The Importance of Having Business Intelligence - Term Paper Example

Introduction Business intelligence is a term that describes the tools used in the analysis, consolidation, storage and provision of access to large quantities of data with the aim of assisting users make informed business choice. The tools comprise of theories, processes, methods, architecture and functional technologies that have to do with data management. This means that for the practice of business intelligence to be incorporated in an organization, there needs to be a collection of data otherwise known as a database. A database is any form of operational data collected and stored by various users through various methods or applications (Thierauf, 2001). However a proper business intelligence system is worthless if the model of information and data storage does not provide a good management system. As such, it is necessary to have a good data management system. A Data Management System (DBMS) is an application that manages the execution of user data in the data regulating their access and modification capabilities. This guarantees the reliability, security and integrity of the data. Over the years these platforms have evolved greatly from simple queries to a wide array of systems that are applied broadly. This paper seeks to show the importance of having Business intelligence as well as data and information management systems through the review of various literatures and the incorporation of a case study. Database management in business intelligence Market analysis of consumer data is not a new practice as it has been in use since the ancient Mesopotamians time where clay tablets were used to store information (Thierauf, 2001). Although the systems in use may have evolved dramatically, the core principles of the practices have not. Data mining and business intelligence in an organizational setting main goal is to analyze the market practices with the aim of providing the best course of action for specific market populations (Nandi, 2014). Traditionally, business intelligence was meant to; analyze vendor relationships, sales trends, effectiveness of marketing, customer habits, market behavior and manage organizational finances as well as predict demand (Shaw, 2011). However, with the development of intelligent systems, the scope of the practice has grown with the systems having broader applications. These include tracking of service delivery, sharing of information of between professionals like doctors and safeguarding the privacy of individuals’ information (Shaw, 2011). Nevertheless, the data management systems of today that have enabled large data storage in small spaces with ease of access has greatly furthered manipulation of data to create predictive models as a goal of business intelligence. Data as a predictive tool For centuries, businesses have been analyzing information and data as a method of consumer behavior prediction. The prediction was also intended for other important task behavior. This mainly incorporated statistical methods and data mining analysis that provides relative certainty in predictions (Ayres, 2008). This developed the notion that data management and analysis replaces human intuition in many business intelligence areas. This is because computers and other intelligent systems may do the tasks of decision making where there are good structures. This is through the following information systems: i. Decision support systems (DSS) These are business intelligence systems that recognize that an essential part of the any decision making process is played by human intuition. As such, the system only seeks to provide the human decision maker with the necessary tools (information) that will enable him or her to make an informed choice that will help solve the problem (Nandi, 2014).the concept in this is that the systems do partake in the actual decision making process. ii. Expert systems These are intelligent systems that serve as predictive tools that are properly structured to analyze problems and provide solution recommendations without the need of human expertise input (Thierauf, 2001). The data mining analysis enables the system to filter the information in the database and predict trends. In the medical industry, such systems quantify diagnostic questions (Ayres, 2008). Expert systems may, however arise from DSS as the intuitive input in the latter may turn out to be application of set of decision rules that are well structured. Business intelligence in predictive models The applications of data mining go beyond the periphery of consumer behavior prediction. There are systems developed to analyze known consumer behavior patterns with the purpose of extrapolating them and making precise recommendations on the possible purchases of the future. Known as recommendation engines, these systems have been adopted by various online services offering companies like Amazon (Nandi, 2014). Another sector that uses predictive models of business intelligence is the banking industry. Normally, loan officers in a bank have access to the same information. However, the officers have different intuition levels that enable them to establish the loan repayment capabilities at different levels creating diversity in the service they offer. This means that those with low intuition capabilities may make mistakes by giving loans to people who are not able to pay. This saw the development of a system that one can feed information on a customer in a computer with the system providing recommendations after comparing the customer to a cohort (Nandi, 2014). Business intelligence in search functionality Data management’s main feature is the ability to store, manage, consolidate and easily provide data of any amount (Thierauf, 2001). Without a business intelligence feature that acts as a data manager, it would be possible to store the data and may be arrange it through a tedious process. But suppose in situations where data is arranged in alphabetical order with numeric order subdivisions one wanted to access file Q, 1234. He or she would have to scroll through all the alphabets and numbers before the said file before reaching it. This may take a long time and may be tiresome, which will reduce the efficiency of the organization. Enter the search engine. Innovations in business intelligence systems have seen vendors include options in their products that enable one to search and jump to financial and operational files as well as general files found in any database. The incorporation of such appliances as Google search has enabled the easy location and access to files in remote as well virtual databases like the cloud (Shaw, 2011). Business intelligence in banking The Bank of India was among the first banks in India to incorporate technology its operations using computers and an ATM as early as 1989 (Bitterer, 2010). The growth in operations saw the volume of data handled multiply. To alleviate inefficiency and redundancies, the bank incorporated a Core Banking Solution in all its branches in 2009 (Bitterer, 2010). It also incorporated a system of Corporate Performance Management that made the whole organization to have only one Operational Data source. The aim was to create a portfolio analysis that was fully fledged across its products. Since the conventional design and development procedure would take over 2 years, the bank decided for a pre-built analytic solution to serve as the organization’s business intelligence (Bitterer, 2010). The bank installed Ramco Banking Analytics software that integrated all available information of the bank. The software provided outputs of Key Performance Indicators, scorecards, user specific dashboards, graphical visualisations of data and analytical reports (Bitterer, 2010). All these mechanisms facilitated fast, informed and easy decision making as well as the safeguarding of the organization’s interests through the procedures discussed below. The first effect was the ease of feeding data into the database. The analytics software would automatically feed the information dealing with any transaction filling in the unspecified information that could be extracted from the pre-existing software. The incorporation of data mining through application of the BI software enabled the development of an all round view of the data for informed decisions (Bitterer, 2010). This provided quick serving of data to the employees for fast decision making. The BI systems are used to analyze historical trends. The analysis takes into consideration information concerning employees, sales, expenses, deposits profit, sector, timing, customer profile and products and interprets them into metrics of profitability (Sahu, 2012). It also provides a better understanding of the needs of customers while showing the gaps to be filled. Market evaluation is also provided from the history, providing recommendations on the changes to products and projected performance (Sahu, 2012). BIs also serve to manage the relationships with customers (Sahu, 2012). As discussed above, the BIs provide a better understanding of the customer needs, behaviors and preferences. Initially, the customer relationship dealt with the banking relationships alone. However, BI has enabled Bank of India’s customer relationship management to be even more personal as the increased understanding enables the improvement of services, products and market exploitation strategies (Bitterer, 2010). This is because the systems enable transformation of customer oriented business models from product centered models. Data mining enables the extraction of personal data to provide insights into possible customer demands of the future as well as identify profitable customers while filtering out those who are not. Then there is the ability of BIs to profile customers from their attributes saved in the database enabling the bank to offer specific products to specific customers. In the banking industry, both financial and non-financial risks are part and parcel of operations. Mishandling or mismanagement of both customer and operational data may result in the occurrences of these risks. However, the integration of BI in the banking operations mitigates the probability of them happening. In the case of credit risks where there is the probability of borrowers defaulting, a BI creates a database that contains borrowers posing great risk. This is through the analysis of past transactions (Bitterer, 2010). As stated above, this helps loan officers and the bank in general in having a refined process of determination of who to lend to. The systems also enable the sharing of information with other financial institutions so that the analysis is almost foolproof. In the case of marketing risks where the bank is overexposed to market variables fluctuations, study and analysis of past data is enabled. This highlights situations where it is necessary to lower exposure. At the same time the liability of assets is easily managed. Finally, BI and database management allow banks to check operational risk resulting from fraud and human error by allowing proper and efficient auditing (Bitterer, 2010). At the same time the risk is alleviated through the keeping of efficient and up to date records of stakeholders’ transactions and highlighting deviations from the norms. Issues of business intelligence The first issue has to do with data cleansing. The quality of data stored and managed by any BI depends on the individuals that fed it into the system (Nandi, 2014). It is only as good as the input. Slight and common errors while inputting the information deteriorates the quality altogether. Since it is common to make keyboard errors, most of the data stored in intelligent systems is compromised to some extent (Nandi, 2014). As such, it is necessary to clean and standardize all information in order to attain meaningful and correct information. Although it goes against the aim of using the system altogether, this is issue is minimal as the benefits outweigh it by far. The other issue is concerned with the summarization of data. The problem is that large volumes of data are used in computing aggregations. Even simple questions require millions of data blocks a sizeable computing power (Thierauf, 2001). Even the fastest computers in the intelligent system requires the availing of this information at the fingertips. This means, then information needs to be summarized prior to the computation which takes up a significant amount of time. However, some BIs support drill down mechanisms that analyze a cohort as a whole providing information at successive usage instances providing the information in real time. Setting up a business intelligence system does not come in cheap. One needs to first of all buy and put in place the necessary tech in terms of hardware. This includes servers, storage disks, computers, etc. Generally, this will require a significant amount of finances depending on the location of the business. This is before one can contact a firm to install the software. After the costs of purchase and installation of the set up, the staff needs to be trained on how to deal with the new tech which also requires financing. Even if one decides to employ properly trained staff, their wages will see the cost of running business raise. This is because professionals like data mining analysts are some of the most paid people in the information systems industry (Nandi, 2014). All this shows the initiation of the business intelligence models is a costly affair. However, like any good investment, a good business intelligence system will guarantee increased profits. This is because of the increased efficiency of the system and ease of operation. Conclusion A business intelligence system comprises of a set of techniques and tools that convert raw data into analytical information that is useful in business. The success of the business system depends on the data management used. The incorporation of a good business intelligence results in growth of the business due to the improvement of mechanisms of operations that improve efficiency. The practice has been adopted in the banking industry with the Bank of India providing an example of the success it brings. Business intelligence also has faults in terms of data cleansing and summarization requirements. The cost of setting up the system may also be costly depending on the location. However, the benefits outweigh the faults. References Ayres, I. (2008). Super crunchers: How anything can be predicted. London: John Murray. Bitterer, A (2010). What Can a Business Intelligence and Analytics Solution Do for Your Bank?. Banking On Business Intelligence, (2). Nandi, V. (2014). Maintaining Database: Business Intelligence Tool for Competitive Advantage. Business Intelligence Journal, 5(2). Sahu, R. (2012). Business Intelligence for banking. Retrieved on 5 November 2014 from http://www.infosys.com/finacle Shaw, R. (2011). What is Business Intelligence?. Database Trends and Applications. Retrieved on 5 November 2014 from http://www.dbta.com/Editorial/Trends-and-Applications/What-is-Business-Intelligence-73502.aspx Thierauf, R. J. (2001). Effective business intelligence systems. Westport, Conn. [u.a.: Quorum Books. Read More