Top AI Tools for Data Analysts 2023


As an interactive analytics and data visualization platform, Tableau can be used by someone unfamiliar with programming as one of its main selling features. Tableau allows users to build and distribute reports to others via desktop and mobile devices. Reports can be shared in a web browser or embedded in an app with the help of the data analytics tool, which allows for the viewing and analysis of data. Whether Tableau is hosted in the cloud or on your servers, this is possible. VizQL is Tableau’s query language, and it turns dashboard and visualization components that users drag and drop into database queries. There is also little requirement for speed improvement on the end user’s part. 

Microsoft Power BI

Microsoft Power BI, a powerful business intelligence platform that lets users filter through data and visualize it for insights, is another top AI tool for data analysis. Users may import data from practically anywhere into the platform and immediately create reports and dashboards. To further aid in data analysis, Microsoft Power BI also allows the development of machine learning models. Multiple integrations are supported, including a built-in connection to Excel and one with Azure Machine Learning. Businesses that already use Microsoft Office products will find it simple to use Power BI for reporting, visualization, and dashboard creation. 


Polymer is a strong AI technology that data analysts may use to streamline and adapt data into a powerful database with minimal human intervention. One of the finest things about Polymer is that it doesn’t require any code, just like other fantastic AI tools. The software employs AI to examine information and improve understanding. All of this is possible with Polymer without requiring a lengthy learning curve. Users may easily gain insights from their data by uploading a spreadsheet to the platform and instantly converting it into a simplified database. Polymer boasts that it is the only software that “instantly” makes a user’s spreadsheets “searchable, intelligent, and interactive.” Data analysts, digital marketers, content developers, and more use the service. 


Akkio, a business analytics and forecasting tool, comes close to the conclusion of our list of the five best artificial intelligence tools for data analysts. This is an entry-level tool designed for users who are just getting started with their data. Users can upload their data to the AI tool and then choose the variable they wish to predict to have Akkio construct a neural network specifically for that variable. It has great potential as a tool for predictive analysis, advertising, and closing deals. To use Akkio, you need no programming knowledge, like many of the other great tools below. Eighty percent of the data submitted is used for training by Akkio, while the remaining twenty percent is used for validation. The AI tool provides an accuracy grade for the models and filters out false positives rather than making predictions. 


MonkeyLearn is another no-coding platform with AI data analysis features to aid users in visualizing and rearranging data. Multiple artificial intelligence (AI) powered text analysis tools are available in MonkeyLearn, allowing users to analyze and visualize data immediately. In addition, it may be used to implement text classifiers and text extractors, which facilitate automated topic and intent-based data sorting and the extraction of product features and user data, respectively. MonkeyLearn’s use of machine learning to streamline business processes and analyze text eliminates the need for countless man-hours of data entry. The ability to automatically pull data from incoming requests is a popular feature in MonkeyLearn. Data is sorted and categorized based on keywords and advanced text analysis, and relevant content is highlighted and filed away accordingly. 

IBM Watson Analytics

IBM Watson Analytics is a cloud-based business intelligence and analytics tool designed to aid organizations in making more informed decisions based on the results of analyses performed on their data. Natural language processing (NLP), predictive analytics (PA), and text mining are only some of the AI and ML methods used by Watson Analytics. Watson Analytics can be utilized to analyze semi-structured data found in documents and webpages and unstructured data found in social media and sensor data. Watson Analytics can also aggregate and analyze data from many sources to reveal previously unseen patterns. IBM Watson Analytics is a potent resource for organizations looking to understand their data better and make informed decisions. The platform is adaptable and can be scaled to accommodate businesses of any size.


RapidMiner is an all-inclusive data science platform that streamlines the processes of developing, deploying, and managing data science projects. The data preparation, modeling, evaluation, and deployment tools it provides are useful for businesses of all sizes. RapidMiner’s visual workflow designer is one of its most appealing features. For users without any coding skills, this makes it simple to design and share data science projects. You may save time and energy while creating projects with RapidMiner because of the extensive library of pre-built algorithms and machine learning models it provides. RapidMiner is a robust data science platform thanks to its many capabilities, including a visual workflow designer. Companies of various sizes utilize RapidMiner as their data science platform of choice. It’s a potent resource for creating, deploying, and overseeing data science initiatives of any size.


DataRobot is a web-based service that streamlining the entire machine learning model development and deployment procedure. It’s an effective method for firms of any size to improve their data-driven decision-making. DataRobot’s many useful features make it an excellent choice as an analytics platform. DataRobot streamlines the time-consuming steps involved in developing and releasing ML models. This allows organizations to construct models without the need to employ or educate data scientists. DataRobot is a simple platform that even those unfamiliar with machine learning may utilize. The platform’s drag-and-drop interface simplifies model development and deployment. Fraud detection, churn prediction, and product suggestions are just some of the many commercial uses for DataRobot.


KNIME is a free and open-source IDE for those interested in machine learning and data science. The users can construct data analysis and transformation procedures. Data cleaning and preparation, data exploration and visualization, machine learning model construction, predictive analytics, and dashboard generation are just some of the many uses for KNIME. In addition to data scientists and analysts, KNIME is also useful for engineers. A sizable group of people use and improve upon it, and it’s simple to pick up and utilize. A sizable KNIME user and developer base is available for assistance in case of problems. KNIME is a wonderful alternative if you need a data analytics solution that is both robust and versatile. A sizable group of people use and improve upon it, and it’s simple to pick up and utilize.


The powerful data analytics platform Alteryx is useful for businesses of all sizes. Some of its features are data mining, machine learning, and predictive analytics. Alteryx’s strength is its capability to automate mundane processes. This can improve the precision of a company’s data analysis and save a lot of time and money. Alteryx’s capacity to connect to external data sets is also strong. Thus, businesses can use Alteryx to analyze data from databases, spreadsheets, social media, and other unstructured sources. Alteryx is a flexible program that has many applications. It can help with things like optimizing marketing efforts, detecting fraud more accurately, and making more informed financial decisions, among other things.

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