Business Intelligence
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Business Intelligence: Tools, Techniques, and Applications, High school final essays of Computer science
A bunch of cycles models and innovations that change crude information into significant data that drives beneficial business activities are called Business Intelligence. Business Intelligence was gotten from the possibility that supervisors with erroneous or deficient data will in general settle on more awful choices on normal than if they would be wise to data. Changing information into significant insight and information is a set-up of programming
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Business Intelligence
Learning Outcomes
By the end of this unit students will be able to:
LO1. Discuss business processes and the mechanisms used to support business
decision-making.
LO2. Compare the tools and technologies associated with business intelligence
functionality.
LO3. Demonstrate the use of business intelligence tools and technologies.
LO4. Discuss the impact of business intelligence tools and technologies for effective
decision-making purposes and the legal/regulatory context in which they are used.
Why do organizations need business intelligence?
Business intelligence comprises of information that contains relationships, patterns, trends
about customers, business employers and employees.
BI system process, stores and provides useful information as per the requirement.
Measurement - creating key performance indicators based on historic data.
Identify and set benchmarks for varied processes.
With BI system organizations can identify market trends and spot business problems that need to
be addressed.
Helps on data visualization that enhances the data quality and thereby the quality of decision
making
BI system can be used not just by enterprises but small and medium enterprises
Business intelligence systems and tools
Business intelligence system is an information system that employs business intelligence tools to
produce and deliver information.
BI tools are computer programs that implement a particular BI technique. The techniques are
categorized into three ways:
- Reporting tools
- Data – mining tools
- Knowledge-management tools
Business intelligence systems implemented
Step 1: Raw Data from corporate databases is extracted. The data could be spread across multiple
systems heterogeneous systems.
Step 2: The data is cleaned and transformed into the data warehouse.
Step 3: Using BI system the user can ask quires, request ad-hoc reports or conduct any other analysis
Example
A hotel owner uses BI analytical applications to gather statistical information regarding average
occupancy and room rate.
It helps to find aggregate revenue generated per room.
It also collects statistics on market share and data from customer surveys from each hotel to
decides its competitive position in various markets.
By analyzing these trends year by year, month by month and day by day helps management to
offer discounts on room rentals.
Descriptive Analysis working
Business metrics are decided.
The data required is identified.
The data is collected and prepared.
The data is analyzed.
The data is presented.
Examples of descriptive analytics
Summarizing past events such as sales and operations data or marketing campaigns.
Social media usage and engagement data such as Instagram or Facebook likes.
Reporting general trends.
Collating survey results.
Predictive analytics working
Predictive analytics is based on probabilities.
Using a variety of techniques – such as data mining, statistical modelling (mathematical
relationships between variables to predict outcomes)
machine learning algorithms (classification, regression and clustering techniques) – predictive
analytics attempts to forecast possible future outcomes and the likelihood of those events.
Predictive analytics working
To make predictions, machine learning algorithms, for example, take existing data and attempt
to fill in the missing data with the best possible guesses.
- A newer branch of machine learning is deep learning, which, according to Cornerstone
Performance Management, mimics the construction of ‘human neural networks as layers of
nodes that learn a specific process area but are networked together into an overall prediction.’
Deep learning examples include credit scoring using social and environmental analysis and
sorting digital medical images such as X-rays to automate predictions for doctors to use when
diagnosing patients
Typical Data mining applications
Data mining techniques are a blend of statistics and mathematics, and artificial intelligence and
machine learning.
Because data mining is an odd blend of terms from different disciplines which is also known as
knowledge discovery in databases.
Data mining techniques
Unsupervised data-mining characteristics:
No model or hypothesis exists before running the analysis.
Analysts apply data mining techniques and then observe the result.
Cluster analysis
Supervised data-mining characteristics:
Analysts develop a model prior to their analysis
Statistical techniques to estimate parameters of a model
Regression technique
Neural networks predict values and make classifications
Business Intelligence Platforms
Tableau
Tableau is one of the most popular Business Intelligence platforms that is used by businesses to
get an idea of their operations using data analysis.
Tableau is very famous as it can connect to multiple data sources and also produce detailed data
visualizations in a very short time.
In fact, Tableau also allows its users to prepare, clean, and format their data and then create
data visualizations that can be used by businesses to obtain actionable insights.
Business Intelligence Platforms
Qlik Sense
Quik Sense is a Business Intelligence platform that allows decision-makers in businesses to use
any BI use cases and find out unique insights into their data.
Quik Sense can help companies in becoming data-driven entities by providing data analytics
services and Artificial Intelligence systems on a cloud architecture if they want.
Corporate employees can easily load and visualize their data on Qlik Sense, no matter its size.
All the data charts, tables, and other visualizations that can be created on Qlik Sense are totally
interactive and they can automatically update themselves based on the changing data context.
Quik Sense is also very intuitive, and professionals can easily create data analytics using just
drag and drop.