The task of data visualisation may be made simple by the application of artificial intelligence systems for data analysis. Homogeneous data analysis across all inputs is made possible by AI. AI can predict how your ads will perform and even proactively provide insights to ensure you have all the facts because of its lack of human bias and capacity to analyse enormous volumes of data at breakneck speed.
You can use this to review and manage your data even if you are not a data scientist. After all, using AI technologies allows you to analyse your data without having to spend time and money creating algorithms.
Data analysis can be done with a variety of artificial intelligence (AI) techniques. Some of the best tools are listed below:
Tableau: You can use Tableau, a well-known business intelligence and data visualisation application, to help you analyse, understand, and convey your data. Tableau allows you to create interactive dashboards and visualisations, connect to a wide variety of data sources, and share your insights with others.
Polymer: By controlling your spreadsheet with just a few clicks, Polymer’s potent AI enables you to explore your data and improve your understanding of it. There is no drawn-out onboarding process or training videos to view. Simply send your spreadsheet to Polymer, and it will be transformed into a well-organized database.
Power BI: By using Microsoft Power BI, a business intelligence and data visualisation tool, you can more effectively comprehend, communicate, and analyse your data. A variety of tools and capabilities are offered, making it simple to connect to data sources, build interactive dashboards and reports, and share your insights with others.
Splunk: Splunk is a helpful tool for collecting, processing, and displaying machine data. Large amounts of data may be analysed and indexed in real-time, enabling rapid searches and analyses of the data to spot trends and find answers.
RapidMiner: The robust data mining and machine learning platform RapidMiner enables the development of prediction models and data analysis. Even if you are new to data mining and machine learning, you may start using it right away because it is made to be user-friendly.
Developers and analysts may examine and present their data with Sisense, a new data analytics tool. The platform is easy to use, with lots of drag-and-drop tools and a dynamic user interface that streamlines the procedure. Sisense also provides consumers with access to their “In-Chip” technology, allowing them to select between RAM and CPU while processing huge datasets. This quickens computation.
KNIME: You may analyse, transform, and model data with this robust open-source data analytics tool (Konstanz Information Miner). You may create unique data pipelines and workflows using its user-friendly interface and flexible design for a variety of data analysis jobs.
Apache Spark is an open-source platform for distributed computing that handles and analyses large datasets. You may develop and distribute data pipelines and apps that expand to handle petabyte-sized data thanks to its rapid and simple design.
QlikView: Data analysis, comprehension, and communication are made easier by the business intelligence and data visualisation software QlikView. Even if you’ve never performed data analysis before, you can use it right away because it’s made to be user-friendly.
Talend: Using Talend’s big data and real-time data processing capabilities, a wide variety of data types may be managed and analysed. Utilizing big data technologies like Pig, Hive, or Mahout, you may process and analyse data on a variety of big data platforms, like Hadoop, Spark, or Hive.
