A massive amount of data is being produced daily, and it will increase in the upcoming years, close to 2.5 quintillion bytes created daily. The data play a significant role for businesses, enabling them to make crucial decisions, collect valuable insights, and offer data–based solutions. The data analyst analyzes the data frequently, thus the advent of data analytics.
However, numerous tools are available to solve the purpose of transforming raw data into helpful. Data analytics use this tool to extract useful information from raw data and make informed decisions for the business. But, from varied options, how to pick the best tool? In this blog, we discuss some of the best data analytics tools used to enhance a data analytics career.
Top Data Analytics Tools of 2023
Here is a list of the best data analytics tools for 2023 that you can use to add intelligence to the business processes:
1. Datapine
It is an interactive BI dashboard that allows you to access a company’s data and make data–driven decisions regardless of size and domain of industry. Datapine's top feature is a predictive analytics forecast engine that helps evaluate data from various sources connected with other data connectors. However, with the help of this tool, the employees can explore the data and obtain relevant insights based on it. For data analytics professionals, its advanced features allow you to dig deep into data.
2. R Programming
It is an open-source programming language used for statistical calculations or graphics. This tool analyzes organizational data and develops software applications that perform statistical analysis. R democratizes analytics allowing business users with interactive data visualization and reporting tools. Also, it enables them to minimize the time spent on data preparation and offers them correct and in–depth analyses. Some of the functions carried out with these tools are:
· Web Analytics
· Data Mining
· Sentiment Analytics
· Time series analysis
· Text mining
· Social media analytics
3. Python
Python is a robust data analytics tool available to users with a comprehensive set of packages/libraries. It is free and open–source software that is utilized for high levels of visualization and comes with packages like Seaborn and Matplotlib. However, Pandas is one of the enormous libraries which come along with Python. Due to its ease and versatility, developers prefer to learn this language, which is a high-level and object–oriented programming language.
Various applications like Instagram, YouTube, and Google are created using Python. The tool is handy for web developers because it is less time–consuming and has a simple syntax. It supports frameworks like Django, Web2Py, Sanic, Pyramid, and many more.
4. Apache Spark
Apache Spark is a software framework allowing data analysts and data scientists to process large data sets rapidly. It was initially developed in 2012 and donated to the Apache Software Foundation. Spark is a distributed computing platform that can perform computationally intensive analytics tasks on many computers. It was designed to analyze big unstructured data. Spark is faster than other similar frameworks (such as Apache Hadoop). It uses RAM instead of local memory to be 100x faster than Hadoop. It is often used to develop data-heavy machine-learning models.
The library of machine-learning algorithms MLlib includes classification, regression, and clustering algorithms. Spark can be computationally costly because it uses large memory. Spark lacks a file system management system. This means that it often needs integration with other software Hadoop.
5. Airtable
Airtable is another name on our top data analysis tools list. You can combine a robust database with data analysis and visualization functions (similar to traditional spreadsheet tools) to analyze the results. Airtable’s most notable feature is its ability to create custom interfaces according to your needs. This gives all team members the required information and an easy implementation of the knowledge.
6. Power BI
Business Intelligence collects raw data and converts it into meaningful information, offering actionable insights to make tactical business decisions. Combined tools transform unrelated data sources into coherent, visually appealing, and deeper interactive insights for the user. Microsoft developer power BI stands out for managing publications and distribution capabilities.
One of the top features of Power BI is that it offers customized visualization where the developers can bring the requirement and transform them into charts, graphs, maps, etc.
7. SAS Viya
SAS Viya is a data analytics program for data mining and predictive modeling. The business intelligence suite offers a 2-week trial and is simple to use. This tool has new modules frequently added upon customer request. It also includes specific functions such as analytics for IoT. The tool is highly business-oriented, making data analysis easy.
8. Tableau
Tableau is a Business Intelligence Tool that can be used by data analysts to visualize, analyze and understand their data. Tableau offers fast analytics and can explore a wide range of data, including spreadsheets, databases, Hadoop data, and cloud services data. The powerful GUI makes it easy to use. You can create interactive dashboards quickly and easily. Tableau is a market leader, allowing you to work with live data rather than spend too much time on data wrangling.
Tableau is constantly improving its services. They have updated to offer users intelligent dashboards that allow them to explore data, make quick analytics, update automatically, and publish dashboards to share on the internet or mobile devices.
Conclusion
Data analytics tools allow customers to have personalized experiences with products and services. Data Analytics Tools are becoming increasingly crucial for businesses and organizations to mitigate risks and resolve recurring problems. Data analytics can be made more effective by choosing the right tool.
