Many business entities in the world today are confronted with huge amount of data relating to their transactions. The capability of storing, retrieving, updating and analyzing such data determines the accuracy and efficiency of their internal decision-making process and also their level of aggressiveness to the market. The mechanisms of storing business data and retrieving it is critical in the survival of a modern business. There are many solutions that are available for processing business data. The choice and adoption on any one of them will depend on a number of factors. The business has to evaluate its data requirements before settling on any of the available solutions. It can however be noted that it is extremely difficult for a business to survive in the modern business environment without a proper and appropriate data handling mechanisms (Shaqrah, 2018).
Businesses are ever-evolving. They are always confronted with issues that need appropriate decisions that can sustain their performance. Additionally, management will always wish understand the operational trends when defining their strengths and weaknesses. This can only be achieved with the use of business intelligence tools. Business intelligence has the potential to identify abnormalities in the regular business operations. Using historical data, the management can then undertake a root cause analysis to establish the main cause of such abnormalities and the much-needed solution (Shaqrah, 2018).
Business analytics are considered to complement the outcome of business intelligence tools. Where business intelligence has consistently identified certain abnormalities, business analytics is involved to spearhead the necessary changes that may be required. The management will always be involved in such decisions. Thus, both business intelligence and analytics serve the interest of the different levels of management (Scholz, n.d.).
Some of the commonly available business intelligence tools revolve around the use of databases. Databases are considered to be central storage where business data can be stored. Further, the database can be used to electronically update, retrieve, analyze and filter data and information. Database make it easier for management to evaluate their performance regularly and can also determine the impact of their decision in the market. The amount of time required to manage the data and the quality and consistency of the information obtained from a database always persuade the modern businesses to embrace the concept of databases on all their processes. This paper will evaluate the appropriateness of the use of the database for a mortgage brokerage firm (Kowalewski and Czyczerski, 2018).
The firm (Butler Financing Company) matches the lenders and borrowers of mortgages. As such, it has a list of several borrowers and lenders together with the mortgage details. Currently, the company makes use of spreadsheets to manage its business data. This process is however prone to errors and other delays whenever data is retrieved and updated. An evaluation of the appropriateness of the use of a database is critical to decide whether or not to adopt a database that can be used to process business transactions (Koehler, 2018).
The management can only be able to improve their strategies with the use of business analytics as long as there is a supportive database. This is what makes business intelligence and business analytics gain more popularity in the modern working environments. It is not only able to avert wrong decisions at the lower management levels but also able to act as reliable tracker to sustainable financial performance. These two tools have basically taken a center stage in every decision-making in the insurance mortgage business. Whether in the board rooms or in the fields, appropriate decisions can now be derived on the basis of the underlying data (Coronel and Morris, 2018).
The use of business intelligence tools and the database will enable the business to reduce the time that is spent on managing the data. Due to the huge amount of data that is associated with the business transactions, retrieving, updating, deleting and editing such data can not only take a lot of time but also introduce errors. As such, the firm stands to lose financially with the use of wrong data. The business can also lose customers since the customers will lose trust on their engagements with the business (Business plus Intelligence plus technology equals Business Intelligence, 2009).
The use of business intelligence tools is witnessed across all industries. Perhaps the brokerage and banking industry stands to gain more with the use of business intelligence tools that are brought about the use of databases to support their transactions. The banking industry for instance uses such tools to detect frauds in daily transactions. Virtually all banking businesses are based on the understanding of the risk. As such, data plays a central role in helping the bank make more informed decisions in regard to loans and understanding market trends. Predictive analytics combined with machine learning data, artificial intelligence and modeling can help insurance companies to create useful forecasts. These features can only be available when there is a reliable database that can support the retrieving, updating and analysis of the business data (Business Intelligence: Oxymoron or a Big Data Technique?, 2018).
The following figure can be used to illustrate the ER Diagram that can be used to illustrate the organization of the data in the database.
In reference to the ER diagram shown above all the details of the lender are stored separately from those of the borrower. Each of the details of the lender are further stored separately in order to enhance the consistency and integrity of the database. There is a match relationship that has been set between the lender and the borrower. The details of the borrower have equally been segregated from those of the lender. All the details of the borrower are stored separately from those of the lender. Each of the details of the borrower are further stored separately in order to enhance the consistency and integrity of the database. This is the manner in which the database has been setup. As such, it is possible to retrieve each entity individually from the database whenever such a desire arises. This also makes it easy for data analysis since more than one details can be retrieved from the database to analyze a particular trend of information (Andersson et al., n.d.).
The advantage of the above setup is the efficiency of retrieving the data and the consistency of such data. It is not possible to store the details of the address for instance in the name field. This does not only sustain the integrity of the data but also the consistency of the data in the database. It can also be noted that the data stored in the database can be filtered to suit a particular need. The search for instance of mortgages that are more than $200,000 is possible with this form of arrangement. This may have not been possible with the use of spreadsheets. A simple query can be used to retrieve such information from the database (Adi and Kristin, 2014).
The use of databases to support business transaction information is widely used in the business environments in the modern world. Many business entities in the world today are confronted with huge amounts of data relating to their transactions. The capability of storing, retrieving, updating and analyzing such data determines the accuracy and efficiency of their internal decision-making process and also their level of aggressiveness to the market. The mechanisms of storing business data and retrieving it are critical in the survival of a modern business. There are many solutions that are available for processing business data. The choice and adoption on any one of them will depend on a number of factors. The business has to evaluate its data requirements before settling on any of the available solutions. It can however be noted that it is extremely difficult for a business to survive in the modern business environment without a proper and appropriate data handling mechanisms.
- Adi, S. and Kristin, D. (2014). Strukturisasi Entity Relationship Diagram dan Data Flow Diagram Berbasis Business Event-Driven. ComTech: Computer, Mathematics and Engineering Applications, 5(1), p.26.
- Andersson, E., Karlsson, M., Thollander, P. and Paramonova, S. (n.d.). Energy end-use and efficiency potentials among Swedish industrial small and medium-sized enterprises – A dataset analysis from the national energy audit program.
- Business Intelligence: Oxymoron or a Big Data Technique?. (2018). Journal of Applied Business and Economics, 20(1).
- Business plus Intelligence plus technology equals Business Intelligence. (2009). International Journal of Business Intelligence Research, 1(1).
- Coronel, C. and Morris, S. (2018). Database Systems. Mason, OH: Cengage Learning US.
- Koehler, J. (2018). Business Process Innovation with Artificial Intelligence: Levering Benefits and Controlling Operational Risks. European Business & Management, 4(2), p.55.
- Kowalewski, M. and Czyczerski, M. (2018). BUSINESS INTELLIGENCE AND DASHBOARDS IN PERFORMANCE MANAGEMENT. Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse Rynki Finansowe Ubezpieczenia, 94, pp.221-229.
- Scholz, J. (n.d.). Enterprise architecture and information assurance.
- Shaqrah, A. (2018). Analyzing Business Intelligence Systems Based on 7s Model of McKinsey. International Journal of Business Intelligence Research, 9(1), pp.53-63.