Business intelligence is a software-driven process
that enables organizations to analyze raw data
from multiple sources and discover insights that
lead to more effective business decisions.
What do I need to know about business intelligence?
Although the term "business intelligence" describes
a methodology and a class of business software,
the primary activity in the field of business
intelligence is data analysis. Business intelligence
tools and applications correlate and process data
about business performance to determine the best
course of action for various business functions.
Why do you need business intelligence?
Business Intelligence (BI) is based on the principle
that the best decisions are the wise ones. Before the
advent of data analytics, companies could only
answer the simplest questions about business
performance. Gaining more granular, complex or
cross-functional insights is either impossible, or
prohibitively expensive and time-consuming.
BI technologies address this issue by allowing data
from multiple sources to be correlated to provide a
more comprehensive understanding of the drivers
of business performance.
Who uses business intelligence?
Traditionally delivered to executives and managers
in the form of reports or dashboards, business
intelligence is increasingly accessed by employees
in many roles across organizations to help them
perform their jobs more effectively. Business
intelligence can be delivered in standalone
applications, integrated into custom applications,
or increasingly via intranet or mobile applications.
Business intelligence systemsOLAP
Online Analytical Processing (OLAP) is a system
that allows users to analyze data from different
sources while providing multiple models or
perspectives. OLAP component databases use a
multidimensional data model and support complex
analysis and ad hoc queries. Standard uses of
OLAP include:
⠁Commercial sales reports
⠁marketing
⠁Management report
⠁Business Process Management (BPM)
⠁budget and forecast
⠁Financial reporting and similar areas
⠁New applications, such as agriculture
OLAP has become very popular due to the variety
of ways in which information is collected and
organized. As a SQL-based software, it fell out of
favor when NoSQL became popular. (Currently,
some companies like Kyvos Insights and AtScale
have put OLAP on top of NoSQL.) OLAP supports
three basic operations:
⠁to merge
⠁to cut
⠁appetizers
Consolidation is the set of data that can be stored
and processed in different ways. For example, a
car sales manager could aggregate the sales of all
the cars in a store to forecast sales trends. On the
other hand, drilling support technologies, detailed
navigation and search. People can view car sales
by color, model, or gas mileage. Slicing and
slicing allows users to take specific data from an
OLAP cube (slices) and view those slices from
different angles
(sometimes called dimensions, as in "multi-
dimensional").
Executive Information System (EIS)
In the late 1970s, CEOs began using the Internet to
search for business information. This has led to the
development of software known as Executive
Information Systems (EIS) to assist senior
management in making decisions. EIS is designed
to provide relevant and up-to-date information
needed to "streamline" the decision-making
process. When presenting information, the system
emphasizes graphical representation and an easy-
to-use interface. The goal of EIS is to turn
executives into "hands on" users who can manage
email, search, appointments and read reports
themselves rather than going through an
intermediary to get this information. EIS is losing
popularity due to its limited usefulness.
Database
Data warehouses became increasingly popular in
the 1980s when companies started using in-house
data analysis solutions on a regular basis. (Due to
the limitations of computer systems at the time,
this was usually done after 5 pm and on
weekends.) Before data stores, a lot of redundancy
was required to provide useful information to
different people in the decision-making process. A
data warehouse can significantly reduce the time it
takes to access data. Data that was traditionally
stored in multiple locations (often in departmental
warehouses) can now be stored in one location.
The use of data warehouses also promotes the
development of big data applications. Suddenly,
vast amounts of data in multiple forms (email,
web, Facebook, Twitter, etc.) can be accessed from
a single repository, saving time and money in
accessing previously inaccessible business
information. The potential of a data warehouse for
data-driven insights is enormous. These insights
increase profits, uncover fraud and minimize
losses.
Business intelligence becomes high tech
Business Intelligence (BI) as a technical concept
emerged shortly after the 1988 conference of the
International Consortium for Multiplexed Data
Analysis in Rome. The conclusions from this
session advance efforts to simplify BI analysis
while making it more user-friendly. Many BI
companies were formed based on the conclusions
of the conference, and each new company offered a
new BI tool. Back then, BI had two basic
functions: creating data and reports, and
organizing and visualizing them in an attractive
way.
In the late 1990s and early 2000s, BI services
began to provide simplified tools to make decision
makers more independent. These tools are easier to
use, provide the required functionality and are very
efficient. Business people can now gather data and
gain insights by consuming it directly.
Business Intelligence and Analytics
These two terms are currently used
interchangeably. Both describe common practices
for using data to make informed, informed
business decisions. The term business intelligence
has evolved to refer to a group of technologies that
provide useful insights. Instead, analytics
represents the tools and processes that can
transform raw data into actionable, useful
information for decision-making purposes. Various
forms of analytics have been developed, including
real-time streaming analytics.
predictive analysis
Predictive analytics are used to predict the future.
This type of analysis uses statistical data to
provide companies with useful insights about
impending changes, e.g. identifying sales trends
and buying patterns and predicting customer
behavior. Business applications of predictive
analytics often include predicting year-end sales
growth, which products customers are likely to
buy at the same time, and predicting inventory
levels. An example of this type of analysis is credit
scoring, which is used by financial service
providers to determine how likely a customer is to
make payments on time.
Prescriptive Analysis
Prescriptive analytics is a relatively new field, but
it can still be a bit difficult to work with. This type
of analysis "prescribes" a range of possible actions
and leads one to a solution. Prescriptive analytics
are used to make recommendations. Essentially, it
predicts multiple future scenarios and allows
organizations to assess many possible outcomes
based on their actions. At its best, prescriptive
analytics can predict what will happen, why it will
happen, and make recommendations. Larger
companies have used prescriptive analytics to
successfully optimize planning, revenue streams,
and inventory levels to improve customer
experience.
flow analysis
Stream analytics is real-time data processing. It is
designed to continuously calculate, monitor,
manage and immediately respond to data-based
statistics. The process involves recognizing and
responding to certain situations as they arise.
Streaming analytics greatly improves the
development and use of business intelligence.
Data for streaming analytics can come from a
variety of sources, including mobile phones,
Internet of Things (IoT), market data, transactions,
and mobile devices (tablets, laptops). It will
manage connections to external data sources and
allow applications to quickly and efficiently
combine and incorporate data into application
streams, or update external databases with
processed information. Stream analysis is
supported:
⠁Minimize losses from social media meltdowns, security breaches, plane crashes, manufacturing defects, stock market crashes,lost customers, and more.
⠁Real-time analysis of daily business processes
⠁Finding Missed Opportunities Using Big Data
⠁Create opportunities for new business models, revenue streams and product innovations
Some examples of streaming data include social
media feeds, real-time stock trading, up-to-the-
minute retail inventory management, or ride-
sharing applications. For example, when a
customer calls Lyft, the data streams are combined
to create a seamless user experience. The app
brings together real-time location tracking, prices,
traffic statistics and real-time traffic data to
provide customers with the nearest available
drivers, prices and estimated time of arrival at their
destination based on historical and real-time data.
Stream analytics has become an extremely useful
tool for both short-term coordination and long-
term business intelligence development.
Current Business Intelligence
Business intelligence requires more than simple
performance metrics. It requires weather forecasts,
demographics, and economic and social insights to
provide a broad information base for predicting the
future. Real-world events affect business
intelligence and the decisions based on it. Some
recent developments that provide useful
information include:
Internet of Things (IoT): It is used to receive data
from various devices ranging from manufacturing
to mobile phones. Office buildings,
communication equipment, trucks, office
equipment—they all transmit data as part of the
Internet of Things.
Automation supports business intelligence: Many
companies still rely on manual processes to
support their business intelligence. Automated
services make fewer mistakes than humans and
provide higher quality data. These services
facilitate better business intelligence.
Analytics has gone mainstream: More and more
companies are using three current types of
business intelligence—descriptive, predictive, and
prescriptive. Descriptive analytics provides most
of the business intelligence, while predictive
analytics analyzes historical data to predict the
future. Prescriptive analytics attempt to predict
future outcomes, but also provide
recommendations based on the predictions.
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