To begin with, Business Intelligence (BI) refers to gaining insight over the fundamental data of the business to answer the basic question of “what” and “how” something happens within the organization. It works upon exploring new business vogues and figures and understanding the past as well the current functioning of the organization.  

On the other hand, analytics deems upon using all the data collected through BI for analyzing and predicting further steps. Unlike BI, analytics doesn’t look in the past but towards future outcomes. Also, analytics toils to answer “why” something is happening within an organization.   

For instance, while playing chess, when our opponent makes a move, we use BI to figure out what move our opponent has played, and how did he use it? However, we need analytics to make sense of our counter move, which would bring us closer to winning the game.  

Today, every organization is dependent upon its valuable data for growth and future development. If used correctly, data can make wonders happen for organizations.  

BI and Analytics are data management solutions to utilize data from the past to predict the future. However, it is impossible to achieve precision in these solutions without deploying a robust BI and Analytics solution. 

Artificial Intelligence (AI) is crucial for gathering data productively, prompting it in a constructive and cost-efficient manner, and ensuring time efficiency to achieve an effective and robust BI and Analytics solution.


Key Requisites for Effective and Robust BI and Analytics
Solutions

AI gives new impetus to data management in the structure of Machine Learning (ML), as it speeds up the pace of the solution’s process and engenders profundity. ML is responsible for finding multiple sets of data through various patterns and algorithms other than mere programs directed towards achieving a single goal.  

Overall, BI and Analytics engines are successful in achieving productivity and speed through the deployment of AI. However, at the same time, consider the following requirements for successful and effective BI and Analytics solutions and engines. These include; 

Using Graphic Processing Units 

Organizations are unable to optimally utilize the mammoth amount of data existing and revolving within the organization. Many companies deploy BI solutions in traditional databases and Central Processing Unit (CPU) for processing their data from BI. Often, these solutions are coined as real-time, but it is too slow for the hardware to process it without heavy delays.  

The traditional databases use batch processing, which uses a non-uniform process to resolve and run data queries. Thus, making it hard for the computational power and hardware to cope up with the solutions. Time is considered to be a major factor in the collection and usage of data.  

Organizations can deal with the slow pace of extraction and gain insight over the data through deploying Graphics Processing Units (GPU) which use a parallel processing method to grasp and process data. These involve one of the most powerful chips for processing data without run-time errors. Thus, making it the fundamental element for a robust solution, as it can manage to cope up with the pace.  

Using In-Memory Database 

CPU uses external data storage disks and devices as it is unable to keep heavy information within itself. Through using external disks and devices for storage, the speed of the extraction is relatively slower in response. However, GPU is capable of using In-Memory storage and performs quicker responses through data streaming and without delays.  

Integrating Machine Learning into Data Science 

Machine learning is capable of dealing with a huge data set, with numerous algorithms working simultaneously to speed up the process. The more data ML is fed, the better it works, as it increases its understanding of the subject involving deep learning, neural networks, regression, etc. Furthermore, Data science teams find it impossible to beat ML in its speed and accuracy.  

After introducing ML, organizations set apart the data science team for BI from ML. However, separating the teams doesn’t work well for the organization, as they use different hardware. Thus, it is best to use an integrated platform with ML and BI working in conjunction. 

Involving Data Streaming 

GPU and AI can together achieve a robust BI and Analytics solution which actually works upon accomplishing real-time data insight through using data streaming. This is possible through parallel scrutinizing of data combined with AI. 

Using SQL Support  

A robust BI and analytics engine must provide SQL support for organizations through an in-built SQL support system. SQL lays down the foundation for the traditional databases. A software that can support Relational Database Management Systems (RDBMS) will be able to assist multiple teams within the organization to utilize and access data collectively.