MBA600 MBA Capstone: Analyzing Strategic Issues in Data Warehousing

This report provides a conceptual analysis of data warehousing, focusing on strategic issues such as real-time ETL, data modeling, OLAP queries, scalability, and real-time alerting. It identifies problems like lack of data sources and mismanagement in key business areas. The report discusses challenges in extracting, transforming, and loading data in real-time, as well as issues related to data aggregation and synchronization. Scalability and query contention are also addressed, emphasizing the need for well-planned structures and appropriate tools. The study concludes by suggesting the use of appropriate technologies and user training to mitigate these issues. Desklib offers similar solved assignments and past papers for students.

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Table of Contents
Introduction......................................................................................................................................2
Enabling the Real-Time ETL...........................................................................................................4
Modeling Real-time Fact Tables.....................................................................................................4
OLAP Queries vs. Changing Data...................................................................................................5
Scalability & Query Contention......................................................................................................5
Real-Time Alerting..........................................................................................................................5
Conclusion.......................................................................................................................................6
References........................................................................................................................................7

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Introduction
The data warehousing companies do not only concentrate on recovering themselves from
the diverse business crisis, but enhance the profit level much remarkably. Kimball et al., (2015)
recognised that during the intervening period, majority of the capitals are invested on upgrading
equipment, training the existing staffs, and developing other innovations. In addition to this, the
companies even look forward to improve the internal corporate governance process, which is
generally based on the high quality information that are transforming the enriched data along
with the suitable experiences. The study will put forward the idea of the strategic issues based on
the data warehousing process. The exploration of the theoretical concept and the identification of
the underlying issues will be presented in this study.
Conceptual Analysis of Data Warehousing and Problem Identification
Data warehousing depends on the integration of the data from the diverse sources into
one system. According to Halboob et al. (2015), complete data warehousing contains the history
of system and the available data for the last few years. The individual operators order for the data
that is transformed through the data warehousing sources. The data warehousing process deals
with specific codes and programs that are associated with the entire programming. The file is the
collection of the data in different formats, which contains the codes of the data warehousing. The
development of the data warehousing delivery solutions creates the basis for the consistent and
sufficiently rapid analysis of the historical data. This information helps in gaining knowledge
regarding several methods that predict specifications for the future.
One of the major problems identified in the data warehousing is the lack of the data
sources, which is considered as the fundamental barriers to the performance management of the

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products, sales channels, key indicators, and segments. It is necessary to build the high quality
data warehouse is one of the most significant parts of securing an insurance strategy.
Figure 1: Motivation of implementing the data warehousing
(Source: Barbierato, Gribaudo and Iacono 2016 )
The above figure highlights the motivational areas for the implementation of the data
warehousing, which helps in addressing the challenges in a specific manner. It is notable that the
warehouse implementation is based support users who are associated with the business areas,
finance and controlling areas, and risk management and solvency areas. Mismanagement in
these processes creates the significant problems in the data warehousing process.

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Enabling the Real-Time ETL
The development of the data warehousing face the difficulties in extracting, transforming,
loading, and cleansing data from the appropriate source system. It is noticeable that the
performance of the ETL data in the real-time introduces some of the additional challenges. ETL
tools along with all other systems are generally based on the off-the-shelf products that are
operated in a batch mode. It is assumed that since there is the probability of extracting files on a
certain schedule, which makes the data available at any point of time. However, the problem
emerges when the system cleanses and transforms the data into the warehouse. It is noticed that
users cannot get access data warehouses in time loading (Wibowo 2015). Another most
considerable issues faced in the improper scheduling. The data loading is generally performed
late at night, which is quite inconvenient to many of the users. In fact, when the data is
continuously loaded, there will not be any system downtime. The requirements for the
continuous updates excluding the warehouse downtime are shown to be inconsistent with the
systems and the traditional ETL tools. However, currently there are new tools available in the
market that specialize the in data loading and real-time ETL services.
Modeling Real-time Fact Tables
Introducing a real-time data into an already existing data warehouse creates the
considerable issues in the data modeling process. For instance, a data warehouse generally
includes the data aggregated at different levels, which depend on the time dimension. This
dimension needs to consider the possible aggregated information, which might be out of proper
synchronization with the associated real-time data (Gudivada, Baeza-Yates and Raghavan 2015).

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The issues are generally occurring when there is the lack of knowledge regarding the place
where the real-time data is stored and identifying the best link to the rest of data model.
OLAP Queries vs. Changing Data
The Query tools and OLAP system are designed for operating on the top of static data
and unchanging information. Apparently, the underlying data cannot be changed due to which
they do not take any precautions for ensuring that the outcome would not negatively impacts on
execution of emerging queries (Medas and Motta 2017). This mismanagement can lead to the
confused query results and inconsistencies.
Scalability & Query Contention
Another most difficult issues faced in the data warehousing is the issues with scalability
and query contention. It affects the deployment of real-time data warehouse in which it is
essential to make the real-time and well-planned structure and then testing it thoroughly. It is
essential to clarifying the data warehousing in real-time is becoming much necessary (Junghanns
et al. 2017). The use of the appropriate tools, approaches, advices, and real-time data can resolve
the issue and make it easier in the future.
Real-Time Alerting
The distribution of reports in the email versions is associated with the issues with the
alerting applications. After loading the data, it is noticeable that the availability if the real-time
data creates the alerting applications in a much appealing way (Vulimiri et al. 2015). The
mismanagement of the schedule can affect the data warehousing storage in a significant manner

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Conclusion
The study explores the conceptual analysis of the data warehousing process. It is
noticeable that the data warehousing process deals with specific codes and programs that are
associated with the entire programming. The associated users may face the trouble in accessing
the data while it is loaded at the inconvenient time. The requirements for the continuous updates
excluding the warehouse downtime are shown to be inconsistent with the systems and the
traditional ETL tools. It is thus suggested that the use of the appropriate technologies and the
necessary training of the users an mitigate the issues in a comprehensive way.

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References
Barbierato, E., Gribaudo, M. and Iacono, M., 2016. Modeling and evaluating the effects of big
data storage resource allocation in global scale cloud architectures. International Journal of Data
Warehousing and Mining (IJDWM), 12(2), pp.1-20
Gudivada, V.N., Baeza-Yates, R.A. and Raghavan, V.V., 2015. Big Data: Promises and
Problems. IEEE Computer, 48(3), pp.20-23.
Halboob, W., Mahmod, R., Abulaish, M., Abbas, H. and Saleem, K., 2015, April. Data
warehousing based computer forensics investigation framework. In Information Technology-
New Generations (ITNG), 2015 12th International Conference on (pp. 163-168). IEEE.
Junghanns, M., Petermann, A., Neumann, M. and Rahm, E., 2017. Management and analysis of
big graph data: current systems and open challenges. In Handbook of Big Data Technologies (pp.
457-505). Springer, Cham.
Kimball, R., Ross, M., Mundy, J. and Thornthwaite, W., 2015. The kimball group reader:
Relentlessly practical tools for data warehousing and business intelligence remastered
collection. John Wiley & Sons.
Medas, C. and Motta, D., 2017. Systems and methods for measuring effective customer impact of
network problems in real-time using streaming analytics. U.S. Patent Application 15/470,716.
Vulimiri, A., Curino, C., Godfrey, P.B., Jungblut, T., Karanasos, K., Padhye, J. and Varghese,
G., 2015, May. Wanalytics: Geo-distributed analytics for a data intensive world. In Proceedings
of the 2015 ACM SIGMOD international conference on management of data (pp. 1087-1092).
ACM.

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Wibowo, A., 2015, May. Problems and available solutions on the stage of Extract, Transform,
and Loading in near real-time data warehousing (a literature study). In Intelligent Technology
and Its Applications (ISITIA), 2015 International Seminar on(pp. 345-350). IEEE.