Business Intelligence Techniques: Implementation for Telecity

This report provides an overview of various business intelligence (BI) techniques, including game theory, association rule learning, big data, process mining, machine learning, density estimation, and linear regression. The report focuses on selecting the best BI technique for Telecity, ultimately recommending density estimation. It explains the benefits of density estimation for analyzing the success and failure of business processes, facilitating effective decision-making, and improving productivity. The report also acknowledges potential drawbacks. The conclusion emphasizes the importance of BI in analyzing information for informed decision-making, highlighting the applicability of different techniques based on specific business needs. The report includes references to relevant academic sources.

Business Intelligence

Table of contents
 Introduction
 Potential techniques of business intelligence
 Implementation of one best suitable technique
 Conclusion
 References

Introduction
Business intelligence can be defined as combination of different types
of strategies and techniques which are used by entities for the purpose
of analysing the business data. With the help of it predictive, historical
and current views of the operational activities could be generated.
Main purpose of it, is to facilitate managers to formulate effective
decisions for upcoming period. This presentation is focused with
selection of best business intelligence technique for Telecity.

Potential techniques of business intelligence
 Game theory
 Association rule learning
 Big data
 Process mining
 Machine learning
 Density estimation
 Linear regression

Game theory
Game theory is a study of
mathematical techniques which are
highly focused with collaboration
between coherent decision makers.
There are various fields where it is
applied by the entities as well as
corporations. These are social science,
logic, computer and systems science.

Association rule learning
Association rule learning is a type of machine learning which is based
upon rules so that interesting relations among the variables in the big
data base could be discovered. It is mainly focused with identification
of strong rules which are discovered in data sets by using specific
measured of interestingness. All the rules that are part of this technique
are required to make sure that they satisfy user specified minimum
confidence as well as support at the same time. While using it
minimum support threshold is used for application to figure out the
frequent sets of items in data set

Big data
Big data technique could be defined as a
technique which is used for the purpose of
treating the ways in which information is
analysed, extracted and controlled in systematic
manner. With the help of it, complexities in the
analysis of data could be reduced as it takes large
data in to consideration so that accurate
judgements could be passed.

Process mining
Process mining can be defined as an
approach or technique which is used for
the purpose of improving, discovering
and monitoring the real processes in
analytical manner. While performing the
tasks with the help of this approach
knowledge is required to be extracted
form the event logs that are readily
available in the information system of
current era.

Machine learning
Machine learning can be defined as an
application which is highly focused with
artificial intelligence. It provides ability to
systems to learn and improve from experience
automatically without any help of explicit
programming. Machine learning pays attention
towards the development of various programs
for computers so that data could be accesses
and use the same for the purpose of learning.

Density estimation
This technique is mainly used in statistics and probability analysis. It is
the construction of the estimation which is based upon the collected or
observed information. All the tasks which are related to it are focused
with unobservable probability density function. With the help of it,
success or failure of all the business processes could be determined by
analysing the probability for both of them. It is a fundamental data
smoothing problem in which inferences regarding population are
formed and based upon the finite sample of information.

Linear regression
It is a business intelligence approach which is
used for the purpose of modelling the relation
between explanatory variables and scaler
response. If the variable is one then it will be
considered as the simple linear regression.
When the number of variables is more than one
then it will be known as multiple linear
regression

Implementation of one best suitable technique
From the discussion of different types of business intelligence techniques,
it has been analysed that the best suitable one for density estimation. With
the help of it, the organisation will be able to analyse the failure and
success of all the steps which will be taken by the managers. This
approach will facilitate the manages to analyse the processes which are
required to be focused for the purpose of reaching success and
predetermined objectives.

Continued…
It is the best suitable business intelligence technique which will benefit
Telecity to analyse the way in which it could attain success by
enhancing productivity with formulation of effective decisions. There
are various types of negative aspects which are also required to be
focused while implementing it. The artifacts in it are annoying which
may lead towards estimation of negative zones.

Conclusion
From the above project report, it has been concluded that business
intelligence is a technology which is highly focused with the process of
analysis of information so that effective decisions for betterment of
business in future could be formulated. There are various types of
techniques of it which could be used by companies according to their
requirements. These are game theory, density estimation, linear
regression, machine learning, big data, process mining etc.

References
 Larson, D. and Chang, V., 2016. A review and future direction of agile,
business intelligence, analytics and data science. International Journal of
Information Management. 36(5). pp.700-710.
 Trieu, V. H., 2017. Getting value from Business Intelligence systems: A
review and research agenda. Decision Support Systems. 93. pp.111-124.
 Laursen, G. H. and Thorlund, J., 2016. Business analytics for managers:
Taking business intelligence beyond reporting. John Wiley & Sons.
 Alpar, P. and Schulz, M., 2016. Self-service business intelligence. Business &
Information Systems Engineering. 58(2). pp.151-155.

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