One of the greatest potentials of AI – as we know – is data analysis. Platforms and tools for this purpose, if directed without infringing rights, bring advantages in multiple fields. The functions involved in fact concern reporting, data organization and management, textual analysis and much more. All of which can be automated, entirely for some processes or partially for others, in order to save time and effort.
So there are many digital professions affected by this list, because they can take inspiration... let's see if you find something useful. Meanwhile, for each platform we provide you with a practical example.
Content index
Data analysis for business intelligence and reporting
- Microsoft Power BI: Import data from almost any source, create custom visualizations, and share secure reports. It integrates advanced AI features, in particular a copilot that assists in writing and explaining DAX queries, improving accuracy and speed. With this platform an analyst can create detailed reports with complex visualizations in minutes, saving hours of work.
- Looker: real-time analytics, advanced data modeling, live connections to over 35 SQL databases, self-service analytics for creating custom visualizations, and embedded analytics for integrations with other software solutions. A marketing team can quickly view and analyze data from ongoing campaigns, making changes in real time and optimizing strategies based on immediate insights, without delays or the need to extract data manually.
- Instrument: advanced interactive visualizations, dynamic reporting and dashboards, AI integration for predictive and generative insights, data modeling with support for multi-fact relationships, and real-time data exploration and analysis. Particularly Einstein, the available AI copilot, helps in creating visualizations and calculations using natural language, reducing analysis times and improving the accuracy of results. With this tools a sales team can monitor key metrics and receive proactive insights via email or Slack, optimizing decisions without having to manually build each report.
Team and project management
- Julius A.I.: with this you can interpret, analyze and visualize data using natural language commands. Key features include creating charts, reports, and predictive models. A special feature of Julius AI is the intuitive chat interface that allows users to gain insights directly from data. A marketer can analyze the performance of an advertising campaign and obtain graphs and trends in minutes, saving hours of manual processing.
- DataLab: A collaborative platform that supports advanced data analytics with a notebook-like interface, integrating SQL, Python and R. Its main functions include live report generation and real-time updates. Particularly important is the ability to create and share updated reports immediately with the team, eliminating the need to manually transfer information. For example, a development team can monitor the performance metrics of an application, updating reports in real time during the development cycle.
- Databricks: big data cluster management and collaborative notebook support. Its main functions include efficient data processing and collaboration on data engineering projects. With this, an e-commerce can analyze large volumes of customer data, optimizing marketing strategies based on rapid and detailed analysis of purchasing trends.
Data analysis with AI and machine learning
- Google Cloud AutoML: Platform that enables the creation of custom machine learning models without the need for advanced skills. Its key features include a drag-and-drop interface, support for various data types, and integration with the Google Cloud ecosystem. An e-commerce store can create a personalized recommendation model in just a few hours, improving customers' shopping experience and increasing sales.
- Microsoft Azure AI: Offers a complete suite of cloud-based AI services for building, training, and deploying ML models at scale. Its main features include integration with Microsoft tools such as Power BI and Dynamics 365, and support for several programming languages. An IT team can develop a demand forecasting model, optimizing inventory management and reducing costs.
- OpenAI: Mainly offers GPT-4, accessible via API for integration into custom applications. In this case, the generative AI, so well known only for texts, is transformed into a tool that allows a business to integrate GPT-4 into customer support, improving service and reducing operating costs thanks to accurate and personalized automatic responses.
Data visualization and dashboards
- Polymer: Data visualization and a wide range of tools to create interactive charts and dashboards. Its key features include connection to over 20 data sources, automatic dashboard generation, and AI integration to provide data analysis insights and explanations. A marketing team can analyze campaign performance and obtain detailed graphs, improving strategic decisions without the need for advanced technical skills.
- sisense: Allows you to combine complex data into unified visualizations, including interactive dashboards. Integrates numerous data sources and uses advanced analysis tools. A manufacturing company can monitor production metrics in real time, improving operational efficiency and reducing downtime thanks to constantly updated dashboards.
- Qlik: Data visualization that facilitates visual analysis and detailed reporting. It is possible to integrate different data sources, use associative analysis and create interactive dashboards. A financial services company can analyze customer transactions and identify trends and anomalies, improving safety and regulatory compliance.
Textual analysis and data extraction
- MonkeyLearn: use theartificial intelligence to extract data from texts such as emails, surveys and social media posts. It allows you to use pre-built or custom models for text classification, sentiment analysis and entity extraction. With this you can create machine learning models without the need for advanced technical skills. A business can use MonkeyLearn to analyze customer reviews on social media, identifying sentiment trends and recurring feedback to improve products and marketing campaigns.
- SAS Text Miner: An advanced tool for analyzing text data. Its main functions include automatic document classification, sentiment analysis, entity extraction and trend prediction. A special feature of SAS Text Miner is the ability to handle large volumes of textual data with high precision. A market research company can analyze thousands of product reviews, identifying emerging trends and customer preferences to improve product strategies.
- Rapid Miner: This also offers powerful tools for text analysis and includes document classification, keyword extraction and sentiment analysis. Any business can analyze social media comments, quickly identifying customer concerns and improving the services offered.