Business Intelligence and Data Warehousing

Running head: MANAGEMNT 1
Business Intelligence and Data Warehousing
Student Name
Institution

MANAGEMENT 2
Executive summary
Business intelligence and data warehousing are two important business analytics that
enables the business to understand its operations and customer for effective decision making.
The paper presents the application of various data analysis techniques in manufacturing, banking
and transport industries. Some of the identified techniques include content analysis, business
intelligence analytics, data exploration, and discovery, planning, and forecasting, predictive
analytics, data warehousing, information integration and governance, text analytics, text mining,
business intelligence data mining. In the transport industry business intelligence and data mining
is used for planning, predicting and designing transport systems or routes for effective traffic
flow. The impact of business intelligence application in the transport industry is the expansion
and traffic planning that enable free flow of traffic, especially during peak seasons. Banking
industry uses data analysis for understanding customer transactions, purchase power,
determining creditworthiness and payment of insurance services. The banking industry has
utilized the business intelligence and data mining techniques to design customer service and
products such as credit determination and insurance payment. Manufacturing utilizes chunk data
to understand customer taste, customer demand related factors and target market for designing
and manufacturing of products. The paper concludes by identifying the importance of business
intelligence and data mining techniques in current business settings.

MANAGEMENT 3
Table of Contents
Introduction.................................................................................................................................................3
1. Transport industry...............................................................................................................................3
1.2 Content analytics...............................................................................................................................4
1.3 Data discovery and exploration.........................................................................................................4
1.4 Predictive analytics............................................................................................................................5
1.5 Information integration and data governance.....................................................................................5
1.6 Planning and forecasting analytics.....................................................................................................6
2. Banking industry.................................................................................................................................6
2.1 Information integration and governance............................................................................................6
2.2 Data discovery and exploration.........................................................................................................7
2.3 Predictive analytics............................................................................................................................7
2.4 Business Intelligence data mining......................................................................................................8
2.5 Customer analytics............................................................................................................................8
2.6 Content analytics...............................................................................................................................9
2.7 Discovery and exploration.................................................................................................................9
2.8 Data warehousing..............................................................................................................................9
3. Manufacturing industry.....................................................................................................................10
3.1 Business intelligence.................................................................................................................10
3.2 Content analytics.............................................................................................................................10
3.3 Planning and forecasting..................................................................................................................11
3.4 Data and content management.........................................................................................................12
3.5 Data warehousing............................................................................................................................12
Conclusion.................................................................................................................................................12
Reference...................................................................................................................................................13

MANAGEMENT 4
Introduction
Business intelligence and data mining techniques are one area that is currently giving
many companies competitive advantages over other competitor business. Business intelligence is
the analysis and use of business accumulated data for planning and decision making. Data
warehousing, on the other hand, refers to the storage, analysis, and management of business data
for effective business decision making. Business intelligence involves data management and data
analysis that is important for business decision making. These field of data analysis and data
management take advantage of many data set often derived from chunk data within the business
system. These data are analyzed for reporting and understanding f the trend that give more
picture of customer behavior. Many business sectors are making use of these chunk data for
prediction, planning, modeling and analysis that is useful for product design and marketing.
Some of the examples of industries that are using business intelligence and data warehousing are
banking, manufacturing and transport industry. The following paper, therefore, explores various
business intelligence and data warehousing techniques that are sued for competitive advantages
and decision making.
1. Transport industry
There are some data mining and business analytics techniques that are important for the
transport industry. Firstly, business intelligence data mining techniques help track the pattern of
transportation system throughout an industry. According to Munoz (2017), the data mining
technique analyses the pattern of data in the transport industry such as transport routes, transport
traffic, transport impact and flow of some means of transport. Transport rout and traffic is
important for optimization of routes and planning for transport expansion for those areas that
have been identified for heavy traffic. Route optimization is one way through which the data
analysis has helped improve the transport industry and increase the business competitive

MANAGEMENT 5
advantage. The data mining for the global petroleum industry, for instance, has help
identification of countries such as US that largest producer of petroleum products opening the
chance for trade with the country (Dedić & Stanier, 2016).
Figure 1: Interconnection information data (Ashton, 2018)
1.2 Content analytics
Content analytics technique enables the determination of effects of a certain variable in
the transport industry. This is important for modeling and planning that is used to determines the
drivers that affect transportation within the industry. For instance, data mining particularly
provides information on the drivers to transport industry and competitive advantage is useful for
ensuring proper planning. Some of the drivers to transportation are analyzed for effective
transport system is prices, demand, competition, and energy. The regression data mining
technique, therefore, provides the necessary information on the relationship between these two or
more variables within a data set (Evelson, 2010).

MANAGEMENT 6
1.3 Data discovery and exploration
The data discovery and exploration technique is another data analysis technique that
helps determines the association between specific variables within the transport industry. The
techniques help in establishing the relationship between factors such as energy consumption and
pollution. Data on energy consumption is another area that plays a crucial role in the
management of energy requirement and environmental impact of various means of transportation
as shown in the figure below. For instance, the association between customers’ social class and
specific transport service is used in air transport to determine the service categories such as first
class, economic class among others (Rankin, 2013).
Figure 2: Energy Consumption (shrinkthatfootprint.com, 2019)