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Business Intelligence for Data SCience Students
1. Business Intelligence
Dr. M. Kriushanth
Assistant Professor
Department of Data Science
St. Joseph’s College (Autonomous)
Tiruchirappalli – 620 002
2. Features of BI
Task of BI:
• The main task of BI is providing decision support for specific goals
defined in the context of business activities in different domain areas
taking into account the organizational and institutional framework.
Foundation of BI:
• BI decision support mainly relies on empirical information based on
data. Besides this empirical background, BI also uses different types of
knowledge and theories for information generation.
Realization of BI:
• The decision support has to be realized as a system using the actual
capabilities in information and communication technologies (ICT).
Delivery of BI:
• A BI system has to deliver information at the right time to the right
people in an appropriate form.
3. Actual Challenges of BI
Tasks of BI:
• Now a days we can find a well-structured understanding of the business
logic in almost all domain areas. This new understanding has also led to
a process-oriented conceptual view, which integrates workflow
considerations and process mining into BI. Another aspect is that new
organizational structures like decentralized organizations want to apply
decision support within their environment, and, hence, ideas from
collective intelligence or crowd sourcing are applied in BI.
Foundations of BI:
• Besides the traditional Datawarehouse, we also have to take into
account data on the Web. Such data is often not well-structured, but
only semi structured such as text data. The need to integrate different
data useful for decision support in a coherent way has led to models for
linking data in BI. In connection with such new data, the scope of
analytical methods has broadened and new tools such as visual mining,
text mining, opinion mining, or social network analysis have emerged.
4. Realization of BI systems:
• Today’s software architectures allow interesting new
realizations of BI systems. From a user perspective, Software
as a Service (SaaS) constitutes an interesting development for
BI systems. From a computational point of view, we have to
deal with large and complex datasets nowadays. Moreover,
cloud computing and distributed computing are important
concepts opening new opportunities for BI applications.
Delivery of BI:
• Mobile devices offer a new dimension for delivering
information to users in real-time. However, these
developments have to take into account that quality of real-
time information is a new challenge for BI.
5. BI Scenarios
1. Business intelligence separated from strategic management:
• In this case BI is mainly concerned with the achievement of
short-term targets in a division of an organization, for
example, a department of an enterprise or a clinic in a
hospital. Typically, results of the BI application are more or
less standardized reports for a dedicated part of the business.
2. BI supports monitoring of strategy performance:
• Such a BI application is motivated by overall strategic goals
and formulated in accordance with these goals. Monitoring of
the performance is done by defining measurable targets. A
data warehouse allowing a unified view onto the business is
usually a prerequisite for such an application scenario.
6. 3. BI feedback on strategy formulation:
• This application goes one step beyond the previous strategy
and aims at an evaluation of the performance using analytical
methods. In the best case, such an application can be used
for the optimization of a strategy. A typical end-product in this
scenario may be a balanced scorecard.
4. BI as strategic resource:
• This strategy uses the information generated by BI not only
for optimization but also as an essential input for the
definition of the strategy at the management level. Typical
examples are customer-based marketing or development of
standard operation procedures for patient treatment.
7. BI Views on Business Process
• Event view – Time stamp for the start
• State view – attribute
• Cross-sectional view – History, event and variables
8. Task and Analysis Format
• Data Task
• Business and Data Understanding Task
• Modeling Task
• Analysis Task
• Reporting
• Analysis Format
15. Data Extraction
• Data extraction is the process of collecting or retrieving
disparate types of data from a variety of sources, many of
which may be poorly organized or completely unstructured.
Data extraction makes it possible to consolidate, process, and
refine data so that it can be stored in a centralized location in
order to be transformed. These locations may be on-site,
cloud-based, or a hybrid of the two.
• Data extraction is the first step in both ETL (extract,
transform, load) and ELT (extract, load, transform) processes.
ETL/ELT are themselves part of a complete data integration
strategy.
16. ETL process as a whole
• In essence, ETL allows companies and organizations to 1)
consolidate data from different sources into a centralized
location and 2) assimilate different types of data into a
common format. There are three steps in the ETL process:
• Extraction: Data is taken from one or more sources or
systems. The extraction locates and identifies relevant data,
then prepares it for processing or transformation. Extraction
allows many different kinds of data to be combined and
ultimately mined for business intelligence.
17. ETL process as a whole
• Transformation: Once the data has been successfully
extracted, it is ready to be refined. During the transformation
phase, data is sorted, organized, and cleansed. For example,
duplicate entries will be deleted, missing values removed or
enriched, and audits will be performed to produce data that is
reliable, consistent, and usable.
• Loading: The transformed, high quality data is then delivered
to a single, unified target location for storage and analysis.
