Computational System for Decision Support: Critical Analysis

This essay provides a detailed analysis of computational systems used for decision support, emphasizing the relevance of technologies like Machine Learning, Big Data Analytics and Enterprise Resource Planning (ERP). It discusses how decision support systems enhance business intelligence architecture by automating and accelerating decision-making processes. The essay explores key components of decision support systems, including data nature, design principles, employee training, and performance evaluation. It highlights real-world applications of computing-based decision support systems in organizations like Walmart and Domo. The essay also critically reviews Big Data Analytics and ERP, weighing their pros and cons as tools for business decision-making, and concludes by noting the limitations of ERP systems for small and medium-scale enterprises and the challenges in incorporating new resources for decision support.

Running head: COMPUTING SYSTEM FOR DECISION SUPPORT SYSTEM
COMPUTING SYSTEM FOR DECISION SUPPORT SYSTEM
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Part 1:
Decision support system forms the basis of developing business intelligence architecture.
Traditionally the business demission making has been managed manually and was heavily
dependent on the human intelligence. However with the expansion of business requirements,
there has been a major change in the way business used to operate. Today the business needs to
deal with huge amount data due to expansion of customers as well as field of services. It is not
only important to collect the data, but there should be proper analysis method for extracting
useful information from the data set in order to make intelligent and effective business decision
(Dssresources.com, 2018). With the advancement of technologies, business organizations are
today experiencing the power of technologies. Organizations are recognizing the relevance of
technology in the context of business decision making (Onlinecampus.bu.edu, 2018).
There are various approaches to design the decision support system and the process
becomes easier when effective technology like Machine learning is used. It is one of the
emerging technologies in the field of artificial intelligence, is already proving the efficiency and
credibility in creating effective decision making for the various businesses organizations. One
key area which needs to be considered while judging the efficiency of the computing based
decision support system in the context of business analytics is the ability of the technology in
bringing automation and speed in the decision making process. Decision support system is
creating value for the organization by bringing acceleration in the decision making process itself.
In order to store data that is huge in volume and also diverse in nature, efficient data storage
techniques in necessary (Frank, 2016). Machine learning technologies are equipped with
affordable data storage solution and advanced processing power. Due to this feature machine

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learning can process huge amount of data efficiently and it also helps to drive insightful
information from the data set, thus helping in creating technology based decision support system.
Although companies are deploying technologies to bring automation in the workforce,
however the design of efficient decision support system has always been a major problem for the
organizations, which further affects the workflow process in the organization (Larson, 2014).
Organizations often seem to be slow and inefficient in decision making. There could be various
reasons behind that. It may be the work pressure that make the employees become less attentive
to the decision making or perhaps involvement of too many decision maker in one approval step
is what makes the decision process slow and inefficient which hampers the productivity and the
profitability.
Designing of decision support system or DSS With machine learning has not only
become hassle free but efficient as well. This makes the technology highly relevant in the context
of business decision making. With effective decision support system in place today organization
can focus on the information that matters the most to the business process (Sharma, 2015). All
other aspect of the decision making like collecting data, analyze them to find insightful
information from them are managed by the machine learning algorithms. Thus the decision
making process becomes easier for organization and it is now possible to focus on the decision
making process itself rather than managing others aspect what is required in making the decision
process itself.
In order to understand the characteristics, there are six components that need to be
addressed while using decision support system for business decision making. The nature of the
data varies according to the requirements and also the information that needs to be extracted

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from the data set. Hence the requirements and the feature of the data play an important role in for
making the data analytics useful and effective (Nagai, 2014).
Choice of designing principle is another important factor for the successful use of
decision support systems. There are various kinds of designing principles depending on the
nature of the data. Designing approach used for data prediction is not suitable for data
classification or data clustering.. Naive Bayes, Support Vector Machines, Decision Trees, k-
Means clustering are some popular designing principles that are commonly used.
Once the natures of the data and the expected features from the data set are clearly
defined it is required to train the employees on basis of the architecture that is chosen for the
system design. The training is required to train the employees so that they can learn by
improving the experience. Once the training and evaluation of the system is completed, test is
conducted to assess how the system performs when assigned with a new set of tasks that is
different from the data used during the training of the employees. It is used to assess the overall
performance of the system as well as the ability of the employees to use the new system
(Goodfellow et al., 2016).
Decision support system improves the quality of resource management to a great extent.
In order to improve decision making process it is important to identify and measure the
credibility of the proposed solution and the desired solutions to minimize the error in the
decision making process and optimize the output. However, it requires intensive case-by-case
analysis when applying manual approach to find the errors in the measurement technique (Frank,
2016). Decision support system with the help of Machine learning makes these evaluations

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efficient and effective.. This feature makes decision support system effective in improving the
quality of the decision making process by identifying and minimizing the errors in measurement.
Computing based decision support systems are being used by various leading
organizations all over the world top make decision for various business related activities.
Walmart uses HANA, a cloud platform by SAP which uses machine learning algorithm. It helps
to make business and sales related decision. The platform stores replicated data in RAM rather
than in disk which makes data access fast by the applications and analytics built on the HANA
platform and makes the business decision making faster.
Domo, a software company which designs business management software, has designed
a dashboard that uses AI and machine learning techniques to create decision support system for
the organizations. It helps company to gather information and make effective decision based on
that.
The eSales solution from Apptus, a software designing company, uses machine learning
algorithm. It helps to predict the choice of the customer by analyzing their past purchase history
and this information helps the retailer to serve their customers better and make effective sales
related decision.

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Part 2:
Big data analytics is another important toll for business decision making. Big data can
be classified as a large set of data that is high in volume, velocity and variety. Volume refers to
high amount of data, velocity defines the speed of incoming and outgoing data and variety means
range of data sources from where data is coming. Data can be in structured, semi-structured or in
raw format (De Mauro, Greco & Grimaldi, 2015).
Big data analytics, which make use of computer intelligence, plays an important role in
decision making that drives business organization towards success (Gandomi & Haider, 2015).
Big data analytics comprises of three key elements called data sourcing, data refining and
exploitation of the collected data from various sources. In order to make data analytics work
perfectly it is important to make sure that all of these key elements work in perfect coordination.
Big data has an important role to play in the business decision (Wang et al., 2016). Big data
analytics can be used to increase sales in the organization as well as bring optimization in the
product price to increase return on investment. Big data with the combination of intelligent data
sourcing and data analytics helps to determine the hidden pattern in the data sets and makes it
possible to extract useful information that goes on making important business related decision.
Big data analytics can be used to predict the future performance for the organization by
analyzing previous data about sales, number of customer and data related to revenue as well.
However there are certain drawbacks that need to be considered as well. Due to the
technologies that empower the data analytic projects is relatively new, there may be technical
barriers that may affect the quality of the analytics. Big data enabled projects can have
compensating results, yet regularly require specific assets and innovation so as to change

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information into applicable and noteworthy understanding. With this comes a level of budgetary
duty that can make boundaries to hierarchical purchase.
ERP or the enterprise resource planning is another important tool for developing decision
support system or DSS. The popularity of the ERP has only increased in the recent years due to
its effectiveness and efficiency for decision making. The technique uses simulation model for
predicting and allocating resources (Ptak & Schragenheim, 2016). ERP provides effective means
to integrate the information flow with the business process, thus bringing more seamless
approach in decision making. The ERP system helps to integrate different aspect of business
decision making like production planning, purchasing, manufacturing, and sales, distribution,
accounting and customer service (Nwankpa , 2015).
However the ERP system, instead of the popularity and the effectiveness in decision
making is not effective for small and medium scale organization due to the complexity in the
technological requirements and the high cost requirement which is a major concern for medium
to small scale enterprise (Leyh & Sander,2015 ). However organizations that have already
integrated the ERP system are now facing problem in incorporating new resources for decision
support. Based on the information provided by the empirical research, it is found that the
reporting capabilities provided by many ERP packages are not sufficient for making effective
business related decisions for organizations that have implemented the ERP system.

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