Category: Business Intelligence

Introduction

Textile Company A is a small business in North Carolina that is currently struggling due to the changing global environment. The organization is engaged in the purchase of textiles in Europe, the Middle and Far East, as well as their distribution in the United States. The complication of global supply chains leads to a constant increase in the cost of purchasing textiles, which negatively affects the companys profits. To overcome this crisis, Textile Company A plans to expand its operations to South America and Canada. In this regard, the company needs to upgrade its information infrastructure for better data analytics within the company.

Prior to the implementation of the project to create a business intelligence (BI) system, the organization used an outdated approach to storing and analyzing data. It was minimally automated and did not allow working effectively with a large amount of data. In particular, the company used several separate data sources (CRM, ERP, individual databases, and external sources) and generated individual reports based on the data received from each of them. This approach made holistic data analytics and information-driven decision-making more difficult. A diagram illustrating the old data analytics system at Textile Company A is presented in Appendix A.

As part of the project, it was possible to create a new BI infrastructure in the organization, which fully automated the companys interaction with data. It was possible to create a structure consisting of ETL tools (Extract, Transform, Load), a data warehouse, online analytical processing (OLAP), and data marts. A detailed scheme of the BI infrastructure created for Textile Company A is illustrated in Appendix B. As a result, this infrastructure uses all the companys data sources for analytics and generates the most informative reports for different organization reports. Additionally, BI helps in structuring data and filtering out unnecessary information. Textile Company A can now use the framework for descriptive and predictive analytics. This allows the company to streamline existing processes and make better decisions for future expansion planning.

The project started with an evaluation of the data analytics structure that existed in the company. After assessing the available resources and the needs of the organization, decisions were made regarding suitable storage solutions as well as BI tools. Further, the BI infrastructure was planned, and the steps for its implementation into the existing information structure of the company took into account all the necessary changes. After that, BI was implemented and integrated into the analytics structure of Textile Company A. At the last stage, tests were carried out to evaluate performance, and employees were trained to work with the end-user interface.

Review of Other Work

The materials considered in the project proposal made it possible to effectively plan the development and implementation of the BI solution. However, during the implementation of the project, additional research was required to identify points for improving the existing design. The paper by Dieni et al. (2021) describes the process of creating a BI infrastructure for unemployment rate analysis and management for the Central Bureau of Statistics of Indonesia. Although this article does not belong to the scope of SMEs, it provides a detailed description of all stages of the process of developing and implementing a BI infrastructure. Dieni et al. (2021) pay great attention to the development stage of the ETL tool, which, as emphasized, is the most significant for the correct operation of the BI structure. Based on the analysis presented in the article, it was decided to use the Pentaho BI solution as the main ETL tool, as well as a resource for the cloud data warehouse. This approach can both reduce the cost of creating a BI infrastructure and provide a company with additional options for the future expansion of an information-analytical system.

During the implementation of the project, the possible use of alternative solutions to create the most balanced infrastructure was also considered. At the planning stage, it was decided to use a hybrid approach to the data warehouse: both cloud-based and physical storage. Garani et al. (2019) analyzed the case of implementing the BI solution in a telecommunication company. They focus on the design of a data warehouse that would be effective for all the business tasks required by the organization. As part of the research, Garani et al. (2019) propose the scheme of the data warehouse containing five dimensions of data sets divided into different categories (customers, calls, contracts, employment, and product). Most importantly, they explain how each of those data sets is related to OLAP structures, which are used in the current project as well. This study showed how to structure a data warehouse information system in the most efficient way in order for it to work best with data OLAP. Additionally, the article confirmed the need to use cloud-based storage for interaction with OLAP in conjunction with a physical warehouse.

On the whole, OLAP and data warehouse design and implementation are the most challenging parts of the project. At the stage of developing these parts of the infrastructure, many questions arose regarding how best to organize these elements relative to each other. Al-Aqrabi et al. (2019) present a case study on the integration of OLAP into the BI infrastructure. Most valuable is that in this article, the researchers offer a detailed description of the OLAP framework and describe the elements that interact most effectively with various types of data warehouse solutions. The use of cloud-based BI data storage when integrating OLAP requires compliance of the BI application with web services architectural standards (and the standards defined by the SaaS or PaaS provider, like Google Apps standards) (Al-Aqrabi et al., 2019, p. 81). This information is extremely important for the implementation of the project, as it allows you to take into account the compatibility of infrastructure elements.

Thus, the literature reviewed in the project proposal helped in the analysis and evaluation of the existing analytical structure of the company, as well as in the selection and design of a relevant BI system. However, during the implementation of the project, it was necessary to optimize the design of the infrastructure in the most efficient way so that it meets the needs of the company and uses a limited amount of resources. The materials considered during the implementation and described in this section made it possible to take into account the subtleties of the design of BI infrastructures that were not obvious during preliminary planning. The study of these sources will help create the most effective structure and avoid possible difficulties with its operation.

Changes to the Project Environment

Before the implementation of the project, the analytical infrastructure of Textile Company A was extremely outdated and required an upgrade in accordance with modern technical standards. The company used manual data analysis of the companies listed in the databases, which was carried out by the analytical department. After that, based on the analytics, reports were compiled for the company as a whole without taking into account the needs of individual departments. This structure was slow and inflexible and could also contain a large number of errors and irrelevant information. This situation made it impossible to effectively optimize the companys operations, as well as plan for expansion.

The BI infrastructure implementation project has significantly changed the companys decision-making process based on the received analytical data. Most importantly, data analytics has become as automated as possible, which has increased the efficiency of using the information in the company at times. In particular, the ETL tool made it possible to filter out irrelevant data, which minimized the number of errors. The inclusion of March data in the BI infrastructure has allowed departments to receive personalized analytical reports based on their needs. Additionally, the use of OPLA cubes has made the structure extremely stable in the event of a future increase in the number of databases and an increase in the number of queries.

Overall, the implementation of the BI infrastructure has allowed Textile Company A to use descriptive analytics to optimize its operations and predictive analytics to plan the companys expansion. It is also important that employees of various departments have access to multidimensional information for better decision-making. Data analytics in the company has become efficient, eliminating errors and providing the most relevant and important information. This system can become the basis for the future development of the company and a significant increase in its performance.

Methodology

ADDIE methodology model was chosen for the project execution as it presents the most appropriate approach. This model includes five crucial steps: analysis, design, development, implementation, and evaluation. The main advantage of this structure is the sequence of steps and flexibility in the implementation of the project. In particular, ADDIE involves evaluation after each of the major stages of project execution, which allows you to adjust its development depending on the emerging conditions.

The analysis stage includes goal-setting and the resources available for the project. This step also identifies the problem the business has and offers a set of potential solutions that could address it. At the design stage, the specific tools that will be used within the project are determined, deliverables are developed, as well as project evaluation criteria. Those two steps have already been completed under this proposal and are described in the project overview sections, which contain a description of the problem and the relevant IT solution.

The development stage started with the assignment of the analytical team, which will work with ready-made BI. This step also includes the choice of the warehouse, the analytical architecture of the system, and the end-user interface, as well as setting data integration tools. The implementation of the project will include the creation of a fully functional BI structure for Textile Company A and the completion of the onboarding of the analytical team. The final stage of the project is evaluation, which requires tests to create informational reports for various departments.

Project Goals and Objectives

Goal 1: BI infrastructure features assessment and planning. At this stage, it is necessary to evaluate the existing resources, infrastructure, goals, and responsible persons, as well as a potential vendor for the implementation of BI. This goal has been met when all key aspects of the BI solution are selected and clearly defined based on available data and based on the objectives of the organization. This goal included two objectives:

Objective a: BI implementation strategy development. This objective includes activities to evaluate the companys current resources and infrastructures and appropriate changes that have to be made in order to achieve the companys goals. The objective was considered successful when all the decisions regarding BI infrastructure design were made, including the choice of relevant and available tools.

Objective b: Defining key BI characteristics. This objective allows the project to scale the required BI structure and exclude features that the company does not need. At this stage, it is important to assess the competencies available to the organization, as well as the KPIs that are key to monitoring. The objective was considered successful when the complete diagram of the BI infrastructure was created.

Goal 2: BI infrastructure design and implementation. This goal is key within the framework of this project, as it involves the creation of a functioning BI infrastructure as a result. This objective is supported by three objectives, which include setting data integration tools, BI architecture design, and end-user interface implementation. These objectives describe the key aspects of the software architecture BI solution and constitute a fully functional structure.

Objective a: Setting data integration tools. For the correct operation of BI, tools for collecting, processing, and storing data are required, which requires the choice of data warehouse and data integration tools. At the moment, cloud data storages are the most common, which is especially important for SMEs. The objective was considered successful when the data integration tool was installed and connected to the companys databases.

Objective b: Choosing an approach for architecture design. At this stage, it is necessary to decide, based on the needs and resources of the company, which BI architecture is the most relevant. In this situation, the company plans to expand, so it would be logical to use a hybrid setup (warehouse + OLAP + data marts) to increase the potential duration of the BI solution. The objective was considered successful when the data warehouse, OLAPs, and data marts were installed and connected to the ETL tool.

Objective c: The end-user interface implementation. At this stage, it is important to choose a tool that will allow the company to effectively represent the collected data in the form of analytical reports for employees. The objective was considered successful when the end-user interface was installed and connected to the BI structure.

Project Timeline

The main stages of the project implementation took place according to the schedule, as the project team worked well-coordinated and according to a well-thought-out preliminary plan. However, the delay occurred during the BI infrastructure setting phase, which was scheduled to be completed in 10 days. Instead, the process took 12 days, as there were difficulties connecting the companys databases to the ETL tool, which required additional research and planning. Difficulties arose due to the incompatibility of existing databases with the selected ETL tool. After reviewing the materials described in the review of the work section, it was decided to switch to Pentaho ETL solution, which required additional settings. Thus, the project implementation ended on 10.32.2022 instead of 20.20.2022 with a delay of less than 2 days.

Unanticipated Requirements

An unanticipated requirement that arose during the implementation of the project was the need to choose a more relevant ETL tool. The problem arose during setting up the BI infrastructure and testing its operation. Initially, it was planned to use the Apache Spark tool as an ETL since it is free and easy to implement. However, later it was discovered that this Apache Spark does not correctly interact with various file formats, including PDF, which are widely used in company databases. This problem led to incorrect data filtering, which drastically reduced the effectiveness of the tool. Thus, it was decided to choose a more suitable instrument, which resulted in the need for additional research and a slight delay. As a result, it was decided to use Pentaho because this structure is able to work with a variety of files, and it is also widely considered in the literature and is popular in case studies. The main reason for choosing this solution was the widespread use of Pentaho among SMEs, which is most relevant for this project.

Conclusions

The main result of the project implementation is the creation of a fully functional BI infrastructure in Textile Company A. The potential effect of the project is the ability to effectively use descriptive and predictive analytics in the organization to optimize current operations and plan for expansion. Among the immediately observed effects, one can single out the complete automation of the process of storing, analyzing data, and generating information reports. Most importantly, the ETL tool integrated into the infrastructure allows the company to minimize the amount of irrelevant information through the sorting of data in the databases used. The project is considered successful because, as a result of the implementation of the BI infrastructure, it became possible to generate multidimensional informational reports that various departments of the company can use for a more efficient decision-making process.

Project Deliverables

Appendix A shows a diagram of the data analytics structure that existed at Textile Company A before the project was implemented. Previously, the company used manual data analysis and the generation of information reports, which were handled by employees of the analytical department, which resulted in low efficiency of data analysis. Appendix B illustrates the BI infrastructure diagram that was created at Textile Company A as part of the project implementation. This scheme describes all the key elements of the system and their interaction. Based on this illustration, it can be judged that the project made it possible to automate the process of data analytics in the company in order to increase the efficiency of the decision-making process based on reports. Finally, Appendix C contains an example of a multidimensional report that is generated by the BI infrastructure created within the project. This illustration is the most important deliverable of the project, as it shows the actual result of its implementation.

References

Al-Aqrabi, H., Liu, L., Hill, R., & Antonopoulos. (2019). Cloud BI: Future of business intelligence in cloud. Journal of Computer System Sciences, 81(1), 85-96.

Dieni, T. O., Tania, K. D., Fathoni, M., Jambak, I., & Putra, P. (2021). Business Intelligence for unemployment rate management system. International Journal of Informatics and Computer Science, 5(2), 111-117.

Garani, G., Chernov, A. V., Savvas, I. K., & Butakova, M. A. (2019). A data warehouse approach for business intelligence. 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 70-75.

Appendix A

Data Analytics System in Textile Company A before the Project Completion

Appendix B

BI Infrastructure for Data Analytics in Textile Company A after the Project Completion

Appendix C

The Example of Multidimensional Report Generated by BI Infrastructure Visualization Tool

Abstract

Business Intelligence System (BIS) is a fundamental tool that makes businesses enhance strategic competitive advantages. This paper demonstrates the advantages of implementing a business intelligence system to monitor employees’ productivity, and monitoring employees’ performances provide a unique tool for measuring organizational performances. The report reveals that Jefferson was facing unique challenges of monitoring employees’ performances, however with the implementation of BIS, Jefferson medical center can improve its financial performances, employees’ efficiencies, and overall management strategic decisions.

This report enhances the knowledge of business managers, government, and scholars on the advantages of business intelligence systems on the performances of business organizations.

Introduction

The increase in competition in the business environment has mandated business organizations many business organizations to initiate a policy of Business Intelligence system (BIS) into their business strategic decision to enhance business competitive advantages. Businesses have realized that modern business transactions have seen the need to document their transactions using automated methods, and there are needs for managers to be informed on the day-to-day of their business organizations to be able to make informed decisions. (UQS, 2009). Afolabi and Thiery define Business Intelligence System as the process of collection, treatment, and diffusion of information to reduce uncertainty in the making of business strategic decisions. (Afolabi, Thierry nd). A strategic decision is what enhances business performance. Business Intelligence System aids management to make an informed decision, through analytical tools, and techniques for conducting data analysis. (UQS, 2009).

The main objective of this research is to examine the case study of Jefferson Medical Centre in the implementation of business intelligence (BI) software for the improvement of its business performance. Typically, BIS permits management to implement a relational data warehouse, where relational database management can allow the organization to access multiple data to aid the decision-making process. (Afolabi, Thierry nd). It should be noted the decision of Jefferson Medical center in the implementation of business intelligence has improved its business performances and has increased its critical business accomplishment. (Anderson-Lehman, et al nd).

The rest of this paper is structured as follows:

First, the paper examines key drivers and business cases for implementing a business intelligence system at Jefferson Medical Centre.

Additionally, the paper examines the key challenges that Morie Mehyou would have faced in

Implementing a business intelligence system at Jefferson Medical Centre.

Moreover, the research analyses two key factors which Jefferson Medical Centre needs to consider to maximize the utilization of its business intelligence system.

Furthermore, the paper provides the criteria Jefferson Medical Centre can use to measure its business performances using a business intelligence system

Finally, the research designs a basic balanced scorecard with one key objective and an associated key metric for each of the four dimensions of the balanced scorecard.

The decisions of Jefferson Medical Centre to implement a business intelligence system are to measure productivity, next section provides key drivers for the implementation of BIS at Jefferson Medical Centre.

Key drivers and business case for implementing business intelligence

The system at Jefferson Medical Centre

Jefferson Medical Centre was influenced to implement a business intelligence system because of the problems that Jefferson Medical Centre experienced when the organization faced some unique challenges with the increase in the number of patients in the hospital. However, most of these patients were low-income patients, and Jefferson Medical Centre did not have health care new technology to improve its services. Moreover, employees’ productivity was very critical to organizational performances, but Jefferson Medical Centre did not have an adequate facility to monitor employee productivity. (Tan, Lane, 2009). As being argued by Jones and Kato (2003) employee productivity is a fundamental tool in measuring organizational performances, and to measure firms’ performances, there is a need for a modern method of measuring productivity through enhanced efforts of employees. (Jones, Kato, 2003).

Thus, Jefferson Medical Centre did not have a modern method of measuring employee performances and overall organizational performances. These factors also affected management decisions on strategic issues of the company. Moreover, Jefferson Medical Centre had problems with modern methods of data storage, and data retrieval, which can enhance management performances. Jefferson largely relied on the outdated main frame of retrieving data such as paper reports, and financial statistics, and data retrieved from the mainframe may be up to two months olds, and these types of data could not inspire confidence, nor improve productivity. (Tan, Lane,2009).

It should be noted that for businesses to properly analyze data, and make an analytical business decision, there is a need for the implementation of an automated computer system that can enhance organizational performances. Using standard automated specialized software will allow a large volume of data to be extracted and processed, and will increase the speed at which information is available for management decision making. (UQS, 2009).

Thus, the management of Jefferson Medical Centre believed that with the implementation of a business intelligence system, management would be able to measure employees’ productivity and organizational performances. At the same time, Jefferson Medical Centre would be able to have access to reliable financial data that would enhance management strategic decisions. (Tan, Lane, 2009).

However, it is well understood that Business Intelligence can increase analytic management decisions, however, there are still key challenges in the implementation of business intelligence. The next section discusses challenges Jefferson would have faced in implementing business intelligence.

Key challenges that Morie Mehyou would have faced in implementing a business intelligence system at Jefferson Medical Centre

This section examines the key challenges Morie Mehyou would have faced in implementing a business intelligence system at Jefferson Medical Centre.

Some of the key challenges are as follows:

First, a major challenge that Morie Mehyou would have faced is a technical challenge. Typically, the technical challenge in implementing BIS involves the design cost of the real-time warehouse of business intelligence, it should be noted that processing transactions are complex, which requires aggregating data into analytic information. (Band, 2003).

It should be noted that Jefferson Medical Centre relied on funding from government and insurance payment and this was reflected from Morie Mehyou’s statement as he said “As the hospital has no private endowment, it relies for funding mostly on government and insurance payments – not exactly cash cows. At the same time, Jefferson is constantly trying to invest in new healthcare technology and improve its services. Productivity is critical,…..but there was a problem.” (Tan, Lane, 2009, p 21).

Another technical challenge that Morie Mehyou would have faced was the management of the data warehouse. The warehouse requires an experience database administrator for the management of BIS. The database administrator must have strong technical skills in software, and networking to be able to manage the data warehouse of Jefferson Medical Centre. Apart from this, a database administrator should possess a strong understanding of business and decision-making to support the data warehouse of Jefferson Medical center. The requirements of the database administrator to manage the warehouse of the business intelligence system would have been a challenge to Morie Meyhou.

Another technical challenging issue of the implementation of the business intelligence system was the security and privacy of the data warehouse. It should be noted that information security is essential to maintain adequate privacy of data warehouse. Data in the warehouse are very sensitive for Jefferson Medical Centre. Without adequate security, a hacker may invade the data warehouse of any organization, and rendering data invaluable. Thus, providing adequate corporate security for the business intelligence system would have been a key challenge to Morie Meyhou. Providing adequate securities include techniques to restrict access to a data warehouse, and establishing effective corporate internal control to maintain adequate security and privacy. (UQS, nd).

Apart from technical challenges, Morie Meyhou would have also faced socio challenges in implementing business intelligence system for Jefferson Medical Centre. Morie Meyhou would have faced challenges of convincing employees of Jefferson Medical Centre that implementing business intelligence system would not affect their employment. It should be noted that there is general belief that a computer can do what thousands of people will do in year, computer will do within a day. With this general belief that implementing business intelligence system can result to lay off worker. Thus, Morie Meyhou would need to educate his employees that implementation of business intelligence system was to enhance their performances, and implementation of Business intelligence system was to measure employee’s productivity, and improvement of organisational performances. Another socio challenge that Morie Meyhou would have faced was that patients might find it difficult to secure hospital appointment online. Part of the implementation of business intelligence system for Jefferson Medical centre was to allow patients to secure appointment online. It should be noted that bulk of patients that frequent Jefferson Medical centre were low-income people, and most of them might not have knowledge of operating computer, and might not be able to secure appointment online.

Despite these challenges, Jefferson Medical Centre had already made decision to implement business intelligences system, nevertheless, Jefferson medical centre needs to maximise the utilisation of business intelligence system. Next section considers two key factors to maximise utilisation of business intelligence system.

Two key factors which Jefferson Medical Centre needs to consider maximising

The utilisation of its business intelligence system

This section discusses two key factors that Jefferson Medical Centre needs to consider in maximisation of business intelligence system.

In maximising business intelligence system, Jefferson Medical Centre needs to design its BI system to incorporate data warehouse. It should be noted that data warehouse has the advantages of storing large volume of data. (UQS, nd). This advantage allows Jefferson Medical Centre to combine data gathering, and data storage to improve the timeless and the quality of decision process. It should be noted that data warehousing is fundamental tool that determine success of business intelligence system. By developing real time data warehouse, the advantages would provide single source of information, which will provide employees quick access to key information that relate to patients’ accounting software, payroll software and many other internal systems.

In addition, Jefferson Medical Centre will reap reward from data warehouse because management would be able to have quick access to accurate and integrated data for enhancement of management. Meanwhile, data warehouse is a core to business intelligence, and by integrating real time warehouse, Jefferson Medical centre will maximise it business intelligence.(Anderson-Lehman,et al nd, Tan, Lane,2009.).

Second factor that Jefferson Medical centre needs to consider in order to maximise it business intelligence system is integrating online analytic processing (OLAP) in their business intelligence and this will improve data visualisation, and decision making. Typically, with examining many data in complex relationship, Jefferson Medical centre will be able to analyse data in patterns, and have ability to analysis data visually. With the implementation of OLAP, it is possible to compare data in year-to-year or quarter to quarter. ( Lane,2009.).

Integrating data warehouse and online analytical process will enhance performances of Jefferson Medical centre. Next section examines how Jefferson Medical centre measure its business performances using business intelligence system.

How did Jefferson Medical Centre measure its business performance using a business intelligence system?

Measuring employees output is a fundamental key factor of measuring business performance, and overall organisational growth. Typically, business performance management is a real-system that alert manager on the opportunities that an organisation has been reaped , and this also reveal the impending threats that an organisation is facing, and this will help management to plan for the process of integrating business strategies to alleviate these problems. (Lane, 2009).

To measure performances of Jefferson Medical Centre using BIS, it is essential to examine problems Jefferson Medical centre was having before implementation of

BIS, and compare it with after implementation of business intelligence system.

Problems of Jefferson Medical centre before implementing business intelligence software

Jefferson medical centre had many problems before implementing business intelligence system. First, Jefferson medical centre did not have any means of measuring its employee’s productivity, and this was one of the methods to measure performance of Jefferson medical centre. Without having modern, and automated modern method to monitor employees’ productivity, there was no way Jefferson medical centre can distinguish employee performances.

In addition, Jefferson Medical centre had problem of making analytic decision that can influence an organisational performance. Management did not have access to relevant data in order to make decision. The hospital made use of outdated mainframe to store, and retrieve data, and when a manager demanded for financial reports, the data were almost two month old, and the data did not inspire confidence or productivity. These problems led Morie Mehyou to make decision that hospital could improve its productivity, and lower its costs by using business intelligence system. (Tan, Lane, 2009).

Performances of Jefferson Medical centre after implementing business intelligence software

Jefferson medical centre implemented business intelligence system after approximately eight years. Management purchased a WebFocus BI platform from New York-based BI vendor. With implementation of business intelligence software, management is able to retrieve patient data with accounting software, and payroll. Moreover, management was able to use BI software to retrieve data for many internal systems, and these help management integrate third party sources such as benchmarking. This system helps management to compare hospital performances with others, and at the same time be able to measure its national average. (Tan, Lane, 2009).

In addition, Jefferson Medical centre also develops intranet with business intelligence software, and this make mangers from 75 departments from hospitals to log in, and have access to their group data. Moreover, there are real time operation reports where management has access to daily, weekly, and monthly financial reports. The report is no more two months old before reaching manager’s desk. With business intelligence software, Jefferson medical centre is able to monitor employees’ performances, and overall organisational performances. Thus, with the advantages of BIS, Jefferson Medical centre is able to measure its Financial, Customer, Internal processes, Learning and growth. (Tan, Lane, 2009).

Design a basic balanced scorecard with one key objective and an associated key metric for each of the four dimensions of the balanced scorecard (Financial, Customer, Internal processes, Learning and growth) and provide a rationale for each objective and metric.

From illustration of score card in fig 1, a scorecard provides financial, customer, internal business process, and learning and growth. Typical, scorecard is based on measuring these four perspectives.

The objective of using scorecard on financial metric is to measure financial performances of an organisation. Each organisation has balance scorecard to measure its financial performances. It through a metric that an organisation would be able to analyse its annual growth, and productivity.

Lane (2009) points out that majority of the organisation use financial metric to measure performances.

Apart from financial metric, the objectives of using customer in a scorecard are by accomplishing its mission on how an organisation should appear to its customer. Corporate appearance to customers is very important for organisational strategic position. Hence, an organisation must always achieve its vision of how it appears to its customers.

The objectives internal business processes are to satisfy shareholders, customers, and other stakeholders of an organisation. Satisfying stakeholder is among primary objectives of an organisation. Comments of stakeholders on organisation have great effect on organisation image.

Finally, Learning and Growth has objective of how an organisation achieves its vision, and sustainable to improve and change.

These are the four dimension of balanced scorecard (BSC) that an organisation can use for indicators of financial and non-financial performances. BSC measures the performances, and management system of an organisation. It is through BSC that an organisation translates its financial, customer, business processes and learning and growth objectives and goals into actionable initiatives. (Lane, 2009).

Conclusion

This paper demonstrates that business intelligence system is a policy that an organisation can use to measure productivity, measuring business performance, and achieving overall organisation growths. Report reveals that manual method of data processing has become obsolete in modern business transactions. This paper demonstrates how Jefferson Medical centre transforms its business, increase its productivity using business intelligence system.

The paper provides key drivers Jefferson Medical centre decided to implement business intelligence system. In addition, paper reveals key challenges Jefferson Medical centre would have faced by implementing business intelligence system. The report also discusses two key factors that Jefferson Medical needs to consider to maximise the utilisation of its business intelligence system

Moreover, the research analyses how Jefferson Medical Centre measures its business performance using a business intelligence system.

Finally, the author designs a basic balanced scorecard, and provide one key objective, and associated metric for Financial, Customer, Internal processes, and Learning and growth.

References

1. Afolabi, B, Thierry, O, (nd), Business Intelligence System user’s parameters: an application to a document database, Laboratorie Lorreine de Recherche en Informatique et ses Appllication.
2. Anderson-Lehman, R, et al (nd),Continental Airlines Takes Off with Real-time Business Intelligence, Teradata Student Network.
3. Band, J, (2003), The Business Intelligence Outlook, Making business intelligence work for its money, Business Insight Ltd.
4. Jones, D, C, Kato, T, (2003), The Effects of Employee Involvement on Firm Performance: Evidence from an Econometric Case Study, Social Science Electronic Publishing, Inc.
5. Lane, M, (2009), Module 8 Business analytics, CIS8008 Business intelligence systems, , University of Southern QueensLand, Australia.
6. Lane, M, (2009), Module 9 Business performance management , CIS8008 Business intelligence systems, University of Southern QueensLand, Australia.
7. Tan, W, Lane,M, (2009), Assignment 2 CIS8008 – Business intelligence systems, Introductory Book, University of Southern QueensLand, Australia.
8. UQS, (2009), Module 5: Business intelligence – overview of origins and architecture, CIS8008, Faculty of Business,University of Southern QueensLand, Australia.
9. UQS, (nd), Module 6 Data warehousing, Faculty of Business,University of Southern QueensLand, Australia.

Abstract

This paper will discuss and evaluate the significance of business intelligence from a manager’s and internal decision maker’s point of view. It would also cover an overview of what business intelligence is, what are its relevant and associative terms, what are its general uses, and especially how it is helpful to a company’s management. In the contemporary corporate sector set up, the competition among various business entities has gone far beyond. Companies use all tactics to meet this strategic competition on al levels. Business Intelligence has become inevitable now for survival in the business market nowadays.

Introduction

Most people do some form of analysis on a regular basis throughout their lives—when they buy a house, a car, or go on a holiday, for example. Every business also does some form of analysis, whether it is looking at productivity improvements, benchmarking, cost savings, or using the balanced scorecard.

However, change is passing by every business that is focusing internally and on information derived from internal data. Strategic and industry risk to any organization come from the external environment. Despite this, many organizations and their decision-makers today are investing a vast amount of resources in attempts to obtain the solutions to their (external) market and industry challenges via enhanced information technology (IT) and information systems capabilities. Management guru Peter Drucker (1997) has noted that managers have come to rely too heavily on computerization and systems and that this fails the manager when they cannot gather the necessary data in the first place. He further notes that a large number of executives spend all their time with data that is both internal and incomplete. Drucker concludes that the information that executives need the most is about the “outside world, ” and that most important decisions should be focused on data gathered about what is going on externally to, rather than inside, the company (Drucker 1997, 46-54).

Definitions of Business Intelligence (BI)

There are two popular BI definitions: one is broad and the other is narrower. The broad definition suggests that BI is information concerning the business environment in which a company operates (Prior 1998, 66-68), including customers, competitors, industry trends, public policy, and other STEEP (Social, Technical, Economic, Environmental, and Political) factors.

This is viewed to be broader and more inclusive than CI in that it includes these “noncompetitive” or indirectly competitive facets of the environment. This use of the term BI reached its height of popularity, as measured in a number of publications and studies, during the 1970s when it was viewed as an effective new way for organizations to respond over the longer-term to what were then viewed to be fundamental or structural changes to the macro-environment of business (Fleisher and Bensoussan 2003, 85-87). The term “business” intelligence also provided a sharp contrast with “government” intelligence activities, and as such helped to differentiate it from the intelligence activities performed by government agencies such as the U.S. Central Intelligence Agency (CIA), Britain’s MI-5, or Israel’s Mossad.

The narrower version of BI became very popular in the late 1990s and remains so at the present time. In its narrower sense, BI is commonly a technologically driven process of discovering hidden data among and within an organization’s own variety of databases (Halliman 2001, 133-40). This narrower form of BI has primarily an internal focus, is focused on shorter-term horizons than its broader cousin, and includes various data-mining technologies to help the organization better understand itself and its own abilities (Kudyba and Hoptroff 2001, 56-61). It is also an electronic-commerce-oriented and IT-intensive form of information-resource management, possibly done at the expense of human intelligence or with the provision of non-technical solutions. BI is particularly used to assist operating managers, business-unit managers, marketing managers, and product managers in their decision-making. It is not generally provided to the organization’s top decision-maker. One negative aspect of referring to the field as “BI” is that the term has been associated recently with the one company that has appropriated its trade-mark, namely, IBM.

Relevant and Associative Terms of Business Intelligence

Competitor Intelligence (CI)

Although often employed as a synonym for business intelligence, it is widely regarded to be more restrictive or limited in scope (Prior 1998, 66-68). This restricted scope appears in terms of both its range of focus and its shorter time horizon. Competitor intelligence uses public information about specific competitor organizations and analyzes it in order to identify a competitor’s potential actions or to identify potential new competitors themselves. It primarily serves the needs of strategic planners, strategic business unit (SBU) operating managers, business-development specialists, mergers-and-acquisitions officers, and/or marketing managers concerned with product or brand management. The time horizon of competitor intelligence can vary from the very near term when it is used to provide marketing managers with operational intelligence about pricing or distribution, to the long-term as support for strategic planners. (Vibert, 2000, pp. 77-81) It has been in use for several decades and is generally viewed to be an institutionalized task of most long-standing competitive organizations.

A majority of companies that perform competitive intelligence will include competitor intelligence as one of the larger set of activities that occurs under the CI umbrella. Sharp (2000, pp. 37-40) provides examples of companies such as Apple, IBM, and Xerox that have encountered competitive problems due to an over-reliance on competitor intelligence, only to be blind-sided by changes in the marketplace. The main drawback of competitor intelligence is the tendency to overrate the activities and intentions of obvious competitors while overlooking indirect competitors, upstarts, or those that may provide substitute products/services.

Knowledge Management (KM)

Knowledge management (KM) is a term that became popular in the late 1990s. Broadbent (1998, pp. 23-36) suggests that KM is about enhancing the use of organizational knowledge through sound practices of information management and organizational learning. In practice, KM is a loosely used term applied to a broad range of organizational approaches that encompasses identifying and mapping intellectual assets within the organization, generating new knowledge for competitive advantage within the organization, making vast amounts of corporate information accessible, sharing of best practices, and using technology that enables all of the above—including groupware and intranets (Denning 1998, pp. 89-97).

Different activities that could potentially fall under the KM umbrella include such things as knowledge-mapping, data- or knowledge-mining, knowledge audits, knowledge databases, corporate intranets or digital library development and maintenance, personal and virtual navigation, corporate knowledge directories, FAQ (frequently asked questions) development, and so on. Some authors might be interpreted to have argued that CI falls under KM as well (Peters 1997, pp. 14-16). KM mainly serves the needs of functional and operating unit managers and is only very occasionally used in strategic decision-making by senior executives.

Problems that CI practitioners have with KM include the fact that it has been heavily driven by systems and technology assets and under-plays the impact that intuition, creativity, and human analysis can achieve, especially in dealing with the kinds of non-repetitive or first-time matters like surprise or serendipity that CI analysts often encounter and need to address. 8 KM is generally mostly inwardly focused, gathering together the tacit and explicit data already within the organization’s walls, while CI utilizes both internal and externally generated data to serve its purposes. CI practitioners also think that subsuming CI under KM, as some KM supporters have argued should be the case, would only add one more layer between themselves and decision-makers, thereby further decreasing CI’s potential effectiveness. As CI expert Ben Gilad (2001) recently stated:

KM is dying a quick death, and not a minute too soon. As a consultant-driven discipline, KM has always been ambiguous, re-inventing the wheel in many cases and complicating existing wheels in others. It has now become an empty title, reduced to a few data warehousing, intranet-connectivity initiatives. Placing CI there is nonsense, of course. (Gilad, 2001, p. 23)

Market Intelligence (MI)

Market intelligence (MI) is industry-targeted intelligence that is developed on real-time (i.e., dynamic) aspects of competitive events taking place among the 4Ps of the marketing mix (i.e., pricing, place, promotion, and product) in the product or service marketplace in order to better understand the attractiveness of the market. Prior (1998, pp. 66-68) suggests that like marketing research, market intelligence looks at the attitudes, opinion, behavior, and needs of individuals and organizations within their broader context. Aaker (1998) notes that MI will include dimensions such as actual and potential market size, market growth, profitability, cost structure, distribution systems, market trends and developments, and key success factors. Advances in electronic commerce now allow it to encompass things like real-time sales data and demand assessment, real-time customer-purchasing-pattern assessment, and ongoing monitoring and assessment of competitors’ pricing. (Aaker, 1998, pp. 115-16)

Market intelligence has been popular for decades and is now mostly institutionalized among competitive marketplace players in the business-to-consumer (B2C) and, increasingly in the business-to-business (B2B) marketplaces. A time-based competitive tactic, MI insights are used by marketing and sales managers to hone their marketing efforts so as to more quickly respond to consumers in a fast-moving, vertical (i.e., industry) marketplace. It is not distributed as widely as some forms of CI, which are distributed to other (non-marketing) decision-makers as well. Market intelligence also has a shorter-term time horizon than many other intelligence areas and is usually measured in days, weeks, or, in some slower-moving industries, a handful of months. (Nolan, 2004, pp. 121-28)

STRATEGIC INTELLIGENCE (SI)

Strategic intelligence (SI) is a radar-like intelligence process that is primarily focused on the long-term, overall environment-shaping industry and marketplace competition for an organization now and in the future (McGonagle and Vella 1999, 208-11). It is determined by and supports top executives in strategic planning and decision-making about capital investment, mergers and acquisitions, long-term R&D planning, market expansion and country risk, and strategic alliances and partnerships, among other things. This is defined in contrast to tactical or operational intelligence, which may be focused on day-to-day marketing or operational concerns that are not necessarily long-term but are still likely to be competitive in nature. Although it has been used irregularly in business organizations for years, mostly since the 1970s, strategic intelligence has remained popular in government intelligence circles. (Prescott, 1998, pp. 4-12)

Role of (BI) in managerial and internal decision making process

With this prevalent internal focus, it is unfortunate that so few executives are delivered the right intelligence to enhance their decision-making and to assist them with managing industry and market risk, the primary bases of business intelligence focus (Hammonds 2001, pp. 150-56). No matter how many customer relationship management (CRM), knowledge management (KM), or business intelligence (BI) systems an organization implements and pays for, they are not going to dramatically improve its competitiveness. Companies need to focus on the external aspects of their environment if they are to succeed today and in the future. Customer learning is clearly important, but equally important is competitor learning that comes through competitor analysis (Fahey 1998, pp. 110-13). Good strategies come from making effective choices (Porter 1996, pp. 61-78) about what both the external environment and the internal organization can tell the decision-maker.

Numerous strategy scholars have noted that organizations have to constantly reinvent and reposition themselves in order to stay ahead of the competition, and in many instances they have to do this just to stay in the game. Of the 500 companies making up the S&P 500 in 1957, only 74 remained on the list in 1997. Of the original Dow Dozen in 1896, only one remains—General Electric. All the others have fallen aside, been absorbed, or been unable to compete. (Werther, 2001, pp. 41-47)

Business intelligence (BI) is defined as a systematic process for gathering and analyzing information to derive insights about the competitive environment and business trends in order to further the organization’s business goals. It is about managing the opportunities and risks in the competitive battle and delivering to decision-makers the capacity to act. The opportunities and risks today are many, and include the increasing pace of business, information overload, increasing global competition from new competitors, more aggressive existing competition, massive political effects, and rapid technological change, among other things.

Every important business decision entails opportunity or risk. So how are strategies formulated and how do firms ensure that the chosen strategy is the right one? The answer: It is only through the careful collection, examination, and evaluation of the facts that appropriate strategic alternatives can be weighed in light of organizational resources and requirements.

Every good manager recognizes the need for systematic analysis of his or her competitors and the external environment. Analysis has been described as an obvious weak link in many public and private intelligence programs (Werther 2000, pp. 19-22). Compounding the matter, so few managers actually receive analyzed information for their decision-making or even have a strategy (Hammonds 2001, pp. 150-56).

Called by one expert (Herring 1998, pp. 13-16) the “brain” of a modern BI system, analysis is one of the more difficult roles that a BI specialist is called upon to perform and that a manager is called upon to oversee. The brain requires a good flow of oxygenated blood, or, in the case of BI, accurate and reliable data flow. The brain is a muscle, and like all muscles, it requires constant exercise to be fully effective. This exercise comes in the form of deep and regular thinking, which results in and causes enhanced learning. What does this all mean for the job of an analyst?

The job of an intelligence analyst is to protect and enhance his or her company’s competitive market interests by providing useful and high-quality analysis to decision-makers, policy-makers, and resource allocators (otherwise known as their “clients”). Analysis is given to these clients in the form of analysis process outputs such as assessments, briefs, bulletins, charts, conclusions, estimates, forecasts, issue reports, maps, premonitory reports, profiles, recommendations, and/or warnings. These analytical products are the most tangible manifestations of the outcomes of the analytical process.

So what is analysis? Analysis involves a variety of scientific and non-scientific techniques to create insights or inferences from data or information. For the purposes of this chapter, the working definition given previously suggests that analysis is the multifaceted combination of processes by which collected information are systematically interpreted to create intelligence findings and recommendations for actions. Analysis answers that critical “so what?” question about the information gathered, and brings insight to bear directly on the decision-maker’s needs, helping the client to make enlightened decisions. It is therefore both a process and a product (Fleisher 2001, pp. 176-80).

What purposes does analysis serve? In his influential 1980 book Competitive Strategy, Michael Porter asserted the need for sophisticated competitor analysis in organizations, and subsequently the need for an organized and systematized mechanism—some sort of competitor intelligence system—to make the process efficient (Porter 1980, pp. 147-52). Most managers in today’s competitive environments implicitly or explicitly recognize the need for more systematic analysis of their competitors, competition, and competitive landscape. However, recognizing that there is a need for the capability and putting into place the systems, structures, and skills needed to exploit that capability are very different things.

Numerous researchers through the years have identified enduring gaps between what is viewed as being needed for decision-making in organizations i.e., expectations) and what is actually being delivered by organizational competitor-analysis systems (i.e., performance). Langley (1995, pp. 63-76) notes that the analysis process serves intermediate decision-making purposes such as reducing the number of input variables, providing more time for decision-making as opposed to facts absorption, providing connections among seemingly unrelated data and information, providing a context by relating information to organizational mission, objectives, and strategy and creating a “working hypothesis” by making a story out of disparate business-environment information. Analysis usually takes place at multiple levels within an organization.

Strategic analysis is arguably the most vital form of intelligence, because it provides a framework within which other forms of intelligence collection and analysis take place, offers an overall assessment from the top down rather than from the bottom up, and helps to provide a basis for policy formulation, resource allocation, and strategy development. Tactical analysis is a necessary and important complement to work done at the strategic level. It is the natural linking element between macro-level analysis and the micro-level focus on individual cases. Operational intelligence analysis overlaps with investigation and is often single-case oriented. It involves technological assessments of the methods used for marketplace battles, specific investigations of competitive threats, and the like.

An important component of operational analysis is identifying the particular vulnerability or vulnerabilities that have been exploited and providing guidance on how it or they can be minimized or eliminated. Each of these analytical levels requires a direction or focus, a methodology, and some experience. To simply try to answer “tell me what you know” leaves one at a loss as to how to satisfy a manager’s requirements. Similarly “tell me everything about x” does little to support good analysis or an executive’s decision-making process. Poor analysis will in turn provide little room for quality decision-making.

The skill-set that the BI team leader possesses is vital to team success. Managers need to be able to understand the degree of management their particular team requires. Due to the creative and sometimes ambiguous nature of BI, team leaders must know when to step in and give direction or when to let the BI team members find their own resolution. Micromanagement from the team leader will likely undermine team effectiveness and inhibit team performance as members become dependent upon guidance or possibly resentful of interference (Simon 2000, pp. 8-10).

Too little direction from the team leader may lead to confusion with regards to the composition of team deliverables. The team does, however, need a certain level of direction, as a lack of team and project structure can decrease BI effectiveness (Simon 2000, 8-10). Effective planning can enhance team performance, as it allows the team to gain greater understanding of the thought processes and concerns of their colleagues. Proper planning can also ensure project alignment with the clients’ demands, and thus create a strategy that will fulfill the project’s overall purpose (Simon 2000, pp. 8-10).

It is important that BI managers also possess heightened communication and organizational skills, as it is their role to coordinate the BI function with that of the organization. More importantly the BI manager must build effective networks with the organization’s decision-makers in order to clearly and effectively relay project requirements to the team as well as communicate output from the team.

All team members need to possess certain skills to be effective BI professionals. They must be creative and resourceful, have a passion for delving deeply into issues, and be able to deliver finished intelligence products in a form that executives can utilize (Simon 2000, pp. 8-10). The skill-set of the BI team should invariably incorporate a broad array of expertise. By including members with only one type of educational or work background, the BI manager may inadvertently create a team that lacks vital skills. For instance, a team in which members consist only of scientists “may produce a report that lacks a business or social perspective, ” whereas a disproportionately “tactical team may lack strategy evaluation or strategic insights” (Simon 2000, pp. 8-10).

Strategic intelligence is information that helps the firms to direct and scope an organization over the long-run to meet the needs of markets and to maximize the shareholders’ wealth. In others words, strategic intelligence is intelligence that help the firms to create competitive strategy. Business intelligence is the application created to help the firms to use the advantage from the information transformed into intelligence. The management could create competitive strategy based on the intelligence that they receive from the application. We could say that Strategic Intelligence is the business intelligence in competitive strategy.

To be leader, firms should penetrate their market globally. Doing business in different markets mean that firms have to stay in the different environment. As a result, they have to handle with the huge amount of information. The bigger we are the more complicate we face. Luckily, the advance in business intelligence technologies enables the global companies to run business more efficiency. Information Technology is the main factor that helps firms to integrate the business function all over the world to unify the organization. Wherever the branches are, the information could transfer to the main server at the head quarter.

Today, external environment change rapidly. To create any strategy, customers and suppliers are the elements that firms should rely on. Customers are our market, we can not ignore their needs otherwise we will be out of business. Also, suppliers are the person that send raw material to us. To be partnership is the way to streamline the production process. The innovative management such as Enterprise Resource Management: ERP, Customer Relation Management: CRM and Supply Chain Management: SCM are the trendy knowledge management for businesses. These knowledge managements integrate the external environment (customers’ data and suppliers’ data) to strengthen the internal organization.

The analytical capability of business intelligence allows a company to move towards being a truly customer-oriented organization. CRM solutions employ a business strategy that merges business intelligence technologies with innovative marketing practices to retain customer relationship. The data is collected from the buying or using habits, age, sex, education of customers. Customers’ information helps business to determine lifetime value of customers, prioritize prospects and develop new products.

List of Some Business Intelligence and Solution Provider Companies

• ITtoolbox Business Intelligence. Web. – A business intelligence community for IT professionals. Focusing on data mining, reporting, queries, and other business intelligence disciplines.
• CRM Today – Business Intelligence – Provides news, events, articles, research reports, white papers and reviews.
• Business Intelligence.com – Communications platform for business & technical users, consultants, software vendors and analysts. Offers articles, research, white papers, industry news, jobs and training information.
• The Business Intelligence and Data Warehousing Glossary – Technology tour displays terms in logical order. Features alphabetical listing of terms and recommended reading.
• Strategic Assets – Provides advice on using an organization’s intellectual property to gain a competitive advantage. Features news, reports and links to related sites.
• Business Intelligence Toolbox – A business weblog with articles by professionals about business intelligence and market research.
• Meeting Industry-Specific Challenges With Business Intelligence Solutions – White paper discusses the changes affecting the financial services, telecommunications and retail industries and how BI solutions address industry-specific needs. By Erik Johnson and Megan Lordeon.
• Montague Institute – Membership organization for information professionals engaged in all aspects of managing intellectual assets in a business context. Features events, articles and membership information
• Business Intelligence Value Chain – Business Intelligence – How Agencies Can Breathe New Life Into Old Data

Conclusion

Information technology has forced many firms to take a serious look at their BI systems. Since large quantities of information are now available to BI professionals, organizations need to take a very close look at the strengths and weaknesses of both employees within the firm who are responsible for the BI function and the IT infrastructure that exists within the firm. A careful analysis of these two elements is essential in order to establish a good balance of TI and PI to ensure that the firm’s data, information, and intelligence needs are met.

Creating a healthy balance of PI and TI will increase the effectiveness of capturing the behavior of competitors, regulators, technologies, and other external influences. Additionally, a good balance between PI and TI will systematically integrate a wealth of internal and external information that was previously scattered among a variety of media.

How a company analyzes the data and disseminates the required information to the necessary people within the organization are essential. Front-line workers, such as sales and marketing employees, require current competitive information to ensure future success. The impact that the intelligence information has on the company for both short- and long-term decision-making is critical to the success of an organization’s current and future strategy. A company with a healthy BI system will have achieved an optimal balance of PI and TI, enabling it to thrive on industry change.

As is the case in many relatively youthful fields that have not yet reached maturity, the term business intelligence (BI) has both positive and negative connotations for its various stakeholders’ dimensions. This chapter has sought to systematically identify and assess these arguments. It does not suggest whether CI should or should not be replaced, but hopefully it delivers a balanced and fair view of arguments being offered on both sides.

Internet allows people to search for the information that they want to know. There are tons of choices provided for customers via the internet. They could search for the information that they want to know easily before making a choice. Also, the internet enhances companies to serve their customers 24 hours a day. The gap of profit is shortened time to time. Any businesses that provide the huge amount of profit will lead other parties to join in this market and then the profit would be less or no more profit at all. Only the stronger could be alive in the market.

We are living in the world of chaos and uncertainty. To deal with this world, we have to adapt ourselves to fit into the new environment. Be differentiate, be innovative, be creative and be customize, be quick are the powerful phrases for doing business in this edge. Businesses have to be creative, be innovative to differentiate themselves from the others. Otherwise, it’s no place for them in the market. To maintain one’s place in the market, business intelligence must be the top priority and core issue.

References

Aaker, D.A. (1998). Strategic Market Management, 5th ed. New York: Wiley. 115-16

Broadbent, M. (1998). “The Phenomenon of Knowledge Management: What Does It Mean for the Information Profession?” Information Outlook2 (5): 23-36.

Denning, S. (1998, October 11). “What Is Knowledge Management?” Background document to the World Development Report. 89-97

Drucker, P., as quoted in T. Davenport. (1997). “A Meeting of the Minds,” CIO Magazine10 (21): 46-54.

Fahey, L. (1998). Outwitting, Outmaneuvering, and Outperforming Competitors. New York: Wiley. 110-13

Fleisher, C.S. (2001). “Analysis in Competitive Intelligence: Process, Progress, and Pitfalls,” in Managing Frontiers in Competitive Intelligence, ed. C.S. Fleisher and D.L Blenkhorn. Westport, CT: Quorum Books. 176-80

Fleisher, C.S., and B. Bensoussan (2003) Strategic and Competitive Analysis: Methods and Techniques for Analyzing Business Competition. Upper Saddle River, NJ: Prentice-Hall. 85-87

Gilad, B. (2001). “Industry Risk Management: CI’s Next Step,” Competitive Intelligence Magazine 4(3): 21-27.

Halliman, C. (2001). Business Intelligence Using Smart Techniques: Environmental Scanning Using Text Mining and Competitor Analysis Using Scenarios and Manual Simulation. Houston: Information Uncover. 133-40

Hammonds, K.H. (2001). “Michael Porter’s Big Ideas, ” Fast Company 44:150-56.

Herring, J.P. (1998).“What Is Intelligence Analysis?” Competitive Intelligence Magazine1(2): 13-16.

Kudyba, S., and R. Hoptroff. (2001). Data Mining and Business Intelligence: A Guide to Productivity. Hershey, PA: Idea Group Publishing. 56-61

Langley, A. (1995). “Between Paralysis by Analysis and Extinction by Instinct, ” Sloan Management Review36(3): 63-76.

McGonagle, J.J., and C.M. Vella.. (1999). The Internet Age of Competitive Intelligence. Westport, CT: Quorum Books. 208-11

Nolan, J. (2004). Confidential: Uncover Your Competitors’ Top Business Secrets Legally and Quickly—and Protect Your Own. New York: Harper Business. 121-28

O′Dell, C., and C.J. Grayson, Jr. (1998). “If We Only Knew What We Know: Identification and Transfer of Internal Best Practices, ” California Management Review40(3): 154-74.

Peters, R.F. (1997). “Information Partnerships: Marketing Opportunities for Information Professionals,”Information Outlook1 (3): 14-16.

Porter, M.E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: Free Press. 147-52

Porter, M.E. (1996). “What Is Strategy?” Harvard Business Review74 (6): 61-78.

Prescott, J.E., J. Herring, and P. Panfely. (1998). “Leveraging Information for Action: A Look into the Competitive and Business Intelligence Consortium Benchmarking Study, ” Competitive Intelligence Review 9(1): 4-12.

Prior, V. (1998). “The Language of Competitive Intelligence: Part One, ” Competitive Intelligence Review9(2): 66-68.

Sharp, S. (2000). “Truth or Consequences: 10 Myths that Cripple Competitive Intelligence, ” Competitive Intelligence Magazine3(1): 37-40.

Simon, H.A. (2000). Comment: “AI’s greatest trends and controversies.” IEEE Intelligent Systems & Their Applications, 15(1), 8-10.

Vibert, C. (2000). Web-Based Analysis for Competitive Intelligence. Westport, CT: Quorum Books. 77-81

Werther, G.F. A. (2000). “Profiling ‘Change Processes’ as a Strategic Analysis Tool, ” Competitive Intelligence Magazine 3(1): 19-22.

Werther, G.F. A. (2001). “Building an Analysis Age’ for Competitive Intelligence in the Twenty-first Century, ” Competitive Intelligence Review 12(1): 41-47.

Introduction

WidgetSupplies’ business is a distributor and supplier of medical equipment which in the recent past experienced rapid expansion. This ISO 9001:2000 certified distributor has got many departments managing various specific products in some locations of sales offices in different regions. The company has in the past ensured value addition in their products through proper packages and customer attractive discounts, besides offering after-sales services to ensure an increased customer base.

The need for the company to participate in social responsibility

Even though the company has not involved itself in social responsibility it is high time it did. Some of the social responsibility activities the company can participate in include; cleaning health centers, creating a foundation to support those who cannot meet their hospital bills, supporting campaigns to fight a specific ailment, organizing sporting activities for the hospital staff and the entire public among other activities.

As the company experienced expansion its requirements also evolved. The company’s growth requirements super ceded the existing off-the-shelf retail management software and basic Excel spreadsheets which were applied. And therefore the management was confused on whether to acquire new systems or improve existing ones. Before experiencing expansion the existing simple template was ideal and satisfied the company operations. Any complexity could easily be handled through spreadsheet tinkering and duplicate data entry, though this brought a lot of time and other resources wastage and therefore diverted the management’s attention from the existing core business activities to the system maintenance.

Explanation on some of the WidgetSupplies’ business intelligence requirements

One of the Widgetsupplies’ requirements was reports which were necessary for business planning and daily operations. As they experienced expansion the existing system could not easily access data in the time leading to a serious negative effect on sales and budgeting. The existing reports for instance could not readily calculate the profit margins. WidgetSupplies’ business needed a simple system to easily record sales and to compare the projected sales and the actual which the system could not provide. It forced the management to handle all these manually in spreadsheets which became too complex and difficult to update. Thirdly, the existing spreadsheet formulas were suspected due to the errors which existed in spreadsheet macros. The spreadsheet calculated the commission in terms of the sales value instead of the profit margin. Finally, the existing reports were complex and therefore difficult to create and maintain. As the company experienced expansion, the business requirements intelligence increased and could not be easily addressed by the existing software.

The detailed explanation of the solutions for the above problems

There was there a need to come up with the relevant solutions to address the above problems. A provision of an efficient data mining solution that is cost-effective can address some of these problems. The adoption of a simple database reporting with automated calculations for margins, markups, and commissions can help the company deal with the complaint of complexity and constant errors in calculations. The company can also develop spreadsheets in terms of region, product, customers department, and salesperson to make data retrieval easier. Finally Widgetsupplies’ can develop a system that makes it easier for the tracing of the original records with a validated data entry form to feed in budget and sales projection.

A brief conclusion showing the need for improved operation systems within companies

In a nutshell, it is important to acknowledge that as the company grows so do its internal and external requirements. The management must constantly adapt those specific solutions to address the fundamental issues that negatively affect company performance.