Business Intelligence on Big Data: Know How Big Data Has Changed Business Intelligence by Using New Tools and Techniques

The business enterprises are now creating unique strategies that are now obtainable in business intelligence. Business Intelligence on big data can be accelerated by using proven techniques such as extraction transformation and load (ETL), dimensional modeling and ad hoc reporting and dashboards. 

These advanced techniques have surfaced from the constant demand to support the management reporting that was not usually available from transaction processing systems. Nowadays, business enterprises require more accurate, real time and in-depth reports, and are leveraging their Big Data for improved decision making. 

Here are a few key characteristics to keep in mind while developing a big data analysis strategy for your business.

What Makes Big Data Different?

Big data is usually described in terms of the diversity of sources, velocity and size of data. In several ways, large volume of data is the simplest of all to address. Deploy adequate commodity servers and storage with a suitable distributed file system such as big data Hadoop file system that allows you to easily accumulate and store petabytes of data. 

If your business enterprise has quickly changing data then, you should consider making use of a real-time and advanced big data analytics tool that can process the data in instantly while providing error tolerance and assured processing of your messages. 

If you are managing massive amount of different structured data types then, designing programs to work using this data is very easy. However, if you are dealing with unstructured data then, you will encounter major problems.  Natural language texts have a tendency of having domain-specific features, and the tools you build for analyzing customer emotions in online reviews might not be of much assistance in analyzing financial documents, identifying company names and geographic locations.

Business Intelligence on Big Data- Different Analysis Techniques:

Managing and analyzing big data is quite different from working with structured data extorted from the transaction processing systems. When companies commence a business intelligence project, they have an idea of the kind of data that they have to analyze. 

Users require certain standard reports which are adequately parameterized so that they can evaluate sales through products, sales personnel, sales areas and other aspects supported by the data warehouse. With conventional BI systems, companies usually commence with a driving reporting requirement. With big data analytics, companies can begin with a diverse fusion of data types that will uncover business insights that are not generally tracked by sales, stockists or the human resources systems. Big data sources also encompass social media, application log files and machine sensor data. 

Once business enterprises have recognized a group of potential customers that spend a large amount of time browsing, but never make a purchase, they must analyze their navigation patterns. This will help them personalize their offers to convert these prospects into customers, and help in creating cross-channel campaigns that encourage meaningful customer experiences.

Avoid believing in the headlines which assert that conventional BI techniques and tools are dead. The business intelligence on big data introduces new approaches of understanding business functions, by using the existing management reporting systems.