Advanced analytics is a broad category of inquiry that can be used to help drive changes and improvements in business practices.
While the traditional analytical tools that comprise basic business intelligence (BI) examine historical data, tools for advanced analytics focus on forecasting future events and behaviours, allowing businesses to conduct what-if analyses to predict the effects of potential changes in business strategies. Predictive analytics, data mining, big data analytics, and location intelligence are just some of the analytical categories that fall under the heading of advanced analytics. These technologies are widely used in different industries.
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.
Forecasting helps us to know the future. It also helps us to compare, to estimate
and to analyse the data to arrive at the estimated results. It leads to regular investigation of different aspects of production and management within and outside the organisation. Forecasting prepares a ground to work together and brings better co-ordination, co-operation and control in the organisation.
Business forecasting is a process used to estimate or predict future patterns. Executives, managers and analysts use the forecasted results to aid in making better-informed business decisions. For instance, business forecasts are used to estimate quarterly sales, inventory levels, supply chain re-orders, website traffic and risk exposure. While business forecasting is usually achieved by using statistical techniques, data mining has also proved to be a useful tool for businesses with much historical data.
Simulation & Optimization
Simulation and optimization entirely focus on providing value to you and your business that that models, simulates, predicts, and visualizes systems for various industry. It helps to optimize current and planned processes, identify and decrease waste, flow control, reduce cost, and increase revenue.
Simulate your processes while you are developing them to understand how well those process models might perform. Simulations using estimates that you provide for staffing levels, activity execution times, and so on. Simulating your processes during development enables you to test and refine process designs before implementation. Analyze your processes after they are up and running using historical data stored. You can measure actual execution, wait, and other times as well as track the values of specific business point as they move through each step in a process.
Text analytics is particularly useful for information retrieval, pattern recognition, tagging, context extraction, sentiment evaluation, and predictive analysis. It offers a way to understand what your customers think of your business, product or service, or highlight the issues that your customers raised about most frequently.
The value of text analytics is amplified when both structured and text data are combined, and to this end text mining technologies are witnessing significant uptake. In this scenario text data are converted into a form where they can be merged with structured data from transactional systems and are then scrutinized by data mining technologies, whose sole purpose is to uncover hidden structure in data and reveal exploitable patterns.