Business Analytics

Business analytics is simply the combination of business intelligence and analytics. Such a combination reflects the increased importance of quantitative analysis of data for understanding, prediction, and optimization. Business analytics can be defined as the broad use of data and quantitative analysis for decision-making within organizations. It includes analytics, of course, but involves harnessing them to meet defined business objectives. Business analytics empowers people in the organization to make better decisions, improve processes and achieve desired outcomes. It brings together the best of data management analytic methods, and the presentation of results all in a closedloop cycle for continuous learning and improvement. Despite the name, business analytics is not restricted to privatesector, profitseeking businesses. The meaning of business here is that of an immediate task or objective, with analytics being a means to achieve that objective. The effective use of business analytics within organizations may require some changes from the activities and organizational structures that supported business intelligence. The field of business analytics has improved significantly over the last few years, providing business users with better insights, particularly from operational data stored in transactional systems. The effective use of business analytics requires considerable resources, so organizations that employ them will need to prioritize what data, strategic targets, and decisions are most important to the organization. The key consumer of these analytics is the business user, a person whose job is not directly related to analytics perse (e.g., a merchandiser, marketer, salesperson), but who typically must use analytical tools to improve the results of a business process along one or more dimensions (e.g., profit, time to market). Fortunately, data mining, analytic applications, and business intelligence systems are now being better integrated with transactional systems creating a closed loop between operations and analyses that allows data to be analyzed faster and the analysis results to be quickly reflected in business actions. Analytics are now regularly used in multiple areas, including sales, marketing, supply chain optimization, and fraud detection