What is Augmented Analytics ?
Augmented analytics and augmented data management are two related but distinct concepts in the field of data analytics.
Augmented analytics refers to the use of machine learning and other artificial intelligence (AI) technologies to automate and enhance the process of data analysis. The goal of augmented analytics is to make it easier for non-experts to analyze data and gain insights, by automating many of the time-consuming and complex tasks involved in the data analysis process. This can include things like data preparation, data modeling, and even the identification of relevant insights and trends.
Augmented data management, on the other hand, refers to the use of AI and other advanced technologies to automate and improve the process of data management. This can include tasks like data integration, data quality management, and data governance, among others. The goal of augmented data management is to make it easier and more efficient for organizations to manage large volumes of data, while also ensuring that the data is accurate, reliable, and secure.
Both augmented analytics and augmented data management are becoming increasingly important as the volume of data that organizations generate and collect continues to grow. By automating many of the tasks involved in data analysis and data management, these technologies can help organizations to make better use of their data, identify new insights and opportunities, and gain a competitive advantage in their respective industries.