Roles of Artificial Intelligence

Most decision support systems are used in corporate environments and organizations to solve challenges where different tools and methodologies change DSS and add new capabilities. These include data warehousing and mining, online analytical processing, intelligent agents, enterprise resource planning (ERP) systems like SAP, mathematical software developments (like SPSS), artificial intelligence technologies, data warehousing and mining, online analytical processing, and the Internet, as well as telecommunication technologies like the World Wide Web and the Internet (Power, 2013).

Decision-making tools

Several interrelated components, including the data management component, decision support system architecture, model management component, and user interface management component, are included in Power’s (2013) definition of Intelligent Decision Support Systems. The user interface is a decision support system element that facilitates user interaction. Since the user interface component interacts directly with the user, it is essential to have a decent design.

The data management component is within the bigger computer-based decision support system with multiple subsystems. They consist of the following:

Data that has been received from both internal and external sources is stored in the integrated DSS database. This information can be kept current in a database or only accessed when necessary.

There are two types of database management systems (DMS): relational and multidimensional.

A data dictionary is a catalog containing all the database data definitions; it is typically used in decision-making processes’ identification and definition phases.

For querying activities, use tools like query editors.

A model management component of DSSs also consists of the following modules:

The model base module stores the quantitative models that enable the system to analyze and find solutions.

The model’s definition and related information are in the model dictionary. The model’s generation, execution, and integration components translate user commands into model responses and input them into the model management system.

Using high-level programming languages, the model base management component creates new models.

Knowledge-based decision support systems (IDSS)

To find the best answers to the problem, decision-making processes may use precise techniques, actually tested principles or heuristic approaches. Logical programming, creating expert systems, neural networks, fuzzy expert systems, hybrid intelligent systems (i.e., neural expert systems), and genetic algorithms are some of the primary approaches to solving artificial intelligence challenges. However, using these artificial intelligence techniques when creating software programs to assist decision-making processes is challenging because it is still being determined which approach or system will be the most effective.

According to Sauter (2010), many systems combine modeling, domain knowledge, and analysis tools to offer consumers intelligent support. Knowledge base components are used to solve problems, build decision models, analyze the data, and synthesize the findings. Some of these systems have been enhanced with knowledge-based components to replicate human judgment. To estimate future uncertainty and select a hypothesis that decision models might be built on, some managerial judgments/decisions have been used (Sauter, 2010).

Decisions can occasionally be both knowledge- and data-intensive. As a result, gathering and using enormous amounts of data typically requires significant work. A knowledge management module in the intelligent DSS stores and oversees a new class of developing artificial intelligence (A.I.) technologies, including case-based reasoning and machine learning. Most artificial intelligence (A.I.) technologies may learn from past data and conclusions to support repeatable complex real-time decision-making (Suchanek, Sperka, Dolak, & Miskus, 2011).

The definition of machine learning is the computational process by which a computer system learns from experience, observations, and data and, as a result, modifies its behavior in response to changes in the knowledge that has been previously stored. Genetic algorithms and artificial neural networks (ANN) are the most well-known machine learning methods.

Applications DSS

Most intelligent DSSs are employed in decision-making for the following, not exhaustive, list of structured and semi-structured problems.

DSS provides the correct information when deciding lease versus buy activities, break-even analysis, amortization, depreciation, and undiscounted cash flow.

Real estate investments where DSSs are used to calculate the impact on taxes, cash flows, and other financing options

Portfolio examination

Marketing analysis using consumer sales audits, sales forecasting and analysis, and promotion analysis.

B.I. or business intelligence Decision

Support Systems are a specialized class of computerized I.S. (information systems) that include corporate and enterprise decision-making processes, according to Turban (2011). The main objective of a well-designed DSS is to assist decision-makers in processing useful information from raw data, prior knowledge, documents, and business models to identify problems, provide solutions, and make the right decisions (Turban, 2011).

A.I. tools are included in the intelligent decision support system (IDSS). In this context, “business intelligence” refers to the overall outcome of data collection and processing, generation of useful and pertinent processed data, and integration of that processed data into routine operations to enable managers to make informed decisions now and in the future. Business intelligence tends to support management needs and decision-making.

To make the appropriate decisions in the industry, managers require information. Data required by DSS and pertinent to make a business decision may come from a variety of sources, such as:

The configuration information determines the system’s nature. The DSS system is set up to match the needs and requirements of the company.

Master data is data obtained to specify the modules of an electronic business system. Examples of such data include accounts, pricing codes, and customer and product files. Data Management comprises techniques and technologies that determine and manage an enterprise’s non-transactional data components.

Online Transaction Processing (OLTP) generates operational data from daily business operations. Such data may comprise sales orders, purchase orders, invoicing, and accounting. The OLTP is a system category that enables and handles transaction-based applications, specifically for data manipulation (input and retrieval of data).

Computerized Information systems such as Online Analytical Processing (OLAP) are complicated applications that take information from many sources, evaluate the data, and give appropriate processed data. OLAP software examines the data recorded in a database in real-time. The OLAP server is usually a dedicated module with specific algorithms and indexing tools that process data mining operations with less effect on database operations. Most of the data needed for enterprise decision-making originates from ERP and CRM systems. Data processing includes data selection, analysis, and cleaning, creating the basis for decision-making at all levels of management. In this context, the processing of data is affected by Extraction, Transformation, and Loading (ETL) operations. The ETL or the Data Integration process encompasses data movement, administration, cleansing, synchronization, and consolidation.

Another component of DSS for B.I. is the Data Warehouse, whose major role is integrating corporate data. Data warehousing has developed from batch-oriented environments typically suitable for reporting and analysis. Data warehouses hold huge amounts of data kept at a very basic level. For instance, all sales data is kept and modified in various dimensions.

Applications for business intelligence (B.I.), including SharePoint, BIRT Project, and Microsoft Excel, offer a basic interface between the system and business management. Users of these apps can alter a wide range of visual representations, including charts (bar, pie, profile, and line graphs), multidimensional scatter plots for statistical data visualization, and map-based data displays to investigate B.I. reporting outcomes spatially. In order to promote economic growth, business organizations look for all processed data in the global economy (Surhone, Tennoe, & Henssonow, 2010).

Managers can get consumer and environmental statistics via business intelligence systems. Matching sales returns with site visitor activity over a specified period, trend analysis that compares total sales with site visitor activity over a specified time, matching sales returns with site visitor activity by the hour to gauge the effectiveness of advertising campaigns, and matching sales returns with site visitor activity from primary referrers where the referrer is a search engine service These statistics respond to the managers’ inquiries about who, how much, when, and what they purchased. Additionally, these data provide the geographic locations of the clients as well as the method by which they arrived at the website (i.e., the search engine query they used). Moreover, what page did visitors to the site come from? Weekly and monthly data are collected, together with trends, over a specific time frame (Suchanek, Sperka, Dolak, & Miskus, 2011).

Conclusion

In the modern world, much data is produced daily from numerous sources. Computer-supported decision-making is becoming increasingly important when tackling these complicated problems since it is hard to analyze and benefit from the vast amount of information gathered without using clever and sophisticated data analysis tools. Consequently, a variety of applications have made use of artificial intelligence. For example, clever algorithms let people make precise, well-informed judgments or deliver pertinent information. Search engines and social media platforms utilize artificial intelligence to predict user interests and provide individualized information. In conclusion, intelligent DSS’s primary function is to enable professionals to broaden their area of expertise rather than to confine it.

References

Ghattas, J., Soffer, P., & Peleg, M. (2014). Improving business process decision-making based on experience. Decision Support Systems, 59, 93-107. http://dx.doi.org/10.1016/j.dss.2013.10.009

Power, D. (2013). Decision support, analytics, and business intelligence (1st ed.). [New York, N.Y.] (222 East 46th Street, New York, NY 10017): Business Expert Press.

Sauter, V. (2010). Decision support systems for business intelligence (1st ed.). Hoboken, N.J.: Wiley.

Suchanek, P., Sperka, R., Dolak, R., & Miskus, M. (2011). Intelligence Decision Support Systems in E-commerce. Retrieved 6 February 2017, from http://www.intechopen.com/books/efficient-decision-support-systems-practice-and- challenges-in-multidisciplinary-domains/intelligence-decision-support-systems-in-e- commerce.

Turban, E. (2011). Decision support and business intelligence systems (1st ed.). Upper Saddle River, N.J.: Pearson Prentice Hall.

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Artificial intelligence has two roles in a decision support system (DSS). First, artificial intelligence can serve as a model type. Secondly, applying artificial intelligence in a DSS can provide intelligent assistance to users.

1. How can designers, using artificial intelligence, build the expertise the decision maker lacks into the DSS?

2. Explain how to design and implement a system to address uncertainty in both information and relationships.

Outline your plan addressing these issues and other issues.

Need 3-5 pages with introduction and conclusion. APA format with a minimum of 8 peer-reviewed sources.

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