Business Analytics vs Business Intelligence

The business analytics emphasizes on formulating new ideas and opinion of business operation using statistics and data methods that can be used to make business decisions and optimize business processes.

It has to do with the technologies, practices and skills used for uninterrupted continuous investigation of past performance of businesses to drive business planning and gain business perception.

Quality data, and qualified skilled analysts with an understanding of the technologies and what it takes to make data-driven decisions are the platform which makes effective business analytics. It supports planned decision making in unforeseen circumstances which is to support real-time response.

In business analytics, when the goal of the analysis is known, an analysis procedure is selected and data is obtained to support the analysis.

The process of obtaining the data includes extraction from a business system or more, cleansing, and merging into a single storage space such as a data warehouse.

Analysis is usually performed against a small sample of data. The tools vary from spreadsheets with statistical duties to complicated data mining and predictive modeling applications.

Decision making is driven by broad statistical analysis, predictive modeling and fact based management, and examples of business analytics include data exploration to find new relationships i.e. data mining, statistical analysis, quantitative analysis, testing, multivariate testing, predictive modeling, and predictive analysis.

On the other hand, business intelligence is a set of skills and tools used to gain and convert raw data into information which are usable for the purpose of business analysis.

Business intelligence provides past, present and futuristic views of business activities. It is also used to support a vast array of business decisions from operational to strategic, of which operational decisions include product positioning and pricing while strategic decisions consist of priorities, goals and directions at the widest level.

In business intelligence, the term data surfacing typically is associated with functionality, and some of these functionalities include handling large unstructured data useful in identifying, developing and creating new strategic business opportunities.

The common functions of business intelligence are prescriptive analytics, data mining, business performance management, text mining, predictive analytics, bench marking, and complex event processing and so on.

It is more effective when it makes use of data gotten from the market where a company operates (known as external data) combined with data obtained from sources inside the company such as financial and operational data or internal data.

The components of business intelligence are multidimensional allocation and aggregation, real-time with analytical alert, denormalization, tagging and standardization, interfacing with unstructured data sources, statistical inference, open item management etc.

In summary, business analytics seeks to answer questions like why did it happen? Will it happen again? What will happen if we change it? What else does the data tell us that never thought to ask? While business intelligence answers also seeks to answer questions like what happened? When? Who? How many? Some business intelligence application technicians are incorporating business analytics functionality in their products.