Top 10 AI Analytics Platforms for 2023

H20.ai is an open-source machine-learning platform that offers a variety of features for data preparation, modeling, and deployment. It is a good choice for businesses that want to build and deploy machine learning models at scale due to its expansive selection of custom plugins and algorithms that give businesses more flexibility and control over their analytics processes.

Unlike most other AI analytics tools, H20.ai is open-source. This makes it a great platform for businesses with a budget, since it's it’s free to use and modify and can be easily adjusted to meet business needs. H20.ai also offers an open-source machine learning platform, H2O, and an automated machine learning tool called Driverless AI. It's known for its speed and scalability in building models for predictive analytics.

WatsonX is IBM’s enterprise-grade AI and data analytics platform providing tool designs to expand the impact of AI across your business. The platform is made up of several powerful products, including the watsonx.ai studio for new foundation models, generative AI and machine learning, the watsonx.data fit-for-purpose data store, and the Watsonx.governance toolkit to accelerate AI workflows that are built with responsibility, transparency and explainability.

Watson X supports various programming languages, including Python and R, and offers pre-built machine-learning models to jumpstart your analytics projects. It also stands out for its data fabric approach, user-friendly interface and expansive selection of features, giving you the versatility to make confident data-driven decisions through real-time insights, business analytics tools, forecasts, data analytics and BI tools to increase profitability and drive outcomes across key metrics. 

KNIME is an open-source data analytics platform that provides an intuitive environment for complex analytical processes. It provides a wide range of AI and machine learning capabilities through a modular workflow system, making it highly adaptable to various data analytics needs.

Data experts can leverage the intuitive, drag-and-drop interface to efficiently design, train, and apply machine learning models. With KNIME, solving a problem with the right AI/ML method no longer requires users to code in the language of that library. Users can reach out to Keras for Deep Learning, H2O, XGBoost, or many other AI/ML libraries directly from any KNIME workflow to quickly explore their options.

Talend is an open-source data integration platform that offers a variety of features for data preparation, cleansing, and transformation. It provides a comprehensive selection of tools and functionalities that facilitate the process of data integration, data quality, data governance, and data preparation. Built on a unified platform, Talend empowers organizations to leverage their data effectively, enabling them to make informed business decisions. Analysts can gain instant insight into what data to trust and the diagnosis and tools for fixing the data that doesn’t measure up.

Talend has been named a leader in Data integration tools for seven years in a row and stands out for its AI integration capabilities. Integrating and preprocessing data, its platform uses AI to uncover hidden patterns, make accurate predictions, and gain valuable insights for business optimisation.

RapidMiner is an enterprise-ready data science platform that provides a wide range of AI and machine learning tools for data preparation, modelling, and deployment. RapidMiner’s project-based framework allows organisations to structure and organise all the work that’s associated with a given use case so that their team can easily refer back to it – and share it with others within your organization when they’re solving similar problems.

It allows organisations to gain value out of data no matter how their team is structured or how mature their data science efforts are, making it a one-stop solution for companies looking for AI-fulled data analytics tools. It also offers full automation for non-coding domain experts, an integrated JypyterLab environment for data scientists, and a drag-and-drop design tool that allows teams to work together on data projects. 

Microsoft's Power BI is an industry-leading data visualisation tool with AI-powered features that allow you to explore and visualise data effectively. Power BI is rich in AI features. One that stands out for its incredible Smart Data Discovery tool, which enables the platform to automatically discover and visualise your data. The tool can also be integrated into other Microsoft platforms like Teams to analyse and visualise employee activity. 

Another notable AI feature in Power BI is its Natural Language Query (NLQ), which allows users to type in a question about their data and have Power BI answer your query using natural language – like ChatGPT. This Q&A format allows you to get quick answers and insights about your data, leading to new discoveries, faster analytics processes, and increased productivity. 

MonkeyLearn is an incredible no-code AI platform that allows users to easily analyse and visualise through machine learning. The platform comes with a host of different AI data analytics tools that can turn data into powerful insights. The MonkeyLearn dashboard also allows users to create intuitive visualisations, and custom charts, and filter data by multiple inputs. Users can classify texts by topic, sentiment, and intent or extract specific data such as keywords, names, and even companies

Whether it's sentiment analysis or topic classification, MonkeyLearn offers a huge selection of pre-built classifiers and extractors ready to use. The tool also comes with multiple integrations that work together with whichever tools are already being used and a simple and intuitive UI to match the simplicity of the platform.

Polymer is a powerful AI tool able to transform large amounts of data into a simple and easy-to-manage database without the complicated setup or learning curves. Polymer’s AI analyses data suggests rich insights and builds beautiful dashboards automatically. With this powerful AI, organisations can create visualisations, build dashboards and present their data without having to lift a finger. 

Polymer achieves all of this without a long onboarding process. All a user has to do is upload their spreadsheet to the platform to instantly transform it into a streamlined database filled with actionable insights and useful information. This makes it one of the easiest-to-use AI tools on this list, allowing professionals from a variety of industries to make the most out of their data. 

Databricks’unified analytics platform combines AI, data engineering, and data science. It allows teams to collaborate on big data projects and utilize AI for advanced analytics. With Databricks as their Unified Data Analytics Platform, companies can quickly prepare and clean data at a massive scale with no limitations. 

The platform also enables companies to continuously train and deploy ML models for all of their AI applications, making AI much more achievable for enterprise organizations and enabling them to accelerate their AI initiatives. Databricks also make it easier for companies to build data pipelines across various siloed data storage systems and to prepare labelled datasets for model building, allowing organizations to use AI to analyze existing data and massive data sets.

Our number one spot goes to Tableau – an analytics and data visualisation platform with an industry-leading suite of AI tools that allow organisations to bring data to life. Trusted by more than a million active users, Tableau uses AI, ML and other statistical tools to lower the barrier to data. It doesn’t require any knowledge of coding to use, allowing users to seamlessly create reports and share them across multiple platforms of their choice without spending hours analysing complicated datasets. It can also be run on the cloud or on-premise, making it readily available for businesses of all shapes and sizes. 

Tableau also recently launched Tableau GPT, which automates data analysis, preparation, and governance, while also providing users with helpful guidance and contextual instructions so that users can understand how prediction and insights are surfaced. It continues to invest big in AI analytical capabilities, with the goal of empowering users to get answers and uncover unanticipated insights in seconds through AI and machine learning.