Enterprise Business Intelligence Guide: Key Components and Implementation Process
By leveraging business intelligence (BI), enterprises can derive actionable discoveries and make informed decisions that drive them ahead of the competition.
According to Gartnerโs
market research
, about 80% of the businesses surveyed use BI and data analytics software. Industries leading the charge in adoption encompass distribution/inventory management, marketing, advertising, engineering, insurance, and IT services.
Gartner researchers also predict that by 2025, the BI software market will reach a value of $13 billion. There are various factors that drive enterprise business intelligence quick adoption, including the imperative to enhance data precision and uniformity, manage risks adeptly, and identify novel opportunities for revenue creation.
In this article, weโll provide you with all the necessary details about enterprise BI, covering its capabilities, benefits, and industry-specific use cases.
Gartner researchers also predict that by 2025, the BI software market will reach a value of $13 billion. There are various factors that drive enterprise business intelligence quick adoption, including the imperative to enhance data precision and uniformity, manage risks adeptly, and identify novel opportunities for revenue creation.
In this article, weโll provide you with all the necessary details about enterprise BI, covering its capabilities, benefits, and industry-specific use cases.
What is enterprise business intelligence?
Enterprise business intelligence represents a set of strategies and technologies for data collection, storing, and analysis from various company divisions.
Every day enterprises generate a plethora of data, necessitating the need to analyze this data to derive actionable insights and remain competitive. Enterprise business intelligence stands as a pivotal tool for such organizations, fostering enhanced productivity and operational efficiency.
Enterprise business intelligence workflow
A typical enterprise business intelligence workflow consists of 6 main steps, including:
Collecting data from CRM, ERP, and other external data sources.
Pulling data from various source systems and making it accessible for further processing.
Processing extracted data with the help of Extract, Transform, Load (ETL).
Storing data in the data warehouse.
Using BI tools for data analysis, data mining, and the creation of dashboards and reports.
Generating outputs such as dashboards and reports that visualize and summarize the analyzed data.
BI Implementation : Microsoft Azure Synapse Analytics
Aim & Objective
In this project we deploy famous Microsoft Adventure Works 2019 data on Azure SQL Cloud Database using Azure Data Studio and Create a Business Report using Azure BI ArchitectureObjective:
Efforts: Define Business Metrics (KPI) and developed SQL Custom Queries
-- FactView SQL Query[GITHUB Link]
-- Retention SQL Query[GITHUB Link]
Website Analytics Using Google Analytics (GA4) and Data Studio
Aim & Objective
In this project we analyze Track Website Traffic and Building Enagement Dashboard On Google Looker Data StudioObjective:
