What is Business Intelligence?
Business Intelligence is the set
of processes, technologies, and tools that help an organization to transform
raw data into meaningful and useful information for business analysis
(Identifying business needs and determining solutions to business problems).
What is the need for Business Intelligence?
Below are some of the major
benefits of Business Intelligence in any Organization.
·
Get deeper insights in business operations, Identifying new
opportunities and implementing an effective strategy based on insights can
provide businesses with a competitive market advantage and long-term stability
·
Business Intelligence Provides historical, current and predictive
views of business operations.
·
Sales and marketing – Understanding the
profitability of customer segments and answers to valuable questions like,
o Which customers should an
organization target?
o Which are my most profitable
campaigns per region?
o What is the most profitable
source of sales leads and how has that changed over time?
·
Improve Productivity and efficiency
·
Informed decision making
·
Improve Customer Service and satisfaction
·
Streamline budgeting and planning
·
Financial decisions based on results for important
questions like
o What is the full cost of new
products?
o How are forecasts trending
against the annual plan?
o What are the current trends in
cash flow, accounts payable and accounts receivable and how do they compare
with plan?
·
Overall business performance tracking based on
o What are the most important risk
factors impacting the company’s ability to meet annual profit goals?
o Should we expand internationally
and, if so, which geographic areas should we first target?
Common Functions
of Business Intelligence are
·
Reporting
·
OLAP (Online Analytical Processing)
·
Data Mining
·
Process mining
·
Complex event processing
·
Business performance management
·
Text mining, predictive analytics and prescriptive analytics.
Stages of BI
Below are the five stages of Big
data Business Intelligence in any organization.
·
Data Sourcing – Defining the data to be loaded into the system.
Usually BI applications gathers data from a data warehouse (Data marts,
OLTP or OLAP).
·
ETL (Extract Transform Load) – Extracting the source data and
transforming per business rules and loading into the Data Warehouses.
·
Data Warehousing – Storing transformed data into various Data
warehouses types and making it available for business analysis.
·
Data Analysis – Applying various techniques like data mining, text
mining, Process mining to identify trends and patterns in business
operations.
·
Decision Making – Based on the reports, dashboards and alerts from
previous stage, making valuable business decisions and bench marking future
growth.
Data Warehousing
What is Data
Warehousing?
The process of extracting and
transforming internal and external data into useful business information
and loading it into a central database so that it can be explored by business
users across the company is known as Data warehousing.
What is a Data
Warehouse?
A data warehouse is a relational
database that is designed for query and analysis. Enterprise Data
warehouses store current and historical data and are used for creating trending
reports for business management like annual and quarterly comparison reports.
Business Intelligence and Data warehousing architecture
Below is the typical Business
Intelligence and Data warehousing platform architecture.
Data Warehouse(DW or DWH) Types
·
Data Marts – As shown in the above architecture, a data mart is a simple form
of a data warehouse that is focused on a single functional area, like sales,
finance or marketing.
·
Online Analytical Processing (OLAP) – OLAP databases store
aggregated, historical data in multi-dimensional schemas. OLAP systems
typically have data latency of a few hours, as opposed to data marts, where
latency is expected to be closer to one day. Mainly used for Reporting
and allows complex analytical and ad-hoc queries
·
Online Transaction Processing (OLTP) – OLTP systems support
online transactions like INSERT, SELECT, UPDATE, DELETE within fraction of
seconds. OLTP is mainly aimed at fast response, simplicity and efficiency but
not for reporting purpose.
Below is the high level
comparison chart between data warehouse types.
Function
|
OLTP
|
OLAP
|
Data Marts
|
Operation
|
INSERT,
UPDATE, SELECT
|
Complex
Queries
|
Report
Generation
|
Latency
|
Within
Seconds
|
Within
Hours
|
Within
Days
|
Analytical
Requirements |
Low
|
High
|
Medium
|
Age of
Data
|
Current
|
Historical,
current
and projected |
Historical
and
Current |
Business
Events
|
React
|
Predict
|
Anticipate
|
But to handle Big data, the above
regular data marts are not capable and Hadoop (HDFS, Hive, HBase) plays
the role of OLAP data ware house type in typical Big data Business Analysis
using Hadoop. Below is the Hadoop Perspective of the Data warehousing
architecture.
