Data analysis is one of the core bricks laid in the foundation of a company. Today, almost 60% of the companies in the world engineer their business strategies based on data. With a rise in the demand for data scientists, the development of AI tools for data analysts also increased. These tools improve efficiency, increase accuracy, and provide a robust strategy for data mining. By using these tools, analysts can analyze a high amount of data in a shorter period.
Whether you are a fresher or an experienced data analyst, you will benefit from this detailed list of innovative AI tools. These pioneering technologies are built for predictive analysis, automating repetitive tasks, and speeding up the overall process of data analysis.
So let’s explore the 8 best AI tools that data analysts must use
1. RapidMiner
One of the most efficient AI-powered data analytics tools is RapidMiner. This free tool provides multiple features to data analysts and their teams. Users can take advantage of the intuitive interface by using the drag-and-drop feature and streamline multiple data analytics tasks. RapidMiner is programmed to analyze all kinds of data including images, audio files, and texts. With the help of artificial intelligence and machine learning, analysts can perform tasks like loading and analyzing data and receive constant support throughout the analytics process.
2. Tableau
Tableau is another widely used AI data analytics tool. It is quite popular in the data analytics community due to its high-end features and intuitive interface. With interactive dashboards and data visualization features, Tableau is one of the most handy AI tools for data analysts. Users can gain spontaneous visual answers to questions asked in plain language, giving them the ability to analyze data in a nick of time. If you are someone who is looking to explain data in a simple, visual manner, then this tool is for you. Machine learning algorithms clubbed with high-tech artificial intelligence enable users to extract deep insights and seamlessly simplify data.
3. IBM Watson Analytics
With the IBM Watson Analytics tool, users can perform multiple data analytics tasks with high accuracy. The AI tools use natural language processing algorithms that help data analysts efficiently communicate with their data
. Watson Analytics catalyzes the data exploration process with precision while streamlining complex tasks. By using this tool, analysts can get access to automated insights and identify different data trends with a click of a button.
4. DataRobot
DataRobot is a widely used AI-powered data analytics tool. This tool automates complicated tasks with minimal human intervention. With the power-pact combination of machine learning and AI, DataRobot provides users the opportunity to operate their businesses on multiple data centers and public clouds. Another great function of this tool is predictive analysis which helps users make highly lucrative data-driven decisions. Engineered for optimum performance, DataRobot is programmed to pick out the most efficient algorithms that automate data analysis.
5. Polymer
One of the core features of Polymer is to swiftly convert data into a strong, comprehensible database. Similar to the majority of AI-driven data analytics tools, this platform does not require coding. The process of analyzing data is simplified by a well-engineered artificial intelligence algorithm. It reduces manual intervention by allowing users to upload their data and swiftly organize it into streamlined data. This database can be used to gain deeper insights. Due to its interactive nature and intuitive user interface everyone from data scientists to content creators, can use this tool with absolute ease. What makes Polymer stand out is its ability to make spreadsheets interactive and searchable.
6. Monkeylearn
With the perfect blend of artificial intelligence and machine learning, developers have created a tool that simplifies the complex task of data analysis. Monkeylearn is a data analysis tool that allows users to extract crucial insights from raw data. It enables individuals to organize their data according to topics, by setting up text classifiers and text extractors. By automating complex data processing tasks, Monkeylearn, helps users to save both time and energy, by classifying data through keywords and high-end text analysis. The intuitive user interface equips users with the ability to integrate multiple plugins with the tool.
7. Akkio
When it comes to marketing, sales, or predictive analysis, Akkio is one of the best AI-powered tools in the market. Like most robust data analytics tools, Akkio requires minimum human intervention when it comes to streamlining raw data. One of the key features of this tool is lead prioritization. It is also useful when it comes to generative charts and reports. With its interactive and user-friendly dashboard, users can maneuver the tool without knowing how to code. Other key features include multiple API integrations, and creating neural networks using selected variables. This AI-powered tool can used for multiple aspects of a business.
8. Knime
Knime is another, multi-purpose data analytics tool. Individuals can use this AI-powered tool for data exploration, data visualization, constructing machine learning models, and predictive analysis. It can also be used to generate dashboards, which is another key feature of this tool. Engineers, data scientists, and analysts can not only use Knime for data analysis but also for transformation procedures. Another great thing about Knime is that users receive strong support from the team, in case they face any roadblocks on the way. The developers are constantly tweaking the tool and equipping it with the latest upgrades, making sure it is at par with the current technological advancements.
In conclusion, with the use of artificial intelligence, the field of data analysis has seen rapid growth and advancement. The 8 AI tools we’ve explored in this blog show us that these tools can be used by data analysts, data scientists, marketing experts, and professionals belonging to different backgrounds. From automating repetitive tasks to uncovering hidden insights in massive datasets, these tools have the potential to make data analysis more efficient, accurate, and insightful than ever before. As AI and machine learning continue to advance, we can expect even more innovative tools that will help reshape the field of data analysis.
