Day by day, we are moving towards a world driven by data, and in this world, we can’t ignore the importance of Data Analytics. Businesses are changing, and the ability to collect, process, and analyze data is the key differentiator as a Data-driven business will perform better. However, analyzing data is becoming increasingly complex every day because of the sheer amount of data being generated. This is why we need Data Analytics tools. With these tools, businesses can understand their data and make informed decisions from the extracted valuable information.
Data Analytics tools are there to help Data Analysts make sense of Data and perform necessary actions accordingly. This Software helps the company improve by increasing efficiency and profits. There are various tools for Data Analytics, and each one is different and provides some exciting features. Through this article, we will get to know about 10 Data Analytics Tools for beginners and businesses in 2024.
What is Data Analytics?
Data analytics is the process of examining large datasets to uncover patterns, trends, correlations, and insights that can be used to make informed decisions. It involves various techniques and tools to analyze and interpret data, often with the goal of improving business performance, understanding customer behavior, optimizing processes, or gaining competitive advantages. Data analytics encompasses a range of approaches, including descriptive analytics (summarizing data to understand its current state), diagnostic analytics (identifying reasons behind past outcomes), predictive analytics (forecasting future trends or outcomes), and prescriptive analytics (suggesting actions to achieve desired outcomes).
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Top 10 Data Analytics Tools
Looking for the top and best data analytics tools for beginners and businesses in 2024? Explore our curated list of the most powerful and user-friendly tools that can help you unlock the full potential of your data. Whether you’re a data analyst, data scientist, or business professional, these tools offer cutting-edge features and capabilities to enhance your data analysis and decision-making process. Discover the right tool for your needs and stay ahead in the competitive world of data analytics
1. Tableau
Tableau is an easy-to-use Data Visualization tool. Tableau has a drag-and-drop interface which helps to create interactive visuals and dashboards. Organizations can use this to instantly develop visuals that give context and meaning to the raw data, making the data very easy to understand. Also, due to the simple and easy-to-use interface, one can easily use this tool regardless of their technical ability. Furthermore, Tableau comes with a wide range of features and tools that help you create the best visuals which are easy to understand.
The advantage of Tableau that overshadows all others is in its Quality Visuals embedded with Interactive Information. But this doesn’t mean Tableau is perfect. Tableau is only meant for Data Visualisation, so we can’t preprocess data using this tool. Also, it does have a bit of a learning curve and is known for its high cost.
Usage: Data Visualization
Features:
- Easy Drag and Drop Interface
- Mobile support for both iOS and Android
- The Data Discovery feature allows you to find hidden data
- You can use various Data sources like SQL Server, Oracle, etc
2. Power BI
Power BI is Microsoft’s solution for Business Analytics. It provides enhanced Interactive Visualisation and capabilities of Business Intelligence. Power BI achieves all this while providing a Simple and intuitive User Interface. Being a product of Microsoft, you can expect seamless integration with various Microsoft products. It allows you to connect with Excel spreadsheets, cloud-based data sources and on-premises data sources.
Power BI is known and loved for its groundbreaking features like Natural Language queries, Power Query Editor Support, and intuitive User Interface. But Power BI does have its downsides. It can not handle records that are bigger than 250 MB in size. Besides, it has limited sharing capabilities, and you would need to pay extra to scale as per your needs,
Usage: Data Visualization
Features:
- Great connectivity with Microsoft products
- Powerful Semantic Models
- Can meet both Personal and Enterprise needs
- Ability to create beautiful paginated reports
3. Apache Spark
Apache Spark is known for its speed in Data Processing. Spark has in-memory processing, which makes it incredibly fast. It is also open source which results in trust and interoperability. The ability to handle enormous amounts of Data makes Spark distinguished. It is quite easy and straightforward to learn, thanks to its API. This doesn’t end here. It also has support for Distributed Computing Frameworks.
But Apache Spark does have some drawbacks. It doesn’t have an integrated File Management System and has fewer algorithms than its competitors. Also, it faces issues if the files are tiny.
Usage: Data Processing
Features:
- Incredible Speed and Efficiency
- Great connectivity with support of Python, Scala, R, and SQL shells
- Ability to handle and manipulate data in real-time
- Can run on many platforms like Hadoop, Kubernetes, Cloud, and also standalone
4. TensorFlow
TensorFlow is a Machine Learning Library. This open-source library was developed by Google and is a popular choice for many businesses looking forward to supporting Machine Learning capabilities to their Data Analytics workflow as Tensorflow can build and train Machine Learning Models. Tensorflow is the first choice of many due to its wide recognition, which results in an adequate amount of tutorials, and support for many Programming Languages. TensorFlow can also run on GPUs and TPUs, making the task much faster.
But TensorFlow can be very hard to use for beginners, and you need Coding knowledge to use it stand alone, and it has a steep learning curve. Tensorflow can also be quite tricky to install and configure, depending on your system.
Usage: Creation of Dataflow Graphs
Features:
- Supports a lot of programming languages like Python, C++, JavaScript, and Java
- Can scale as needed with support for multiple CPUs, GPUs, or TPUs
- Offers a large community to solve problems and issues
- Features a built-in visualization tool for you to see how the model is performing
5. Hadoop
Hadoop by Apache is a Distributed Processing and Storage Solution. It is an open-source framework that stores and processes Big Data with the help of the MapReduce Model. Hadoop is known for its scalability. It is also fault-tolerant and can continue even after one or more nodes fail. Being Open Source, it can be used freely and customized to suit specific needs, and Hadoop also supports various Data Formats.
But Hadoop does have some drawbacks. Hadoop requires powerful hardware for it to run effectively. In addition, it features a steep learning curve making it hard for some users. This is partly because some users find the MapReduce Model hard to grasp.
Usage: Data Processing and Storage
Features:
- Free to use as it is Open Source
- Can run on commodity hardware
- Built with fault-tolerance as it can operate even when some node fails
- Highly scalable with the ability to distribute data into multiple nodes
6. R
R is an Open Source Programming language widely used for Statistical Computing and Data Analysis. It is known for handling large Datasets and its flexibility. The package library of R has various packages. Using these packages, R allows the user to manipulate and visualize data. Besides, R also has packages for things like Data cleaning, Machine Learning, and Natural Language Processing. These features make R very capable.
Despite these features, R isn’t perfect. For example, R is significantly slower than languages like C++ and Java. Besides, R is known to have a steep learning curve, especially if you are unfamiliar with Programming.
Usage: Data Analysis
Features:
- Ability to handle large Datasets
- Flexibility to be used in many areas like Data Visualisation, Data Processing
- Features built-in graphics capabilities for amazing visuals
- Offers an active community to answer questions and help in problem-solving
7. Python
Python is another Programming Language popular for Data Analysis and Machine Learning. Python is widely recognized to have easy syntax which makes it easy to learn. Along with the easy syntax, the package manager of Python features a lot of important packages and libraries. This makes it suitable for Data Analysis and Machine Learning. Another reason to use Python is its scalability.
This doesn’t mean Python is flawless. It is quite slow when we compare it to languages like Java or C++; this is because Python is an interpreted language while the others are compiled. Besides, Python is also infamous for its high memory consumption.
Usage: Data Processing and Data Visualization
Features:
- Easy to learn and user-friendly
- Scalable with the ability to handle large datasets
- Extensive packages and libraries that increase the functionality
- Open Source and widely adopted which ensures problems can be fixed easily.
8. SAS
SAS stands for Statistical Analysis System. The SAS Software was developed by the SAS Institute, and it is widely used for Business Analytics nowadays. SAS has both a Graphical User Interface and a Terminal Interface. So, depending on the user’s skillsets, they can choose either one. It also has the ability to handle large datasets. In addition, SAS is equipped with a lot of Analytical Tools which makes it valid for a lot of applications.
Although SAS is very powerful, it has a big price tag and a steep learning curve, so it is quite hard for beginners.
Usage: Data Analysis
Features:
- Ability to handle large datasets
- Support for graphical and non-graphical interface
- Features tools to create high-quality visualizations
- Wide range of tools for predictive and statistical analysis
9. QlikSense
QilkSense is a Business Analytics Tool that provides support for Data Visualisation and Data Analysis. QuilkSense supports various Data sources from Spreadsheets, Databases, and also Cloud Services. You can create amazing Dashboards and Visualisations. It comes with Machine Learning features and uses AI to help the user understand the Data. Furthermore, QlikSense also has features like Instant Search and Natural Language Processing.
But QilkSense does have some drawbacks. The data extraction of QilkSense is quite inflexible. The Pricing Model is quite complicated, and it is quite sluggish when it comes to large datasets.
Usage: Data Visualization and Data Analysis
Features:
- Tools for stunning and interactive Data Visualisation
- Conversational AI-powered analytics with Qlik Insight Bot
- Features tools to create high-quality visualizations
- Provides Qlik Big Data Index which is a Data Indexing Engine
10. KNIME
KNIME is an Analytics Platform. It is Open Source and features an User Interface which is intuitive. KNIME is built with scalability and also offers extensibility via a well-defined API Plugin. You can also automate Spreadsheets, do Machine Learning, and much more using KNIME. The best part is you don’t even need to code to do all this.
But KNIME does have its issues. The abundance of features can be overwhelming to some users. Also, the Data Visualisation of KNIME is not the best and can be improved.
Usage: Data Processing and Data Visualization
Features:
- Intuitive User Interface with drag and drop function
- Support for extensive analytics tools like Machine Learning, Data Mining, Big Data Processing
- Provides tools to create high-quality visualizations
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Conclusion
The success of a business is quite dependent on the Data Analytics tool in this Data Driven world. We discussed various Data Analytics tools which are quite essential for business. From Data Visualisation to Data Processing, there are a lot of tools, and knowledge about them is quite essential. With the enormous amount of data created every day, as we move through 2024, the need for Data Analytics tools is ever clearer. Regardless if you are an Analyst, Data scientist, or business professional looking to make your business better, choosing the right tools is very important, as it does give you an edge in today’s world.
FAQs
What challenges should I know about while using Data Analytics tools?
Here are some challenges while using Data Analytics tools-
- The quality of the data has to be ensured to get a reliable result. So, the data should be meaningful and accurate.
- Keeping sensitive and personal data secure is extremely important and challenging
- The interpretation of the result is an extremely necessary and important step.
There are a lot of challenges you come across while using Data Analytics tools. We mentioned a few of them here.
What are some different types of Data Analytics Tools?
Data Analytics Tools come in a lot of shapes and flavors including but not limited to Data Visualization Tools, Predictive Analysis Tools, Data Mining Tools and etc.
When should I use Data Analytics Tools?
When you have a Data-Overload and manually doing Analysis is not viable, you need to use a tool. Also, Data Analytics Tools improve the decision-making process and give you a competitive advantage.
Which Tools should I use if I am a Beginner?
Among the Data Analytics Tools mentioned above, Python, and Power BI are viable choices for a Beginner in Data Analysis.
Which tools are suited for large Datasets?
Tools like Tableau, Python, R, QlikSense, and SAS are often used when handling large Datasets.
