Business Intelligence: Different Business Situations Essay


Describe about the Business Intelligence for Different Business Situations.


Accounting firm case

Business Intelligence (BI)

Business climates are constantly changing to which organizations have to respond with appropriate decisions. Business intelligence is the conceptual foundation for making such business decisions and thus, they can be used for getting data, visualizing it, performing models, dashboards, and taking critical decisions. To survive in a challenging business environment, companies have to keep responding to competitive pressures and measure if the response is in the right direction towards achieving right business objectives(Cokins, 2006).

At any point of time, this requires a manager to navigate through hordes of responsibilities and information to come up with appropriate decisions in different business situations. This would demand designing of business situational models such that decisions can be made tactically and strategically in an organized way. These decisions can be structured, semi-structured, or unstructured. Use of computers for making such decisions can speed up the process and improve the accuracy of efficiency of decision-making. Business Intelligence tools like Decision support systems are thus being used by many organization that act as the foundations to rely on for taking critical business decisions (Holodnik-Janczura & Golinska, 2010).

Business Intelligence is actually an umbrella term for a various set of IT entities including databases, tools, architecture, applications, and methodologies. The key approach used in any of the BI system is consolidation of data on company processes, extraction of information from this data and presentation of the same in such a way that it can be used as a foundation for taking strategic and tactical business decisions. There are a wide variety of BI tools that are available for use such as Enterprise resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM) (IBM Cognos, 2009). For presentation of the business information, certain front-end components and tools are used in BI software systems such as databases, data transformation tools, analytics, visualization, and connectivity tools. BI is used in many areas, disciplines and departments of an enterprise including marketing, sales, customer relationship management, procurement, logistics, manufacturing, financial control, search engineer optimization, human resource management, information and communication management and so on (Horakova & Skalska, 2013).

Competitive Advantage with BI

Business Intelligence implementations in organizations bring some major benefits that include:

  • Saving in data management costs due to consolidation
  • Time saved on data search and deliveries
  • Better quality of information
  • Support for decision making
  • Business process reengineering (Kumar, et al., 2013)
  • Support for strategic goals of a business
  • reduction of the dispersion in the business related information
  • Improved interaction between users
  • real time information availability
  • flexibility built into system in adopting to dynamics of business Improvement in employee productivity(Nandi, 2012)

With a set of these benefits in place, a company can build a competitive advantage for its business. Some ways this can be done by deploying BI tools are:

Visualization: Visualization can be used for identifying patterns in data and companies can detect even that information, which can otherwise go ignored, in a manual data scanning. This could reveal if the data or information is aligned with the strategic goals and objectives of the organization such that deviations can be observed and corrective measures can be taken (Guarda, et al., 2013).

Figure 1: Tableau Software Screenshot(Ajayi, 2013)

In an accounting organization, visualization can be used for analysing and explaining complex financial problems with information dashboards and data models. Financial reports like Balance sheet and P&L accounts can also be created fast through data abstraction making it easier for accountants to compare yearly reports and analyze exact numbers in financial statements for better understanding of company performance. Interactive charts and dashboards can be drilled down to minute details to discover patterns and issues in the accounting data. For instance, Tableau is a BI tool that allows one to create pie charts and histograms that are interactive allowing users to go deeper into the data (Ajayi, 2013)

Consolidation of historical and new data:

Any organization can utilize the repository of its historical data to identify patterns that could be advantageous for understanding business processes and improving them. This can help organizations take critical business decisions such as arriving at the most influential pricing model, identifying most effective marketing strategy and so on. In an accounting firm, the historical data on financial performance can be used for comparing performance of the company over the years to understand if the company is able to achieve the strategic goals of the organization over the years (Walker, 2006).

Integration of information:

Any organization that is a part of a supply chain may get affected by or need information from other members of the channel. With BI, all these supply chain partners can be brought together with their data consolidated enabling a smooth flow of information between them such that business processes can be formalized and real time information about the business can be obtained (Munteam & Mircea, 2008).

System integration:

With integration of different tools used for various processes such as monitoring, reporting, data management, and other features, standards and procedures can be formally defined such that the organization can ensure that the procedures adopted comply with objectives and there is a proper visibility into the system (Horakova & Skalska, 2013).

Defining Stakeholder requirements:

BI tools can be use to record and define stakeholder requirements for any project such that any deliveries made by any organization using BI tool can ensure that all stakeholder requirements are met thereby improving the stakeholder satisfaction. Various stakeholders can be team members, internal customers, external customers, line managers, senior managers, other departments, clients, industry experts, opinion leaders, communities, government agencies, shareholders, trade unions, and suppliers. Recording of stakeholder requirements in an accounting firm can help a firm understand if there are any conflicts in the requirements such that the same can be resolved. As stakeholders have a strong influence on the success of any organization, this information would be helpful in understanding their needs such that conflicts between different stakeholders could be avoided and communication can be improved so as the improve the performance of internal stakeholders and perception of external stakeholders about the organization (ContentExtra, 2012).

Data Mining and Analytics in retail loyalty card schemes

Analytics can be used for identifying meaning patterns from a data set.

In a retail industry, the customer data is used for performing analytics such that the results obtained can be used for designing customer loyalty schemes. Analytics can be used for predictive modelling in retail by studying pas data about consumer behaviour to predict specific consumer behaviour as a reaction to a product or in response to a marketing message or campaign. Descriptive models may also be used for identifying consumer relationships and creating a classification for them. With the use of analytic tools, a retail organization can do the following:

Identify consumer segments and do a detailed profiling of each segment to identify target customers who can be most profitable for the organization. A special loyalty benefit may be provided to these consumers in response to their purchases.

Identify consumer needs and predict their behaviour in response to various loyalty schemes such that loyalty scheme that gives most profitable customers can be utilized to build loyalty in customers.

Targeted promotions and advertisements can be formulated and delivered based on the analytics to get more loyal customers

Retail organizations use the methods for overcoming specific challenges of loyalty schemes by using in-store and online analytics. In the in-store analytics, consumers are monitored physically in the store and the data is collected on their movements through video capture, about their gender, store visits and so on. This is often used for identifying products that are hot for customers and their movement patterns can be used for designing a better store experience. Online analytics is possible through the collection of data from social media, mobile devices, and web searches that could be useful in understanding online movement of consumers, sentiments of consumers about the brand, and their online shopping patterns. This can help a company understand the satisfaction levels of customers. It also helps a company design personalized services to consumers based on their preferences, demographics and other personal information (Gupta & Kumar, 2014).

Data Analytics is now being used as a core of loyalty programs by many organizations for segmenting consumers by using consumer data such as demographics, transactional data, and credit reports and so on. The analytics data is obtained by these organizations from multiple sources including websites, mobile devices, and social media and analytics methods that are used for gaining insights from this data include market basket analysis, customer experience analysis, social media analytics and so on (Friesen, et al., 2014).

Figure 2: Customer Loyalty Trends

There exist some key trends that are being observed in retail organizations that use insights from consumer analytics to design and customize their loyalty programs such as providing seamless channel experience, use of customer rating mechanism, automated product placements and so on.

Queensland Health systems implementation Case

Implementation Failure

Queensland government consisted of three types of organizations that were connected including government departments and agencies, their owned corporations, and general statutory bodies. Queensland health was one organization that was run by the government for providing medical, dental and age care services across geography for Australian population. The employees of the organization were paid through a payroll system called LATTICE which as rolled out in the organization between 1999 and 2002. However, by the year 2005, the system became obsolete and a decision was taken to replace the legacy system by a standardized software solution (SSI) including SAP HR and SAP Finance.

Figure 3: Queensland Health Schedule Delays

This SSI was expected to deliver a series of benefits including system consolidation, increased cost visibility, reduced duplication of data, licence cost reduction, reduction in personnel requirements, economies of scale, increase in service standards through focus on core, and information consistency across governmental organizations. However, due to inherent system and solution complexities, the implementation project completely failed after exceeding both budget and planned schedule with huge variations. While the go Live was to happen in the year 2008, the dates were missed over the coming years and the project could only be completed by 2010. While the project was 18 months behind schedule, the cost variance was even huge going 300% over budget.

Several reasons that had caused these cost and time overruns included:

NO specific project management methodology was followed for implementation

The roles and responsibilities of key stakeholders were not properly identified and defined

Business requirements were not defined properly leading to challenges in testing

The approach and structure used for the implementation was same as used for department of Public Housing project, which was not as complex as Queensland health and thus, did not suit the needs of new project.

Any organization that plans to go for a complex software implementation process should follow certain best practices of project management such as clear articulation of requirements, use of an appropriate strategy for implementation, appropriate levels of system testing before go live, use of appropriate project management methodology and communication of project requirements and progress to stakeholders. None of these processes was followed in the case of Queensland health payroll system implementation (Mansharamani, 2011).

Major challenges that were faced by the implementation project included:

Industry Complexity:

Healthcare industry catered to people, processes, and services that were managed by hospitals, pharmacies, and different diagnostic agencies. The industry structure was very different from other industry in terms of focus and size. While other industries catered to a few thousands of clients, this industry had to deal with millions. The diversity was huge, every case of individual patient was different, and thus, the requirement for having specialists was huge in numbers. There was a huge array of different roles, responsibilities, and salary brackets. There were some 24,000 combinations of wages for different types of healthcare professionals who managed cases of around 40,000 patients per day across 300 sites in Queensland.

Requirement Definition:

requirements of the project were not articulated clearly in the case of Queensland health payroll system implementation. There was no clear documentation of this requirement and thus, when the stage of testing came, issues arose in user acceptance and functionalities testing. The issues identified demanded additional components that were to be added to the project scope resulting in the increase of the project cost.

Implementation strategy:

The project was actually a part of solution that was to be implemented for the whole government and thus, it could have been done one by one in smaller and less complex government agencies but due to the LATTICE system which was obsolete, the payroll systems would not work if the implementation was not done for whole system and thus, the implementation was decided to be carried out for the entire government. For this complete implementation, the case of Department of Housing was taken as a base for developing implementation strategy. However, the complexities involved in the housing department was much less as it was a much smaller entity and thus, the approach was unlikely to appropriately manage the complexities of healthcare system which was much larger and complex (Devedzic, 2000).

System testing and data quality:

For the testing of the project, 10% of employees were involved before Go Live, which revealed discrepancies of $1.2 million AUD in fortnightly payroll, and these were majorly attributed to casual staff and overnight claims. Another test followed excluding these types of claims resulting into $30,000 AUD discrepancy. Because of the defects found in four categories, the system was stopped from going live. During subsequent testing phases, more issues were identified and the same were reclassified from time to time. Because of no rigorous testing performed at one point of time but only considering small number of testing samples, no tests revealed complete picture and thus, many critical issues were not resolve till the end resulting into incorrect payment and non-payment to a huge number of people from staff in the end.

Figure 4:quasi-multiple client-contractor relationships

Client consultant relationship complexities: Usually, in any project, one prime contractor is involved who may hire additional sub-contractors to manage certain activities. However, in the case of Queensland healthcare project, two contractors were involved including CorpTech and IBM resulting into quasi-multiple client-contractor relationships. This resulted into lack of clarity about roles and responsibilities of different parties.

Project management and governance complexities: Queensland project did not use any formalized project management methodology for managing the project. This resulted into issues of communication as no proper governance technique was formalized for defining individual roles and responsibilities.

Communication Issues: IBM did not put their best people on the project and those put on the project were resisted by the government employees who were not willing to cooperate and transfer required functional knowledge to the IBM team (Eden, et al., 2014).

Systems Development Lifecycle approach for Improvement

System Development Lifecycle Approach (SDLC) is used by software companies for designing, developing and testing of software. The aim is to produce high quality software that meets quality expectations of customers and help completing the project as per the planned time schedule and within cost estimates. Thus, this methodology is suggested for cases like Queensland Healthcare payroll system.

SLDC consists of six simple stages including planning, defining, designing, building, testing, and deployment of a software. In the planning stage, senior members of the project team are made to take the inputs from customers, company departments, domain experts, and individuals through surveys. The information obtained at this stage is actually utilized to conduct a feasibility study for the software development involving economical, operational, and technical concerns. If the development is found feasible then in this stage, approaches are considered to take decisions on approaches that can be used for development and implementation of software with minimum risks (Franch & Carvallo, 2003).

After the plan for implementation is clear, the next step is to define requirements, document them appropriately, and get them approved by customer or through a market analysis. This could be done by using a Software Requirement Specification (SRS) document. This document would record all the requirements of the product to be developed for the entire product life cycle. With this definition, the testing could have been done much smoothly in the case of Queensland project.

The next stage involves designing of a product architecture that suits best to the needs of the project. Design approaches can be documented in Design Documentation Specification (DDS) such that they can be reviewed by various stakeholders based on certain parameters like risks, robustness, design modularity, budget constraints, time constraints, and so on such that the best approach for development could be selected. In the current case, no options for different design approaches were considered but a direct base case of Department of Housing was taken to develop the architecture for the software (Chang, 2012).

After the design is chosen, actual development of the project begins as per the DDS. After development, the testing of product is required which could be done to identify bugs. Issues are tracked, resolved and retests are done until all bugs are removed and the product assumes the same level of quality standard as defined in the SRS. Only after the functional testing is completed successfully, the product may go to the users for final testing and acceptance. In case of the Queensland project, there was no appropriate functional testing that was performed but a direct user testing on a very limited number of users was conducted for first and for all retests leading to unresolved issues that were not revealed during user testing (Vennapoos, 2008).

The company may use one from a variety of SDLC models including waterfall, iterative, spiral, and V-model. An appropriate approach may be chosen by an organization based on certain parameters like user requirements, familiarity of users with technology, complications of a system, reliability of system, schedule, cost constraints, project management methodology used stakeholder visibility, team skills, documentation needs, and components reusability and so on. For instance, if the user requirements have to be made very clear on a project then a waterfall or V-shaped approach may not be most appropriate. Further, if the system to be developed is very complex, agile methodologies may not be feasible. With limitations on costs, an iterative SDLC approach would be most appropriate. In the case of Queensland project, the requirements need to be very clear, the system is very complex, and thus, a spiral approach may be most appropriate (Mishra & Dubey, 2013).


Ahmad, N. & Laplante, P. A., 2009. Software Project Management Tools: Making a Practical Decision Using AHP, s.l.: Penn State University .

Ajayi, O. T., 2003. Interactive Data Visualization In Accounting Contexts: Impact On User Attitudes, Information Processing, And Decision Outcomes, Lafayette: University of Louisiana.

Awad, M. A., 2005. A Comparison between Agile and Traditional Software Development Methodologies , s.l.: The University of Western Australia.

Bruegge, B. & Johnstone, J., 1997. Software Project Management Plan: JAMES Project, Pittsburgh, PA: Carnegie Mellon University.

Chang, C., 2012. Selecting an Appropriate Software Development Lifecycle (SDL) Model in an Agency Environment. [Online]
Available at:
[Accessed 12 November 2015].

Cokins, G., 2006. How do Business Intelligence and Performance Management Fit Together?. [Online]
Available at:
[Accessed 1 October 2015].

ContentExtra, 2012. Idnetifying Stakeholders and their relevance, s.l.: ContentExtra.

Devedzic, V., 2000. Software Project Management , Yugoslavia: University of Belgrade.

Eden, Rebekah, Sedera & Darshana, 2014. Building a Better World Through Information Systems. Aukland, New Zealand, 35th International Conference on Information Systems.

Franch, X. & Carvallo, J. P., 2003. Using Quality Models in Software Package Selection, s.l.: Universitat Polit?cnica de Catalunya.

Friesen, S., Pujol, M. & Bastone, J., 2014. Successful Loyalty through Analytics, s.l.: SAS.

Guarda, T. et al., 2013. Business Intelligence as a Competitive Advantage for SMEs. International Journal of Trade, Economics and Finance, 4(4), pp. 187-191.

Gupta, S. & Kumar, N., 2014. Retail Analytics: Game Changer for Customer Loyalty, s.l.: Cognizant.

Ho?‚odnik-Janczura, G. & Goli?„ska, I., 2010. Operations Research And DecisionS, s.l.: Wroc?‚aw University of Technology.

Horakova, M. & Skalska, H., 2013. Business Intelligence and Implementation in a Small Enterprise. Journal Of Systems Integration, Volume 2, pp. 50-61.

IBM Cognos, 2009. Scorecarding with IBM Cognos 8 Business Intelligence, s.l.: IBM.

Kumar, T., G.Sravanthi & Deepthi, D., 2013. Competitive Advantage through Business Intelligence for E-Commerce. International Journal of Computer & Organization Trends , 3(11), pp. 579-585.

Levine, H., 2000. Simplifying Project Management Software Selection: A Balanced Approach , s.l.: Scitor Corporation.

Mansharamani, R., 2011. Performance Testing, s.l.: Free Software Foundation.

Michael J. Saylor, 2007. Applications of Industrial-Strength business Intelligence. s.l.: MicroStrategy Incorporated.

Mishra, A. & Dubey, D., 2013. A Comparative Study of Different Software Development Life Cycle Models in Different Scenarios. International Journal of Advance Research in Computer Science and Management Studies, 1(5), pp. 64-69.

Muntean, M. & Mircea, B., 2008. Business Intelligence Solutions for Gaining Competitive Advantage, s.l.: West University of Timisoara.

Nandi, V. T., 2012. Maintaining Database: Business Intelligence Tool For Competitive Advantage. Business Intelligence Journal, 5(2), pp. 352-357.

Vennapoos, C., 2008. What is the necessity of SDLC?. [Online]
Available at:
[Accessed 30 November 2015].

Walker, R. G., 2006. Consolidated Statements: a History and Analysis, s.l.: Sydney University Press.

How to cite this essay: