Analytics projects begin with data preparation. Teams must access many sources of data to solve complex problems, including data from third parties. Raw data is rarely clean or fully accurate – as such, teams must transform it so it’s useful in machine learning applications and helps users make good business decisions. Altair data preparation software connects seamlessly to virtually every data source and can handle any data format, from PDFs to spreadsheets to big data repositories. Use Altair’s intuitive workflows, automation functions, and pre-built data models to generate clean, governed, and trusted datasets.
It’s no secret that high quality data is critical to every data analytics project. Business users and people with limited data science training can begin using Altair’s no-code, visual interface in just minutes. Additionally, users can connect to all their data sources and cleanse, blend, and transform difficult, incomplete, and/or “dirty” data into trusted and accurate datasets within a governed environment.
Organizations must operationalize large amounts of disparate data to solve complex problems. To keep up with demand, teams must be able to automate data transformation tasks and standardize report formats quickly and easily. Altair’s data preparation tools make it easy to store and retrieve reports quickly and integrate data transformation workflows with enterprise content management (ECM) and robotic processing automation (RPA) platforms.
Data confined within silos hampers decision-making and causes inefficiencies. Our collaborative data transformation and machine learning tools empower business and data analytics teams to work together in secure, governed environments. Sharing accurate and trusted data streamlines cross-functional analysis and increases the quality and diversity of analytics deliverables.
Auditors are under significant pressure to keep expenditures down whether they work for an external audit firm or are part of an internal audit team. Achieving cost-effective audits requires organizations to do more with less - while maintaining or increasing audit quality. To succeed auditors not only need the right expertise and process but also the right data analytics tools.