Semi-automatic and autonomous content and data are examined using sophisticated data analysis tools and advanced analytics techniques that work on Big Data sets. The size of the analysis is beyond traditional business intelligence analytics. The advanced data analytics process is used to generate predictions, recommendations, insight etc., directed towards making data-driven decisions for business and process strategies. Any advanced form of analytics means using maths/ statistical analysis with data to discover relationships and patterns, answer business questions, predict outcomes that are unknown and make decisions for process automation answering to what is advanced analytics.
There are 2 web advanced analytics categories, namely-
The 4 types of analytics, depending on the advanced-analytics meaning/requirements and work-flow, may be associated with the 4 capabilities of Gartner’s model, namely diagnostic, descriptive, prescriptive and predictive advanced-analytics techniques.
Advanced-analytics division can also be made based on the function and form divisions. For Ex: A consultancy firm’s end-to-end, ongoing process may have divisions for
Other advanced-analytics divisions may be based on technological improvements and include data process mining, augmented analytics, graph analytics, etc. By data mining, one means the crucial process using advanced-analytics and an automated method to get meaningful and usable information from Big Data with huge sets of raw data. As in the case of advanced-analytics, big data analytics is beneficial in creating connections, discovering patterns, finding insights, etc., between data sets and data points. The raw data is basically unstructured and source dependent. Hence it has to be cleaned and then used with predictive analytics to provide meaningful/usable relationships and insight into customer behaviour, future trends, etc.
In this article let us look at:
Advanced analytics can exist across several forms and is the umbrella form for high-level advanced analytics tools and methods used in data analytics to get meaning and usable insights from the data collected. This is achieved using the predictive abilities of analytics to forecast user behaviour, trends, events etc. Advanced-analytics use models that are based on advanced statistical algorithms that provide what is known as ‘what-if’ calculations predicting the future aspects and operational abilities that help in drawing up business strategies and process automation based on the data-driven and calculated predictions.
Advanced-analytics is also closely associated with a huge host of other branches using such algorithms as artificial intelligence, machine learning, graph analysis, semantic analysis, text mining, data mining, pattern matching, complex event processing, predictive analytics, sentiment analysis, data visualization, cluster analysis, analysis of networks, multivariate statistics, neural networks, simulation, and far more.
Advanced analytics is a broad term associated with various disciplines and is used in these fields for various purposes. Marketing departments make excellent use-cases. Advanced-analytics helps analyse consumer preferences recorded through its tools to semi-automatically compute, understand and decipher future targets, plan campaigns, draw up business strategies etc. Warehouse and inventory managers can use the benefit of advanced analytics using advanced business analytics tools where the study and analysis of past orders, present sales and future goals can be extrapolated to build the best ways of reducing waste, improving production lines, building a better process and so on.
Supply chain analytics is again a huge field that uses advanced-analytics and RFID technology to keep track of inventory, resources, logistics et,c when computing the best strategies and models for improvement. Organizations involved in the manufacture, service sector enterprises, social-media, fraud detection, security systems, e-Commerce platforms, weather and risk management solutions and a host of other smart applications can also use advanced analytics to drive their performances.
The Sisense Business Intelligence and advanced analytics platform is a great example of an advanced analytics tools list used by organizational teams, systems and departments, which use advanced analytics on complex Big Data and convert such data into easy-to-use dashboard models. Sisense’s algorithms connect disparate and unstructured data into meaningful data creating a real-time accurate overview of the enterprise’s whole gamut of operations. They are superbly fast and can be used for their prescriptive, predictive, analytical, descriptive and diagnostic abilities generating accurate and actionable predictions that are reliable too.
Advanced Analytics and its tools provide accurate, reliable, and fast predictive analytics in various situations across a majority of organizations that are data-driven and use the analysis for their predictability solutions used to make informed and data-driven decisions in advanced analytics examples. The data volumes in these cases generally consist of unstructured/ structured/semi-structured data from tera to zeta bytes in size, collected from multiple varieties of sources meaning raw data needs to be cleaned up before it is used as meaningful data. Advanced analytics thus has multiple uses and is used even in advanced fields of emerging sciences like artificial intelligence, neural networks, machine learning and more.
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