Data Analytics Tools and Using Method. Decision support systems refer to computerized information systems that enable management in the selection of appropriate decisions for an enterprise. On the other hand, business intelligence refers to a technology-driven process that helps executives, managers, and companies to analyze business information, draw suitable conclusions, and present actionable information in an organized manner. Business analytics refers to the technologies, skills, and processes involved in the exploration and identification of business patterns based on the present and past activities to improve on planning ideas. Lastly, predictive analytics refer to an analytics method that uses insights from data mining and machine learning to make suitable predictions about the future events of a business or company.
The different analytics tools have similarities and differences as outlined below. The greatest similarity among these systems and analytics techniques is the use of technological tools to analyze big data and identify patterns relevant to the business. These analytics techniques primarily help the executive and management to predict trends and enable effective decision-making.
However, each of the above have characteristics that distinguish one from the other. Decision support systems enable the user to identify anomalies in a large set of data to improve on the decision-making process, while business intelligence enhances analysis of data, thus, providing insightful patterns to the area of interest. Business analytics on the other hand is a descriptive analytics technique that review past information and identify appropriate trends for the growth of the business. Predictive analytics uses complex mathematical algorithms and other technologies to predict the future and likely trends of the business.
For the different techniques and technologies to enable effective problem solving, certain methodologies have to be deployed. The tools use different methodologies. However, there are common methodologies that apply to both tools. Decision support systems, business analytics, and predictive analytics employ aggregate analysis and correlation analysis to describe and compare different segments and identify useful patterns. However, methodologies like trend analysis are used for techniques like business analytics. Predictive analysis and time series are prominent methodologies employed by the predictive analytics technique. Business intelligence primarily employs segmentation and sizing to make estimates and analyze historical data to identify useful assumptions in the business.
The above tools analyze large data sets and enable effective decision-making. Thus, the technologies and techniques employed to enhance the analysis are likely to be similar. Data warehousing is a technology deployed by the above techniques. Data warehousing refers to the system of keeping large sets of data in a transactional database. Usually, the data is accrued form different source and may represent a wide range of items. In analytics, the technique review the data and draw suitable conclusion and patterns depending on the tool and aim of the analytics.
On the contrary, the dashboard technology is used mainly by business intelligence to analyze and represent the informative patterns drawn from a set of data. Dashboards employ the use of graphs and charts to demonstrate visual information about the trends and predicted patterns from the data. The technology allows the development of summaries and key trends.
Such can develop effective information to help with sales persons, credit scores, and future trends in prices. Other technologies include data discovery, cloud data services, and ad hoc reporting. These methodologies are common among data analytics tools. This are important since they improve the business decision-making process thus increasing profitability and improving planning.
Subprime mortgage case
The subprime mortgage case described reflects lack of effective analytics tools that can help in the execution of various administrative duties and reduce avoidable losses. The bank lacked appropriate technologies and methodologies to help predict the trends and identify the credit worth individuals to avoid defaulter cases.
Decision support systems are technology-based systems that process given information and enable the management to make effective decisions about an issue. The bank should employ the decision support systems to develop suitable decision-making mechanisms. This will enable the allocation of subprime loans to credit worth individuals and development of adjustable mortgages. Such would avoid cases of defaulter borrowers.
The business analytics tool on the other hand checks the historic data and past trends to enhance meaningful conclusions about the activity or business. The case should employ such techniques in the identification of future trends in the prices of the houses and other real estate projects. Consequently, the huge fall of the prices wouldn’t affect the mortgage rates since the issuance of the mortgages would have catered for the same through interests rates.
Additionally, predictive analysis would enable the bank to identify trends and patterns in the real estates projects. Usually, such investments vary depending on the season and other factors. Predictive analysis can help the bank deploy such factors in the analysis and prediction of future trends that can greatly influence the rate of subprime mortgages.
Lastly, the bank can use business intelligence tools in the analysis, presentation, and development of conclusions about a set of data. For example, the technique can enable the company to keep record of all their borrowers and constantly update such records. Such can enable distinguish between the defaulter borrowers and the other groups. Similarly, business intelligence can help in the identification of the credit scores through the technological tools employed in the technique. In addition, the technology can provide assess customers and develop customized loans for each depending on various techniques used to assess the credit worthiness of individual customers.
Decision support system consists of a category of processes employed in the development of a viable solution to an identified issue. The four capabilities of the decision support system are; intelligence, design, choice, and implementation. The mortgage case can use the intelligence capability to identify all the vital information about the real estate and its trends. Such information can help define the problem an allow room for resolution mechanisms.
The second capability is the design process. Thus, the company can use the identified information to develop various methods of analysis and credit evaluation. Thirdly, the development of a choice involves the selection of an effective solution from the developed designs in the second phase. Lastly, implementation of the chosen option is necessary. This enables the deployment of the new techniques and such could help in the risk management.