Advanced Analytics as the scaling of data
Advanced analytics solutions with embedded AI and machine learning enable you to analyze a variety of structured and unstructured IoT data sources.
Advanced Analytics is the autonomous examination of data or content using broad techniques and tools, beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.
Advanced analytics includes techniques such as data/text mining, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and complex event processing.
Analytics as a whole uses data and math to answer business questions, discover relationships, predict unknown outcomes and automate decisions.
Advanced analytics under the diverse field of computer science is used to find meaningful patterns in data and uncover new knowledge based on applied mathematics, statistics, predictive modeling and machine learning techniques.
When an enterprise chooses to implement Self-serve Advanced Analytics, it encourages user empowerment and user adoption. It also enables data sharing and allows the organization to produce fast, dependable insights and improve the value of business analysis across the enterprise, democratising the use of Advanced Analytics and augmented predictive tools among business users. As the business world discovers the benefits of smart data discovery, these tools have evolved, making it easier for business users and data scientists to gather, integrate and analyze data. According to Kartik Patel.
With faster and more powerful computers, opportunity arises for the use of analytics and artificial intelligence. Whether it’s determining credit risk, developing new medicines, finding more efficient ways to deliver products and services, uncovering cyberthreats or retaining the most valuable customers, analytics can help you understand what drives your organization’s success – and how it matters to the world around it.
”Every process is an analytics process ready for improvement. And every employee could be an analytics user in some way. No matter what you plan to accomplish with analytics, the first requirement for any analytics project is data. Once you have data, you need to analyze that data. And then you need to deploy the results of your analysis to drive decision making.”
References
www.sas.com/advanced analytics
www.wiki.org/advanced analytics
www.dataversity.net/advanced analytics
www.gartner.com/analytics insights
