Data Analytics and Artificial Intelligence (AI)
Data is unlike any other asset your organization owns. It never wears out, it never drains, and it can be used repeatedly. But the value in data is not in having it, it’s in how you use it. Altair enables data-driven enterprises by providing teams the power to use data analytics and AI to gain competitive advantages and drive next-level business results.
Operationalize Your Data and AI
To better understand your processes, customers, and products, your team must collaboratively generate and share data-driven insights across the organization. Our solutions are designed for many different skill sets: from data scientists and engineers to MLOps specialists to business analysts to executives. With a no-code, cloud-ready interface, we deliver the powerful capabilities organizations need to harness the full power of data analytics and AI throughout the complete data pipeline.
Altair Data Analytics enables organizations to operationalize data analytics and AI with secure, governed, and scalable strategies.
Combine Programming Languages
Write data-centric applications using the best programming languages for the job, mixing syntax from different languages inside a single program. Our code and no-code tools empower you to create, maintain and run models and programs using Python, R, SQL, and SAS languages.
Organizations that have invested many man-years in developing IP using SAS language use our tools to continue to maintain and run their existing SAS language programs without the need for any other third-party products.
Data Transformation
An industry leader with more than 30 years of experience in data discovery and transformation, Altair offers the fastest and easiest way to extract data from difficult, semi-structured data like PDFs, spreadsheets and text files, as well as from Big Data and other structured sources. Whether data is on premises or in the cloud, Altair can automate preparation tasks and transform your data into accurate and clean datasets in seconds – rather than hours or days – freeing you up to spend time on value-add activities, not on mundane, repetitive and error-prone tasks.
Predictive Analytics and Machine Learning
Altair’s machine learning and AI solutions quickly get to the granular, low-latency data that contain the insights you are trying to uncover. Delivering transparency and automation with features such as AutoML and Explainable AI, we streamline model building so more time can be spent analyzing and results can be trusted. Our flexible no-code approach doesn’t restrict how models are configured and tuned, giving you control over model building. With our support for popular, open-source languages and engines, you can integrate new models built using Altair into your existing analytics infrastructure.
Data Visualization and Stream Processing
Spot anomalies, trends and outliers in seconds with real-time data, and share results across the organization using rich, powerful dashboards. Our stream processing and data visualization solutions are built for people who need to make fast, fully informed decisions based on massive amounts of fast-changing telemetry, sensor, and trading data.
AI-powered Design
Advancements in the fields of AI and machine learning (ML), combined with the increased availability of robust simulation, testing, and field data sets has made engineering data science a critical component of the modern product development lifecycle. Computer-aided engineering (CAE) augmented by AI is offering manufacturers the ability to discover ML-guided insights, explore new solutions to complex design problems through physics and AI-driven workflows, and achieve greater product innovation through collaboration and design convergence.
Featured Resources
Guide to Using Data Analytics to Prevent Financial Fraud
Financial fraud takes countless forms and involves many different aspects of business including; insurance and government benefit claims, retail returns, credit card purchases, under and misreporting of tax information, and mortgage and consumer loan applications. Combating fraud requires technologies and business processes that are flexible in their construct, can be understood by all who are involved in fraud prevention, and are agile enough to adapt to new attacks without needing to be rebuilt from scratch. Armed with advanced data analytics, firms and government agencies can identify the subtle sequences and associations in massive amounts of data to identify trends, patterns, anomalies, and exceptions within financial transaction data. Specialists can use this insight to concentrate their attention on the cases that are most likely fraud. This guide will help you understand the complex environment of financial fraud and how to identify and combat it effectively.
Harnessing the Power of Big Data, AI and Simulation to Accelerate Product Innovation
In a world where everything is becoming more and more connected, Mabe, a leader in home appliances, is leveraging the convergence of big data, analytics and simulation to accelerate innovation. Martin Ortega, Senior Design Engineer at Mabe, explains how they are using Altair’s AI, data analytics and simulation solutions to uncover insights, create new business opportunities, and advance product development. Learn more - click here to read how connected products deliver big ROI.
Guide to Using Altair® RapidMiner® to Estimate and Visualize Electric Vehicle Adoption
Data drives vital elements of our society, and the ability to capture, interpret, and leverage critical data is one of Altair’s core differentiators. While Altair’s data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation, and securities trading, they’re also useful in a variety of more common business intelligence applications, too. Explore how machine learning drives EV adoption insights - click here. An Altair team undertook a project utilizing Altair Knowledge Studio® machine learning (ML) software and Altair Panopticon™ data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level. This guide explains the team’s findings and the process they used to arrive at their conclusions.
Make Machine Learning Work for You
Protecting consumers and enterprises involved in online transactions is just one example of how machine learning (ML) influences our daily lives. In fact, the list of use cases is already long, diverse – and growing fast. The reason is clear – ML is a game-changing tool that enables organizations to make better decisions faster. What’s more, ML is highly effective at balancing conflicting objectives.
Given the breadth and depth of potential use cases, one thing is clear – more and more people will find themselves working in environments where ML plays a critical role. And thanks to the emergence of low-code and no-code software, ML is no longer the exclusive preserve of programmers, data scientists, and people who paid attention in math class. More of us can – and will – be involved in developing and deploying practical ML solutions.
This eGuide will help you understand the key concepts behind ML, some common applications, and how ML becoming more useful to people at all levels of the modern organization.
