University Data Handling and Business Intelligence Report: LCBB5000

This report provides a comprehensive overview of data warehousing, business intelligence (BI), and data mining. It begins with an introduction to data warehousing as an integrated system for streamlining data and decision-making. The main body delves into key concepts like BI (converting raw data into actionable information), data mining (extracting meaningful patterns), and data warehousing (building and accessing data warehouses). The report explores the characteristics of data warehouses, including being subject-oriented, integrated, time-variant, and non-volatile, and also describes the objectives of data warehousing. It further discusses recent trends in these fields, such as cloud computing, SaaS, and SOA, and consolidations in the industry. The report concludes by emphasizing the importance of understanding these trends for informed decision-making in businesses and for investors. References from various books and journals are included to support the analysis.

Data Handling
And
Business Intelligence

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9

INTRODUCTION
Data warehousing is an integrated system comprising multiple simultaneous origins of
traditional and commutative data This streamlines the organisation's monitoring and review
process. This is also a single-specific version of reality for any business to make and predict
decisions. Data warehousing in companies is becoming very common in recent years (Babu,
2012). This Study focuses on different emerging recent trends in data warehousing, business
intelligence and data mining.
MAIN BODY
Business Intelligence: BI relates to systematic collections of different processes, procedures,
and high technologies which contributes in conversion of raw-data into substantive information
which offers profitable and effective business steps. This is a structured composition of different
software applications and services in order to transform meaningless data into quite significant
actionable intelligence and database.
Data mining: Data mining implies to a structured process utilized by organisations to convert
raw-data into significantly multi-purpose and meaningful information. By applying software to
seek for specific patterns in huge collection of information/data, business organizations can
acquire more comprehensive details about its customers group in order to frame a more effectual
merchandising schemes/strategies, value addition in sales and optimise business costs (Demirkan
and Delen, 2013).
Data warehousing: It is more wider concept which involves processes of building and accessing
data warehouse and related structures. A data-warehouse is developed by integration of distinct-
distinct hetero-genus origins that assist in analytical and technical reporting, organized and in ad-
hoc queries/issues as well as in decision making. At the end of year 1998, the scale of data
warehouse industry is estimated to be at least around $8 billion and more than 900 suppliers are
offering different types of data warehousing equipment(hardware), applications(software) and
other related services. Here are some key characteristics of data-warehousing, as discussed
below:
Characteristics of Data warehouse:
Subject-Oriented: A data warehouse is subject or topic-oriented as it provides information
about a topic rather than the continuing operations of organizations. Sales, promotion, delivery,

etc. can be such topics or subjects. The continuing activities are never focused on such a data
warehouse. Alternatively, it focused on designing and evaluating data for planning and decision-
making. It also offers a basic and succinct perspective of the particular topic by excluding data
that is not beneficial in supporting the decision making process.
Integrated: Integration in Data Warehousing means setting up a pervasive unit of measurement
from the significantly different database with all similar information. The data must also be
preserved in a prevalent and widely appropriate way in the Data-warehousing. Through
integrating data across various sources such as microprocessor, hierarchical databases, flattened
files, etc., a data warehouse is created. In addition, the naming conventions, layout, and encoding
must be compatible (Glider, Hix and Zalewski, ZeeWise, 2016).
Time Variant: Compared to operating structures, the timescale for data warehouse is very
lengthy. The data obtained in a database system is acknowledged over a fixed time span and
provides historical data. It includes, directly or indirectly, an overarching plot-line. One such
position in which the time variation of Data-warehouse information output is in the database key
layout. Each primary component in the DW must have an overarching plot-line either directly or
indirectly. As well as the day, the month of this week, etc.
Non-volatile: Furthermore, data warehouse is non-volatile, ensuring that past data will not be
lost when entering new data. Data is read-only and updated on a regular basis. This also assists
with examining historical information and understanding what and how much it occurred. It does
not include processes for transaction, restoration and competitiveness regulation. In data
warehouse context, tasks such as removing, upgrading, and adding that are carried out in a
functional application setting are excluded. Just two forms of data warehousing processes are
conducted:
1. Data Access
2. Data Loading
Objective of Data-warehousing:
Data warehousing technology provides a set of new ideas and resources that help the
information user (executive, supervisor, expert) with the decision-making content. The main
rationale for creating a data warehouse is to enhance the company's information efficiency. The
major concern is to provide access to a corporate view of information when ever it exists.
Information from internally and externally channels, from conventional hierarchical information

to rigidly structured data such as word documents or graphics, are cleaned up and compiled into
an unified database in a variety of ways. A data warehouse (DWH) is the reliable collection of
this information made accessible to end-users in a manner that they can comprehend and utilize
in business sense.
Recent trends in data warehousing, business intelligence and data
mining:
Cloud techniques have progressed from conventional server offerings to significantly
higher-level support services even over the previous few years. Data storage are evolving regions
in contemporary cloud. Thanks to the on-demand accessibility of data, processing resources, and
greater-level services, cloud penetration towards these fields is growing. Even though cloud data
warehousing is becoming mainstream, there is a long tradition of data-warehousing beyond the
clouding. The transfer of data from operating technologies to decision-support
networks/structures has been conceived as an artistic principle (IşıK, Jones and Sidorova, 2013).
Firstly, it is essential to recognize that data storage is a mixture of procedures and resources for
preparing data by processing, convergence, including data integration, with Data Warehouse at
its centre. Here are some current trends in data warehousing, business intelligence and data
mining, as discussed below:

Data Warehouse, Data mining and Business Intelligence Trends
As presented in above pictures, overall current trends are classified in five major points:
hardware, software, convergence, consolidation and other that briefly discussed below:
 Hardware: This mainly includes virtualisation that help in creating virtual hardware
platform which enables in storing large data. It also consider mobile devices which makes
a strong network for interacting and receiving with BI system such as online buying and
selling of goods.
 Software: It basically consider visualization techniques that support companies and
people to visualise the desirable results for longer period of time. XML which is a
markup language that is helpful in setting a specific set of standards so that important
documents can easily encoded. The extensible markup language make a better format
which is understandable by machine and human.
 Convergence: This include SaaS which is a distribution model that support in delivering
the useful application to customer through Internet. It is also called hosted software or on

demand software provider which actually manages the application and focuses on
security, performance and availability. It also include SOA which is a systematic
approach that allows application to make proper use of services that are available in
nearby network. SOA comprises a collection of principles that guide the construction of
systems as well as provide methods to incorporate elements into a consistent and
distributed framework.
 Consolidation: This mainly help to companies when planning for consolidation as it ease
the process of integrating of different financial documents. It is also observed that
consolidation of companies in the data warehousing and business intelligence industry
into five or six dominant key factors (Minelli, Chambers and Dhiraj, 2013).
 Others: There have been an increasing needs for maintaining a better quality in
education sectors within data warehousing and business intelligence because student in
present time are more attached to digital technology. This makes learning more
interesting as now children are more attached towards studies and attain better marks.
CONCLUSION
From above report it has been articulated that identification and understanding of trends
in data-warehousing, BI and Data-mining is crucial for key users like corporations, group of
investors and other business entities. It support them in effectively dealing with different
technological, financial and other business issues.

REFERENCES
Books and Journals:
Babu, K.V.S.N., 2012. Business intelligence: Concepts, components, techniques and benefits.
Components, Techniques and Benefits (September 22, 2012).
Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision
support systems: Putting analytics and big data in cloud. Decision Support
Systems, 55(1), pp.412-421.
Glider, C.S., Hix, J. and Zalewski, B.J., ZeeWise Inc, 2016. Systems and methods for collection
and consolidation of heterogeneous remote business data using dynamic data handling.
U.S. Patent 9,411,864.
IşıK, Ö., Jones, M.C. and Sidorova, A., 2013. Business intelligence success: The roles of BI
capabilities and decision environments. Information & Management, 50(1), pp.13-23.
Minelli, M., Chambers, M. and Dhiraj, A., 2013. Big data, big analytics: emerging business
intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.