholders to recognize patterns in the data and devise profitable business strategies. Below are some of the benefits of Data Analytics and Visualization:
- Better Decision Making: By using skilled Data Analysts and the right software, companies can identify market trends and make better business decisions to Boost Sales and Profits.
- Better Insights: Companies can get better insights into their Customer Base- using Data Analytics and Visualization, companies can break large customer data down into smaller sets that can be used to understand the Client Base better.
- Improving Productivity and Revenue Growth: By looking at the results from Data Analytics and Visualization, companies get to know which areas they need to invest in and what processes need to be automated for better efficiency.
- Noting Changes in Market Behaviour: With a real-time Data Analytics and Visualization Dashboard, company stakeholders can quickly identify changes in market behavior and make appropriate business decisions.
- Analyzing Different Markets: Using Data Analytics and Visualization techniques, companies can analyze different markets and decide which ones to place attention on and which ones to avoid.
- Business Trends: This is one of the most valuable applications of Data Analytics and Visualization. It allows businesses to examine the present and past trends to make predictions that determine the way forward for the business.
- Data Relationships: This is one of the most obvious benefits of Data Analytics and Visualization. It helps companies note the relationships between independent data sets and make business decisions based on these findings.
What are the differences between Data Analysis and Visualization?
| Based on | Data Visualization | Data Analytics |
|---|---|---|
| Definition | The graphical representation of information and data in a pictorial or graphical format is known as data visualization. | Data analytics is the process of analyzing data sets in order to make decisions based on the information available, which is increasingly done with the help of specialized software and systems. |
| Benefits | Determine which areas require attention or improvement. Determine which factors have an impact on customer behavior. It assists in determining which products belong in which locations. Estimate sales volume. | Determine the models and patterns that underpin them. Acts as a data source for data visualization; aids in business improvement by anticipating needs Conclusion |
| Used for | The purpose of data visualisation is to visually communicate information to users in a clear and efficient manner. | Every business collects data; by analysing the data, data analytics can assist the business in making better business decisions. |
| Relation | Data visualization aids in better understanding. | The datasets’ conclusions will be drawn using data visualization and analytics together. It may serve as a source of visualization in a few scenarios. |
| Industries | Finance, banking, healthcare, retailing, and other industries use data visualization technologies and techniques extensively. | Commercial, financial, healthcare, crime detection, and travel agencies are all using data analytics technologies and techniques. |
| Tools | Plotly, DataHero, Tableau, Dygraphs, QlikView, ZingCHhart, etc. | Trifecta, Excel /Spreadsheet, Hive, Polybase, Presto, Trifecta, Excel /Spreadsheet, Clear Analytics, SAP Business Intelligence, etc. |
| Platforms | Analysis and design, Big data processing, Service management dashboards | Data mining, analysis, and design are all terms that are used to describe the processing of large amounts of data. |
| Techniques | Static or interactive data visualization is possible. | Prescriptive and predictive analytics are two types of data analytics. |
| Performed by | Data Engineers | Data Analysts |
Conclusion
In this article, you learned what Data Analytics and Visualization are all about and what are the advantages Data Analytics and Visualizations bring forth. Data is becoming one of the most valuable assets in today’s business world, and skills are fast changing to accommodate a world driven by data. With this in mind, professionals must get the required skill set to visualize and analyze data and make sane and practical business decisions based on this information. So, do not get out of the data movement! Get on board today!
Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be a challenging task and this is where Hevo saves the day! Hevo Data offers a faster way to move data from Databases, SaaS applications into your Data Warehouse to be visualized in a BI tool. Hevo Data is fully automated and hence does not require you to code.
You can try Hevo for free by signing up for a 14-day free trial. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Share your experience of learning about Data Analytics and Visualization. Let us know in the comments section below!
