Business Analytics identify business performance from various perspectives. It includes customer insight, market trends, competitive positioning, and financial performance. The term “business analytics” is more often than not used in place of “business intelligence” or “business measurement”. Business Analytics refers to both the analytical skills techniques, and processes for continuous, real-time exploration and measurement of past performance to achieve insight and drive strategic business planning.

Analytics is a methodology that uses data to support strategic decision making by providing insights into what is currently working and what could be improved. Data is the key to understanding business goals and objectives. Therefore, data integration is a major component of business analytics. Successful companies use analytical skills and combine business intelligence and analytics to support strategic direction. Some of the best businesses around the world are using data and analytics to make strategic decisions, while others are applying the methodologies to drive incremental improvements in specific processes.

Data-driven decision making is essential to the success of every organization. The ability to collect, analyze, and communicate data is critical. A successful business analytics analyst must be skilled at collecting, managing, and communicating important data. As an individual professional, an Analytics Professional should have knowledge of data visualization tools and data mining techniques. He should also be aware of the current trends in data management, including cloud computing, mobile devices, and social networks.

Business Analysts from many backgrounds are applying Analytics methods and techniques across many industries. There are two main perspectives in analytics: marketing/ qualitative and business process/ quantitative. Marketing-phenomenological analysis looks at what consumers buy and why, how they buy it, what makes them buy it, how others buy it, and why they buy it again. Qualitative analysis looks at things that people want to buy such as products, services, and other information that most employees or executives cannot readily explain.

Another perspective is predictive analytics, which looks at what customers/clients/customer segments buy and why. These segments vary widely by product, price, target market, and industry. The main goal of predictive analytics is to provide organizations with the insight they need to increase operational efficiency. Examples include finding patterns in product use, segment trends, competitor activity, and trends in customer preferences.

Most business leaders believe that their BPO strategy should be integrated with Analytics. However, not all businesses are comfortable with having a single platform for strategic business decisions. As a result, a growing number of companies are developing their own proprietary analytics tools and reporting systems. While this is helpful to smaller organizations, business leaders feel that these systems often miss the mark and do not provide insight needed to drive their business operations. They argue that a large organization has a number of disparate systems and metrics that need to be analyzed and that an in-house system is simply too limiting.

In addition to trying to find a common platform, professionals in business analytics also want their reports and dashboards to be tightly integrated. This includes integration across all functional areas of the organization, such as human resources, sales, marketing, information technology, and accounting. This gives managers and other key decision makers a well-rounded view of organizational performance. It also helps ensure that business decisions are made based on data-driven and actionable information rather than on assumptions or vague generalities.

Finally, many professionals believe that the future of business analytics is in data mining. Data mining is the process of mining data to uncover previously unknown or hidden information. One of the greatest advantages to data mining is that it is typically fast and inexpensive. Many business leaders are already using data mining to improve their businesses. In particular, some of the more popular advanced analytics tools being used today are predictive maintenance, consumer panel, enterprise resource planning, and event-triggered marketing, just to name a few.