Business Intelligence with Big Data

Business Intelligence (BI) is sometimes used interchangeably with the consultation of books, reports, interrogative tools, and structural information systems. It involves a wide range of applications and methodologies, which enables an organization to gather data, analyze it, develop queries, and create reports against it. Data visualization produces results that are taken into consideration to perform business-related relations. The tools for Business Intelligence (BI) are decisive to useful measurement through the use of crucial presentation indicators and metrics across all levels of a business.

1. What is Big Data
2. Why we need BI in Big Data
3. How Business Intelligence Systems get Implemented on Big Data?
4. Conclusion

What is Big Data

Big Data is enough to explain what it does. A large amount of organized or disorganized data, which could be in raw form and yet growing exponentially with time, is also referred to as complex data. Complex data is not easy to process with the traditional methodologies and Data Management Tools. A couple of examples for complex data could be as.

Social Media: The figures show that more than 500+ Terabytes of new data get gulped by Twitter/Facebook every day in the form of Pictures, Videos, Messages, and Attachments.

Tracking Metropolitan Cameras: According to the research, almost 300+ Terabytes of data are processed by Tracking Cameras in the form of pictures with the help of Image processing Algorithms in the Capital of China.

Jet Engine: A single Jet Engine generates more than 10 TeraBytes data in approximately 30 initial minutes of its take-off time. If we calculate all the data of a month of an Airport, it could range up to Exabytes.

The idea of Big data evolves around 5 V’s.

Volume

Organizations collect data from different platforms, including business transactions, smart devices, equipment, social media, etc. In the early ’80s, storing it would have been a problem. But with the advent of nano storage devices, the volumes of data are storable up to an unimaginative extent.

Velocity

Velocity refers to the availability and diversity of where the collected data is stored. Velocity is said to be more critical than Volume as the data have to be available at the right time to make the right decisions at the right time.

Variety

Variety refers to the variability of sources from data was extracted, whether it is in an organized or disorganized form. These data can have many faces, with diverse values. It could be numeric data in an unstructured Database or text documents and transactions

Veracity

Veracity is said to be the accuracy and quality of the data. As per the researches on the datasets, different link and matches are difficult to relate and merge it into useful information.

Value

With infinite options and tactics, the Value of data is an ongoing process, whether it is structured or available in amorphous form. Information is only as valuable as the business outcomes as it makes possible on how we make use of data, which allows us to fully recognize its real significance and potential to improve our decision-making. It’s the most crucial standpoint of all the V’s.

Why we need BI in Big Data

As Big Data is the object or entity, Business Intelligence is the process to extract useful information from it. Bi uses the extraction of information with the help of relevant tools, applications, and methodologies from external to internal resources and unify it for analysis. Queries are executed over the analyzed data to make reports and charts to present the result for business users. Business Intelligence (BI) is being used as a front end showcasing the data analyzed and managed by Big Data with a better User-interface (UI).

Tools

Below are some tools used for Business Intelligence and Big data to assemble, analyze, and make Analytical plans. The Big Data tools are there to store a large amount of data to process them and get intuitions.

How Business Intelligence Systems get Implemented on Big Data?

• Raw Data from commercial databases get extracted. The data could extend across several systems.
• The data gets scoured and transformed into the data warehouse in the form of tables. A table can tend to link up with each other based on relationships post suitable processes.
• Using the Business Intelligence (BI) system, the user can query the result set, request ad-hoc reports, or conduct post-analysis.

Conclusion

Both Business Intelligence (BI) and Big Data’s primary objective is to aid the business to make the right decisions by analyzing the infinite datasets. Another purpose is to increase the business flow by optimizing the cost and risks. Business Intelligence (BI) helps in finding the resolutions to the business demands we know, whereas Big Data allows us to find the answers to the question that are not visible to the naked eye.

Business Intelligence BI solutions are more headed for structured data, whereas Big Data tools can route and analyze data in various formats, both structured and amorphous. Business intelligence (BI) and Big Data need to be coordinated and synced together to aim for higher results. They both have their purposes, but they share mutual goals. A lot of the discrepancies between Business intelligence and Big Data tend to be arbitrary. If used appropriately, the businesses can make sure their decision power will not only increase but will also be accurate as per the available analyzed data.