Data analytics involves the analysis of raw data in order to make conclusions about that information. With data analytics, organizations are increasingly optimizing their business performance, increasing their efficiency, maximizing profits and revenues, and making more strategically-guided business decisions (Broman & Woo, 2018). Data analytics involves many approaches including looking at what will happen in the future, why something happened, what happened in the past, and what should happened next. However, this cannot be achieved without specific tools ranging from databases, programming languages, big data tools, cloud computing, spreadsheets, and self-service data visualization. Using these data analytics tools, organizations are able to convert raw data into information for decision making.

Databases

Databases are used as data analytics tools in what is known as in-database analytics. This is a data analytics method where an organization analyzes its data within a database in which it is stored. When the data is analyzed within the database, an organization successfully eliminates the problems of having move data from the database to other tools for analysis. Additionally, in using database as a data analytics tool, it means that data analytics logic is built into that specific database instead of having to use a separate data analytics application (D'silva et al., 2018).

Programming Languages

Although programming languages are known for software and system development, they are also used as data analytics tools. Examples of data analytics programming languages are R and Python which have become must-have for data analysts. These programming languages have huge ranges of resource libraries suitable to various data analytics activities and tasks. For example, pandas and NumPy in Python helps to streamline highly computational data analytics tasks and activities. Additionally, these functions support data manipulation (Harris et al., 2020).

Big Data Tools

There are many big data tools for data analytics, one of which is Apache Stark. In data analytics, these big data tools are used as data processing frameworks and used for processing big data and machine learning. Designed for analyzing big data, these big data tools help in analyzing data to draw conclusions from the resultant information. Some of the advantages of big data tools for data analytics include fast data analysis, dynamism, and easy to use. However, they may be limited in some data analytics tasks because of disadvantages such as the unavailability of file management system. Additionally, they have a rigid user interface (Cao et al., 2015).

Cloud

When cloud is used as a data analytics tool, it involves performing data analytics on cloud data in collaboration with a cloud service provider. Cloud-based data analytics is also known as a software-as-a-Service (SaaS). Cloud analytics runs in both public and private clouds and when used, it helps organizations in scaling quickly because it helps in significantly reducing the costs associated with an organization having to perform data analytics using other tools (Cao et al., 2015).

Spreadsheets

Spreadsheets are some of the most basic data analytics tools and they feature calculations and graph functions which are suitable for data analytics. Regardless of what one specializes and other type of software, a data analyst might need, spreadsheets are a staple data analytics tool.

Spreadsheets are mostly used for data wrangling and reporting because of the useful functions and plug-ins which are an advantage to the tool. While spreadsheets are advantageous and, therefore, the reason for their preference, they also have disadvantages like potential for calculation errors and poor handling of big data (Hillier, 2023).

Self-Service Data Visualization

While most of the popular data analytics tools require that users have knowledge and skills of their use and operation, it is different with self-service data visualization tools. These are the types of data analytics tools that, similar to other tools, help users to select, filter, compare, visualize, and comprehensively data without the need for help from specialized data analysts or specialized and advanced data analytics knowledge and skills. With organizations using these kinds of data analytics tools, it means that anyone at the organization can participate in data analytics (Behera & Swain, 2019).