Welcome to the third post in this Business Intelligence for business series. In this series, we discuss several aspects of Business Intelligence and its implications on the business. If you have not read the first article on this series, please follow the links below.
Now let’s dive into today’s topic. Big data and BI
Business Intelligence (BI) landscape is changing quite visibly. BI is evolving, driven by Big Data, Cloud and Advanced Analytics.
The combination of these technologies and methods has unveiled the possibility of invoking new use cases that could not even have been considered before.
In the era of Big Data, businesses have a great challenge ahead: Generating more and more value from their data. Organizations gather huge amounts of information, and they do it at such a speed that their analysis involves more and more difficulties. The objective is not only to analyze the data but to separate the useful data from that which is not.
In order to proceed further into Big data and BI, let's understand the traditional BI architecture. A traditional BI architecture comprises of:
Extract-Transform-Load (ETL) a tool that periodically integrates data from structured data sources – databases or CSV/Excel files – and transforms and reorganizes the data
BI data model made of dimension and fact tables, suited for efficient reporting
Data Warehouse (a relational database - RDBMS) that stores the data transformed by the ETL tool
The reporting tool that leverages the data in the Data Warehouse by using the underlying SQL engine to create dynamic visualizations that are organized into reports and dashboards for business users.
Big Data, as its name suggests, is all about data the challenges Big Data set out to address are:
To capture and store such large amounts of data efficiently
To analyze that data so that the enterprise can ferret out a better understanding of its own operations or what its customers want and how it is addressing those needs.
To collect huge amounts of data and support the processing and analysis directly in a secure fashion
To enable enterprises, sift through the data, ask important questions and visualize the results.
To reduce the delays and latency so analysis can be incorporated into the operations of the enterprise.
Now If we integrated all the above-mentioned uses of Big data into a BI architecture and upgraded our Data warehouse / DB technology to leverage the above-mentioned optimizations, we would have the architecture that’s best of both worlds i.e. BI and Big Data.
Here’s how enterprises can turn Big data and BI integration into a business advantage.
Integrating Big data and business intelligence solution can assist retail organizations to pull out essential information from a chunk of data, by addressing the question at hand. It can provide definite answers to questions like how much of a product is being sold and the type of client that’s buying the product as well as the store or outlet that sold the product, and the specific time.
BI and big data allow operations managers to have a detailed summary of the operations, so they can eliminate any bottlenecks and enhance efficiency. The access to real-time information also enables finance managers to take care of traditionally narrow margins of profit with greater context to make sure they enjoy maximum gains from investment into inventory.
Having access to real-time consumer demand pattern information allows enterprises to match their inventory to their orders accurately, which results in customer satisfaction. Consumer analysis can also help predict seasonal spikes, trends, and depression.
Now that the concept of Big Data has had time to evolve, enterprise decision-makers no longer must feel like they’re on their own and that there are no maps, no established roads, and no guides. Our solution experts at PsiberTech solution can guide you about the Big data and BI integration efforts in your organization.