Data preparation is the process of collecting, cleaning, and consolidating data. Analysts use self-service tools like Xcalar Data Platform to transition raw data source inputs into prepared outputs for analysis and various other business purposes.
Data science is a multidisciplinary blend of data inference, algorithm development, and technology for solving analytically complex problems. Data scientists use tools like Xcalar to automate methods that build and run models to extract knowledge from massive amounts of data.
Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling, and machine-learning techniques for finding meaningful patterns and knowledge in recorded data. Analysts use tools like Xcalar to find actionable insights from real-time and historical data in unstructured, structured, and semi-structured file formats.
Financial services companies compete in a rapidly changing global landscape. With real-time, interactive modelling of complex problems, they can better meet regulatory constraints and combat fraud. The ability to be one step ahead of the market, deliver the products your customers want, and detect potential bad actors is critical to survival.
Technology companies must quickly and accurately analyze contextual data from online services, devices, and social media to improve their offerings and stay ahead of their market. By integrating actionable insights into business operations, technology companies streamline operations, deliver higher quality and more relevant products, and reduce waste.