The definition of Business Intelligence (BI) can be quite complex, but in general, it is a process of transforming raw data into meaningful and insightful information that can help businesses make better decisions. BI involves using various tools and techniques to collect, analyze and report on data that is relevant to the organization’s operations.
Why is BI Important?
There are many reasons why businesses should invest in BI solutions. Some of the key benefits include:
- Increased Efficiency and Productivity – BI can help organizations become more efficient by streamlining their processes and making it easier to access the right information at the right time. This leads to improved productivity as employees are able to work faster and more effectively.
- Improved Business Decision-Making – BI solutions allow businesses to make better decisions by providing access to accurate and timely information. This is especially helpful for companies who want to implement a data-driven decision-making culture across their organization.
- Competitive Advantage – Businesses that know how to use the insights gained from BI analysis have a competitive advantage over organizations that do not use BI effectively.
- Improved Customer Experience – By providing customers with personalized content and recommendations, organisations can improve the customer experience.
There are various other benefits which BI tools can offer.
EVOLUTION OF BUSINESS INTELLIGENCE
The history of business intelligence (BI) is fraught with cases of missed opportunities and wrong turns. It wasn’t until the early 1990s that organizations began to fully appreciate the potential of BI, and started to invest in systems and tools to make better use of data.
One of the key turning points in the history of BI was the development of decision support systems (DSS), which helped business users make better decisions by providing them with access to data and analytics. In the late 1970s, DSS started to gain traction as a way to help managers make more informed decisions.
In the 1980s, business intelligence started to move away from decision support systems and towards management information systems (MIS). MIS reporting gave business users more flexible canned kind of document reports with input parameters, exporting and downloading capabilities to name a few.
In the 1990s, business intelligence became corporate information management as organizations started looking for ways to make better use of data from all over their enterprises. In the mid-2000s, business intelligence evolved into what is now known as decision management or business analytics as new technologies allowed business analysts to generate customized reports and analyses that they could then share across lines of business or within an organization. BI tools also started providing early warning indicators based on key performance metrics and external market trends. This shift represents a fundamental in how organizations can innovate and differentiate
In 2000’s self service BI products became famous and started maturing allowing simple drag drop interface to create analysis. Then lately there have been tools which are using Natural Language Processing (NLP) engines to allow ask questions from the data but with limited success, atleast as of now.
TYPES OF BUSINESS INTELLIGENCE TOOLS
There are various kind of Business Intelligence tools which are there in the market.
There are open source Business Intelligence tools like Helical Insight, Jaspersoft, Pentaho etc. They generally have free community version and paid enterprise version but generally they are extremely cost competitive as compared to proprietary BI tools.
The second type of tool’s are licensed Business Intelligence tools. The popular ones among them are Tableau, Microsoft BI and SAS. They offer a wide range of features but come with a hefty price tag.
These BI tools can be deployed on premise or on cloud. There are certain BI products which
Then these BI tools can also offer in-memory mode or extract mode and the second mode is when they can run directly on the live data. When it is in-memory mode the entire data from the db can be loaded into the BI memory which gives very high performance but on the flip side the hardware requirement is very high. Whereas when querying the live data the hardware requirement is not high but it can give performance issues with more data.