Business Intelligence Justification - Case Study Example

Business Intelligence Justification Name: Course: Instructor: Institution: Date: Executive summary Business intelligence often abbreviated as BI refers to the set of techniques and tools used to transform raw data into meaningful and useful information for business analysis purposes. Business intelligence offers a great opportunity for managers to evaluate their performance, analyze their organizations progress towards organizational mission and objectives and make effective relevant decisions regarding on the status and condition of the organization at that instance in time. Mifflin-Dunder Regional hospital is currently faced with a number of data related issues. The organization does not have a central data bank and therefore each department processes and acts on its own data. This has eventually inhibited effective execution of managerial policies such planning, economic resource allocation and rational approach to staffing. The missing link between the various data sources within the organization makes it to streamline and synchronize organization’s activities. Mifflin-Dunder Regional hospital therefore is justified to adopt and implement an operational business intelligence system at its premises to help solve the current data related issues. Even though there might be risks associated with the implementation process, there is a great potential and viability chance of business intelligence system at Mifflin-Dunder Regional hospital. Such a system will also help the organization make considerable savings incurred towards manual data processing. Analysis of the hospitals strategic information needs that are not currently addressed adequately Certain vital requirements of organizational data have not been adequately addressed by the current practices as at Mifflin-Dunder Regional hospital. They include: i. Data warehouse; the point where an organization collects its data on a number of applications and data stores across the organization. A data warehouse acts as the central store and information bank for all types of data from all quarters of the organization. This central data storage point is subsequently missing within the structure of Mifflin-Dunder Regional hospital ii. Data integration. This refers to the unification of data from various sources to form a data warehouse. Data integration offers a central access point of all data within the organization. Data as well as information at Mifflin-Dunder Regional hospital is not integrated and each department operates on its own data. iii. Analytics services. This refers to a collection of analytic tools used to design and create analytical files that make it easier and quicker to access and retrieve data. This also has not been subsequently addressed by the current operational structure at the hospital since Kelly cannot even access the relevant information needed for expansion purposes. The viability of delivering a BI solution at Mifflin-Dunder Regional There is a great potential and survival chances of a business intelligence system streamlining data handling and manipulation at Mifflin-Dunder Regional hospital. The facility does not have the required information in the first place hence the idea of such a system will necessitate the collection of the relevant data. Another viability reason is the fact that the hospital currently operates on unsynchronized and non-streamlined data. For instance, the human resource department must wait until an employee has retired before he is replaced, there is no such data to indicate when such retirement is due and therefore prepare a possible replacement. A business intelligence system will therefore greatly help synchronize such data for efficient operations at the hospital facility. Risk assessment associated with delivering a BI solution Implementing a business intelligence system at Mifflin-Dunder Regional hospital may not be a welcome idea to all stakeholders. As it is expected with most new policies, there is always an element of internal resistance which often comes up. In fact resistance to change is the initial risk associated with business intelligence systems. This comes up often as a result of poor communication strategies within the organization. Resistance is common where managers force down police on subordinates without sufficient communication. Another probable risk of business intelligence as at Mifflin-Dunder Regional hospital might easily be identified as poor data quality. The system feeds works on Garbage In Garbage Out philosophy. (GIGO). Any wrong information fed into the system will eventually generate meaningless and inaccurate reports. The hospital facility should thus adequately clean the available data and implement stringent data change management policies before going live with the business intelligence system. The other risk most likely to be encountered is over expectation from the system. Due to the current data mess at the facility, stakeholders might expect tremendous and in fact instantaneous changes from the business intelligence system.in such cases, most managers are often duped by crafty IT technicians to install systems that do not meet up the facility’s standards hence total failure. Benefits assessment associated with delivering a BI solution The implementation of a business intelligence system at Mifflin-Dunder Regional will come along with a number of benefits. The fundamental four benefits of such a system if successfully implemented at Mifflin-Dunder Regional are as discussed below. i. Ability to make predictions and respond to trends in the market An operational business intelligence system will enable one to analyze the organization, competitors and use such information to make subsequent adjustments to measure maximum returns. It will also provide an avenue to monitor and track the performance of the organization. The good thing with such a system when used to make analysis is that one explores scenarios with a click of a button. ii. Subsequent reduction in labor costs especially generating reports. An initial consideration of business intelligence systems is that they have ability to point out areas of waste or loss that may have previously gone unnoticed in a large organization Implementation of a business intelligence system will make it possible for automatic collection and aggregation of data hence ability of the system to automatically generate reports upon prompt. A further advantage is the a system coms along with report design tools and therefore makes it extremely easy and simpler to program other new reports as intended. Business intelligence system will also do away with the extra expenditure of training personnel to develop and maintain reports at Mifflin-Dunder Regional and therefore, besides the initial installation costs, no other subsequent expenditures will be incurred. iii. Business intelligence will reduce information bottlenecks within the organization A functional business intelligence system will reduce dependency on the IT team for generation of reports. This is possible since the system allows each end user to generate reports when they require them without necessarily requesting for processing or preparation from any other department. With subsequent authorization, the system also allows one to design new reports according to the required specifications. iv. Improved and faster decision making. High quality decisions increase the revenue gains of any given organization while poor quality decisions consequently result into losses. The quality of ideas made and taken to a large extent depends on the available data for consideration and analysis. Business intelligence system therefore provides decision makers with a wide range of exact and up to date data for the purposes of decision making. It also allows users and decision makers browse through large volumes of data for further investigation and consideration. On the other hand, business intelligence will increase the decision making speed at the facility by allowing combination of multiple data sources in common reports and thereby the user from the extra time and workload of manually assembling data in spreadsheets. The system also provides an analytical and ad-hoc reporting capability which makes it extremely easy to retrieve new or different data combinations as might be required. Conclusion The case of Mifflin-Dunder Regional hospital is a clear example of inefficient organizational structure that emanates from inadequate data handling techniques. This has gone down to affect vital aspects of the organization such recruitment and placement as well as decision making and policy implementation. The inability to obtain, process and analyze data eventually affects the final decisions made by such policy makers and implementers. For instance, Kelly in the cases study is unable to implement the plan to transform the facility into a multi campus hospital due to missing data on the relationship between demographic information about the region and the internal hospital resources. This therefore calls for improvement in the general data soliciting, [processing and storage approaches at the facility so as to enhance implementation of such policies and developmental decisions. Recommendation The current state of affairs at Mifflin-Dunder Regional is due to inefficient data from the management system which in turn makes it hard for Kelly to make managerial policy decisions. This is further reiterated by Jim Halpert, the officer in charge of resource allocation who claims that even in the presence of key performance indicators, the hospital does not have means of monitoring performance. The initial recommendation would thus be for the hospital management communicates the current level of inefficient operations currently experienced at the facility and the intention to bring in a new system that will help solve such inefficiency. Such communication will greatly reduce incidences of internal resistance given that business intelligent system to be implemented might be not familiar with a large employee fraternity. Another recommendation for the management is to design realistic organizational goals that will guide the performance of each employee within the organization structure. This should then be followed by identification of keep performance indicators for the purposes of progress evaluation and monitoring. It is also hereby recommended that the management of Mifflin-Dunder Regional adopt and install operational business intelligence system within the hospital premises. The system, together with realistic goals and key clearly defined key performance indicators will provide efficiency in data handling as well as provide the standards on to which the performance will be evaluated and rated upon. Finally, business intelligence should also be implemented, not only for the purposes of efficient data handling but also to help Mifflin-Dunder Regional cut down on resource wastage and; losses as identified by Kelly. He desire to rationalize both resources and the staff can effectively be provided for by an operational business intelligence system at the hospital. References AANDERUD, T., & HALL, A. (2012). Building business intelligence using SAS content development examples. Cary, N.C., SAS Institute ANANDARAJAN, M. (2004). Business intelligence techniques: a perspective from accounting and finance ; with 18 tables. Berlin [u.a.], Springer. KHAN, H., SCREEN, C., & WARD, A. (2012). Oracle Business Intelligence enterprise edition 11g a hands-on tutorial. Birmingham, Packt LOSHIN, D. (2003). Business intelligence: getting onboard with emerging IT. Amsterdam, Morgan Kaufmann Publishers. SABHERWAL, R., & BECERRA-FERNANDEZ, I. (2010). Business intelligence: practices, technologies and management. Hoboken, N.J., Wiley. SAUTER, V. L. (2011). Decision Support Systems for Business Intelligence. Hoboken, John Wiley & Sons SCHEPS, S. (2008). Business intelligence for dummies. Hoboken, NJ, Wiley. 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