× Limited Time Offer ! FLAT 30-40% off - Grab Deal Before It’s Gone. Order Now
Connect With Us
Order Now

ITECH7407 Real Time Analytics Assignment Sample

Learning Outcomes Assessed

The following course learning outcomes are assessed by completing this assessment task:

• S1. Integrate data warehouse and business intelligence techniques when using big data.

• S2. Create flexible analytical models based on real time data, and use connectivity interfaces and tools for reporting purposes.

• S3. Use real time performance analysis techniques to monitor data, and identify shifts or events occurring in data, as a basis for organisational decision making.

• S4. Use real time mobile tracking techniques to utilise mobile-specific usage data.

• K3. Communicate the key drivers for big data in terms of efficiency, productivity, revenue and profitability to global organisations.

• K4. Identify and describe types of big data, and analyse its differences from other types of data.

• A1. Communicate security, compliance, auditing and protection of real time big data systems.

• A2. Adopt problem solving and decision making strategies, to communicate solutions to organisational problems with key stakeholders, based on analysis of big data, in real time settings.

Deliverable 1. Analysis Report (30%)

Task 1- Background information

Write a description of the selected dataset and project, and its importance for your chosen company. Information must be appropriately referenced.

Task 2 – Perform Data Mining on data view Upload the selected dataset on SAP Predictive Analysis. For your dataset, perform the relevant data analysis tasks on data uploaded using data mining techniques such as classification/association/time series/clustering and identify the BI reporting solution (e.g., diagrams, charts, tables, etc.) and/or dashboards you need to develop for the operational manager (or a relevant role) of the chosen organisation.

Task 3 – Research

Justify why you chose the BI reporting solution, dashboards and data mining technique in Task 2 and why those data sets attributes are present and laid out in the fashion you proposed (feel free to include all other relevant justifications).

Note: To ensure that you discuss this task properly, you must include visual samples of the reports you produce (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use ‘Snipping tool’), and also include any assumptions that you may have made about the analysis in your assignment report (i.e. the report to the operational team of the company). A BI dashboard is an integrated and interactive tool to display key performance indicators (KPIs) and other important business metrics and data points on one screen, but not a static diagram or graph. To ensure that you discuss this task properly, you must include visual samples of the reports you have produced (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use ‘Snipping tool’), and also include any assumptions that you may have made about the analysis from Task 3.

Task 4 – Recommendations for CEO

The CEO of the chosen company would like to improve their operations. Based on your BI analysis and the insights gained from your “Dataset” in the lights of analysis performed in previous tasks, make some logical recommendations to the CEO, and justify why/how your proposal could assist in achieving operational/strategic objectives with the help of appropriate references from peer-reviewed sources.

Task 5 – Cover letter

Write a cover letter to the CEO of the chosen firm with the important data insights and recommendations to achieve operational/strategic objectives.

Other Tasks –

At least 5 references in your report must be from peer-reviewed sources. Include any and all sources of information including any person(s) you interviewed for this project. Please refer to the marking scheme at the end of the assignment for other tasks and expectations.

Deliverable 2. Personal Reflection

This deliverable for assignment help is an individual work and can be attached to your data analytics report. In this part, each student will write a one-page-long reflection report covering the following points:

• Personal understanding of course content, and personal insights into the importance and value of the course topics.

• Three most useful things you have learned from the course and explain how they could help your current learning and future professional career.

• Personal feeling of SAP products (or other equivalent tools) used in lab exercises and assignments.

All discussion is expected to be well backed with real examples.

Deliverable 1. Analysis Report

Task1- Background Information

Australian Institute of health and welfare is recognized as a freely statutory institute generating accessible and authoritative statistics as well as information in order to inform and help better service delivery & policy decisions, leading towards effective wellbeing as well as health for entire Australians. Australian Institute of health & welfare/AIHW has high experience of over 30 years with welfare and health data records. This is also known at both national and international levels for their statistical expertise and ensured track the records in facilitating the independent evidence and high quality. To facilitate statistical information for community and governments to utilize in order to promote discussion & inform the relevant decisions based on health, community services, as well as housing is the mission of AIHW. On the other hand, facilitating robust evidence in terms of information and data for better decisions as well as improvements in health & welfare is the vision of AIHW. AIHW supports releasing various health solutions for community so, they need a good expenditure to continue progress in this field (Health Direct, 2022).

In this manner, the dataset selected for this agency is related to the area of expenditure, broad sources of funding, and detailed sources of funding for AIHW including corresponding states and financial years. This selected dataset is varying from the financial year 1997 to 2012 respectively. This dataset is very essential and helpful for AIHW executive dashboard review for health expenditure with a long duration (1997 to 2012). Hence, the dashboard will help the CEO of AIHW to recognize the entire business expenditure, total resources, and broad/detailed contributors so, that the CEO can make the decisions appropriately and plan further expenditures more accurately by analyzing and tracking this expenditure dashboard. According to the selected datasets, the main areas of expenditure are administration, aids & appliances, other medications, benefit-paid pharmaceuticals, capital expenditure, dental services, community health, medical expense tax rebate, health practitioners, medical services, patient transport service, private hospitals, public health, research, and public healthcare.

 The main importance of this project and selected dataset is listed below-

• To reduce the unnecessary expenses.
• To increase the revenue
• To increase market promotions
• To ensure public health and welfare programs (Burgmayr, 2021).

Task2- Data Mining

The selected datasets from the given source and relevant AIHH teams such as CEO, Director, Finance Director, and Operational Director are identified to prepare the high-level dashboard accordingly. So, the selected datasets can be proposed as-

Therefore, the data analysis is done based on such expenditure review for AIHW from 1997 to 2012. To analyze the defined datasets, some major data mining techniques have been used including classification analysis, cluster analysis, and regression analysis which are systematically mentioned below-

• Classification Analysis: This data mining technique is used to extract the most relevant and required datasets for the financial years 1997 to 2012 for reviewing total expenditure of AIHW agency. Using this analysis method, the different datasets have been classified into different classes. The classification is done based on the six corresponding classes such as financial year, state, area of expenditure, broad sources of funding, detailed sources of funding, and real expenditure in millions. This classification helped to analyze the data using creating the appropriate graphs and charts accurately.

• Cluster Analysis: Cluster analysis is a series of data variables; these variables are equivalent within the same cluster. This method is used to discover the clusters in data according to the range of association between two variables. This is used to get help in customizing the dashboards for CEO and Finance manager specifically.

• Regression Analysis: Regression analysis is also used for the expenditure review in order to identify and evaluate the relationships among such classes or variables. This analysis technique helped to understand the feature values of each dependent class change if any of available independent variables vary. In other words, it helped in prediction of total expenditure and revenue growth of AIHW (Souri, & Hosseini, 2018).

 Task3- Research


Before mining the datasets, it is essential to disclose the main assumptions made for analysis of these datasets, and these assumptions are mentioned in the following points-

• Total of 15 expenditure areas is covered throughout the datasets review.
• Financial year is concerned as per the calendar year from 1997 to 2012.
• Broad source funds and detailed source funds are centralized to calculate the total expenses and revenue.
• A total of 8 states are covered to make the data model.

CEO Dashboard

The dashboard of CEO is outlined below with particular analysis as the main purpose of this dashboard is to review the real expenditure in millions by state as well as area of expenditure. The dashboard comprises major financial years of highest expenses, real expenditure in millions, main states, and the major areas of highest expenditure.


Figure 1: CEO Dashboard

The structure of above-mentioned dashboard is classified into four main classes in order to support the business outcomes. Major descriptions of expenses, areas, states, financial year, and real expenditure amount are calculated and plotted using graphs to highlight the revenue and expense ratios.
In case of CEO dashboard, entire information is at broad level including Australian level rather than only one or two particular cities in order to give major snapshots of this large AIHW business.
Supporting Charts


Figure 2: Expenditure Charts

These supportive bar charts are used in the estimation of total expenses and revenue forecasting by state and area of expenditure as it gives a foundation line view of the details concisely. The other views include the total real expenditure using all measures. On the other hand, top 4 real expenditure is investigated by state and these states are NSW, VIC, QLD, and WA. Similarly, the top 4 expenditure areas are identified including public hospitals, private hospitals, benefit-paid pharmaceuticals, and all other medical services. Thus, the result is clearly outlining the main sources of highest expenditure in AIHW in terms of these four states and areas of expenditure.


Figure 3: Broad source-based expenditure charts

The above-illustrated charts are reflecting following observations-

• The real expenditure graph is estimated mostly for the years 1998 and 1999 where 1997 has no value for highest expenditure.

• For detailed source funding, the real estimated expenditure has constant value based on all measures.

• On the other hand, the main broad sources of funding including government and non-government sources have real expenditure value as shown in right-side designed top and down charts.


Figure 4: expenditure in million

This chart is defining the real expenditure in millions to estimate the total expenses and most sensitive states. Thus, the graph is not static as per financial years.
Finance Director Dashboard


Figure 5: Finance Director Dashboard

The layout of finance director dashboard illustrated above is customized based on a similar approach to previous chairman’s dashboard. Review the real expenditure in millions by state as well as area of expenditure is the main purpose of this dashboard. The high-level dimension’s performance is outlined based on four main classes including the most expensive financial years, most expenses raised state, and most expenses raised areas of AIHW to measure the expense history and plan further financial strategies accordingly. The objective of this finance dashboard is to facilitate more stable information in order to track the financial performance on previous record basis. It is reflected in the developed graphical evidence that is showing the financial performances in terms of real expenditure in millions as per the highest expensive financial years. Supporting this graph, corresponding charts are also generated to show the main expenditure areas and states.

Supportive Charts

These chats and bars are systematically listed below with brief analysis-


Figure 6: Expenditure charts

The overall designed or generated charts articulated above are showing the financial director’s dashboard results in which the review of expenditure is done on the basis of state and area of expenditure. Top four real expenditure areas are discovered with top four real expenditure states based on the financial years. The real expenditure in million is also proposed using static graphic view.


Figure 7: Total value in million

These charts are very specific as these help in estimating the historical expenditure values using reviews and tracks of such selected AIHW records including a possibility to calculate the total revenue according to financial years of 1997 to 2012. This has been estimated that the value of expenditure in previous financial years is majorly higher than 200 million as the graph rate is constantly increasing as represented in above chart. So, it has been estimated that AIHW has higher revenue growth based on total expenditure values. The highest expenditure is the highest revenue so, the result is positive for AIHW as they increased their revenue per year. 

Task4- Recommendations for CEO

To enable the AIHW CEO for improving their business operations based on the business intelligence analysis done previously some recommendations are suggested. The perceptions obtained from the selected and analyzed datasets, and some logical solutions in terms of recommendations will be guided to the CEO of AIHW so, that CEO can achieve the strategic and operational objectives of their agency using different BI models. Big data is one of the most effective solutions that can help CEOs to understand the previous records and current data status of their agency so, that appropriate planning and decisions can be made.

To make AIWS a data-driven agency, CEO has to identify the business and strategic values of big data instead of focusing on technological understanding. To transform this health and welfare agency, the appropriate strategies are required to formulate. So, to leverage the benefits and improvement strategies of Big data analytics effectively, five key strategies are recommended below-

Deployment of big data governance

Governance of big data is an upgraded version of IT governance that concentrates on benefitting the organizational large data resources to build business values. Big data analytics will help AIHW CEO in expenditure management, IT investment, financial planning, and healthcare data governance. The organizational heterogeneous knowledge, information, and data can be easily governed internally by CEO by changing the business processes in more accessible, secure, and understandable manner. To govern the data firstly it is required to frame the mission with clear goals, governance matrices, execution procedures, as well as performance measures. Then, the data is required to review for making decisions. Finally, integration of information is essential to lead big data deployment to encounter issues and develop robust business values.

Building information distribution culture

The importance of big data analytical implementation is to target the AIHW goals to foster the information transformation culture. This involves collection of data and delivery to address the challenges and develop policies to meet business achievements. This will improve data quality, accuracy, and prediction as well.


CEO is responsible to understand, evaluate, and make the decisions accordingly. Thus, CEO needs to conduct training programs to use the outputs of big data effectively in order to interpret the results. In this manner, CEO of AIHW should arrange training for personnel to utilize big data analytics (Tamers, et. al., 2019).

Incorporation of Cloud Computing

To improve the cost as well as storage issues in AIHW, it will be a better solution to integrate cloud computing into big data analytics. This will make data-driven decisions more accurate, fast, and operable. CEO will also enable to visualize the organizational different information sets including different areas, expenditures, and factors. Thus, CEO can balance between protection of patient information and cost-effectiveness by integrating both these technologies.

Creating new business insights

New insights can be created using big data analytics By CEO which leads to updating the business functions that increase competitive advantages and productivity. The AIHW CEO will also allow leveraging the outputs such as alerting KPIs, reports, market opportunities, interactive visualization, and feasible ideas (Grover, et. al., 2018).

Other strategies that can be recommended in improving AIHW operations are-

Accessibility- CEO should understand the entire organizational operations and datasets so, that the database can be designed accordingly. Different information can be accessed separately which will help CEO to access, update, and make decisions more quickly.

Utilization- This is also an essential factor that utilization of any database should be done more attentively so, that the data can be interpreted in knowledge for business decisions.

Validation- Validation is another factor that ensures security so, CEO should focus on robust security system. This will help CEO to protect sensitive information of entire AIHW assets including patients, staff members, equipment, machines, details, records, etc. (Hsieh, et. al., 2019).

The main reasons for using the suggested solutions for achieving the strategic/operational objectives for AIHW CEO are-

• Cost-effectiveness
• Accurate decision-making strategies
• Automatic data processing (accessibility, update, delete, transformation)
• Rapid data delivery
• Robust integration
• High-level security
• Marketing & competitive advantages

Task5- Cover Letter

Australian Institute of Health & Welfare Agency,

Dear Mr. XYZ,

With overall description and analysis, the Australian Institute of health and welfare agency has specific goal to review the expenditure for financial years 1997 to 2012. To achieve this specific mission of AIHW, CEO should focus on leveraging their business operations using the in-depth analysis of their corresponding business datasets. According to the analysis, AIHW has been identified with higher expenditure values. This signifies that the revenue is also good but still, the increasing market structure and competitiveness, this is a possibility of higher expenditure but less revenue growth. In this manner, it is essential for CEO to take responsibility and enhance the scope of their business using strategic objectives. Some relevant recommendations are outlined in the following section that encourages

CEO to understand and apply the business objectives for better results specifically. These recommendations are-

To understand and develop big data strategic values

Big data analytics play a vital role in incorporating strategic business values so, CEO of AIHW can also lead these strategic values as shown in below figure-


Figure 8: Strategic Values of Big Data Analytics
(Source: Grover, et. al., 2018)

The functional value leads to financial performance, market share, and many more as it defines to improve the performance by adopting big data analytics. On the other hand, symbolic value refers to a broadly derived value via signaling effects of investment in big data analytics. Symbolic value comprises mitigation of environment load, organizational reputation, brand, and many more.

To strategically fit the business needs of AIHW, CEO should rely on functional value to balance organizational operations and technological awareness. In this manner, CEO will enable to ensure organizational business efficiency, coordination in operations and personnel, and decision-making features (Grover, et. al., 2018).

To implement innovative E-health record/system

CEO should take an initiative to develop an innovative e-health system to record the datasets and manage the organizational operations more effectively, accurately, securely, and flexibly. The CEO can use this system to conduct the business transparently and achieve business goals more quickly as multiple advantages can be leveraged for CEO including predictive data values, cost and smarter business decision-making capabilities, data storage, management, and improved outcomes as shown below-


Figure 9: E-Heath System
(Source: Dash, et. al., 2019)



Fill the form to continue reading

Download Samples PDF