Term 1 2021 COIT20253: Business Intelligence using Big Data Assessment 1

Assignment 1: Written Assessment

Weighting: 35%       

Assessment Task:

This is an individual assessment.         

In this assessment, you are required to choose one of the following industries: Healthcare, Insurance, Retailing, Marketing, Finance, Human resources, Manufacturing, Telecommunications, or Travel. This assessment consists of two parts as follows:

Part A – You are required to prepare a report on how Big Data could create opportunities and help the value creation process for your chosen industry.

Part B – You need to identify at least one dataset relevant to the industry and describe what opportunities it could create by using this dataset.

In Part A, you will describe what new business insights you could gain from Big Data, how Big Data could help you to optimize your business, how you could leverage Big Data to create new revenue opportunities for your industry, and how you could use Big Data to transform your industry to introduce new services into new markets. Moreover, you will need to elaborate on how you can leverage four big data business drivers- structured, unstructured, low latency data, and predictive analytics to create value for your industry. You are also required to use Porter’s Value Chain Analysis model and Porter’s Five Forces Analysis model to identify how the four big data business drivers could impact your business initiatives.

In Part B, among several open source and real-life datasets, you will identify at least one dataset that is relevant to the industry you had chosen. The dataset can be a collection of structured, unstructured or semi-structured data. Using this dataset, you will first discuss how you chose this dataset among other datasets. Then, you will identify and present the metadata of the dataset. Using the chosen dataset, you will need to describe the opportunities it could create for the chosen industry.

The length of the report should be around 2500 words. You are required to do extensive reading of more than 10 articles relevant to Big Data business impacts, opportunities and value creation process. You need to provide in-text referencing of chosen articles.

Your target audience is executive business people who have extensive business experience but limited ICT knowledge. They would like to be informed as to how new Big Data technologies might be beneficial to their business. Please note that a standard report structure, including an executive summary, must be adhered to.     

The main body of the report should include (but not limited to) the following topics:

1.         Big Data Opportunities

2.         Value Creation using Big Data

3.         Porter’s Value Chain Analysis

4.         Porter’s Five Forces Analysis

The length of the report should be around 2500 words. You are required to do extensive reading of more than 10 articles relevant to Big Data business impacts, opportunities, and value creation processes. You need to provide in-text referencing of chosen articles.  

Your report must have a Cover page (Student name, Student Id, Unit Id, Campus, Lecturer and Tutor name) and a Table of Contents (this should be MS word generated).

 ALL assessment submissions will be checked for plagiarism by Turnitin.

Assessment Submission:

You must upload the written report to Moodle as a Microsoft Office Word file by the above due date.

Assessment Criteria:

You will be assessed based on your ability to analyse and reflect on how organisations are leveraging non-traditional valuable data (unstructured, real-time) with the traditional enterprise data (structured) for business intelligence and value creation. The marking criteria for this assessment are as follows.

Part A (25 marks):

Executive Summary – 3 marks

Table of Contents – 1 mark

Introduction – 2 marks

Big Data Opportunities – 4 marks

Value Creation using Big Data – 4 marks

Porter’s Value Chain Analysis – 4 marks

Porter’s Five Forces Analysis – 3 marks

Conclusion – 2 marks

References – 2 marks

Part B (10 marks):

Dataset identification – 2 marks

Metadata of the chosen dataset – 3 marks

Business opportunities through the chosen dataset – 5 marks


COIT20253: Business Intelligence using Big Data

                            Assessment 1

                   Business Intelligence using Big Data

Link for the data sets– https://www.kaggle.com/rashikrahmanpritom/heart-attack-analysis-prediction-dataset

Executive Summary

This report is about big data in the health care industry, how big data has helped in the health care industry, changing the lives of people, improvising business opportunities, and creating a value chain for wellbeing. The affiliated element data incorporates the center watchwords essentially found in wellbeing huge information and their acquainted catchphrases. For the assortment of wellbeing reports, Web pages were examined and different articles were searched for the correct dataset to validate the report (Cheryl Ann Alexander and Lidong Wang, 2017). Different techniques have been presented to clarify big data opportunities and benefits in the health care industry


Executive Summary

1.0        Introduction

2.0 Big Data Opportunities

2.1 Advantages of Big Data in Health Care

2.1.1 Information Collection

2.1.2 Information Extraction

2.1.3 Modelling

2.1.4 Information Visualization

3.0 Importance of Big Data in Health Care

3.1 Arrangement of driven administrations to patients

3.2 Identifying spreading infections prior

3.3 Adjusting the treatment methods

4.0 Value Creation of Big Data in health Care

4.1 Structured Data

4.2 Unstructured Data

4.3 low latency data

4.4 Predictive analysis. telemedicine

5.0 Porters Five Process Model for Health Care Industry

Part B

6.0 Dataset Classification

7.0  Metadata of the chosen Dataset

Business Opportunities of the chosen Dataset 

Detection of Heart attack through wireless



1.0  Introduction

The healthcare industry is data-intensive and could use interactive dynamic big data platforms with innovative technologies and tools to advance patient care and services (Galetsi, Katsaliaki & Kumar 2020). To manage cardiovascular disease, we have to assess huge scores of datasets, think about and dig for data that can be utilized to anticipate, forestall, oversee and treat persistent sicknesses, for example, coronary episodes. Enormous Data examination, known in the corporate world for its important use in controlling, differentiating, and overseeing huge datasets can be applied with much accomplishment to the forecast, anticipation, the board, and treatment of cardiovascular illness (Alexander and Wang, 2017).

2.0 Big Data Opportunities

2.1 Advantages of Big Data in Health Care

 Some advantages of big data over health care are listed below

2.1.1 Information Collection

This action requires the assortment of information through different illnesses like diabetes and so on the underlying sign are pulse, a circulatory strain which is estimated by ECG, EEG. There are numerous suppliers in the market for giving body sensors. Wellbeing signals are continually gotten from on-the-body or in-the-body sensors and accordingly scholarly by the cell phone (Kulkarni et al., 2020).

 2.1.2 Information Extraction

 Huge information investigation innovation is utilized to remove and dissect significant information and expected qualities from organized information, semi-organized information, and unstructured information that surpass the preparing scope of an overall data set administration framework. The term organized information alludes to the information saved in fixed fields, including social data sets and office data, semi-organized information alludes to information that incorporates metadata and blueprint, despite the fact that they are not saved in fixed fields, and unstructured information alludes to the information that are not saved in fixed fields, including text, video, voice, picture, and mixed media (Telemedicine and e-Health, 2020).

2.1.3 Modelling

Information digging apparatuses are utilized for prescient displaying which helps in the expectation of patterns and examples. In this sort of demonstration different indicators are utilized for anticipating different assortments of information.

2.1.4 Information Visualization

Information representation is an instinctive route for clients to effortlessly peruse and get information, particularly in huge information examinations. It assists with improving the nature of approaches or administrations by introducing a coordinated view and proof for settling on medical care choices (Ko and Chang, 2017).

It is likewise castoff in the prognostic examination which is to perceive and talk about the therapeutic issue before it turning into a wild issue. Medical care experts are skilled to diminish the threat and overpowered the issue with the material imitative from the huge information. Data Visualizations were utilized to morally return wellbeing information to low-wellbeing education patient populaces (Skiba, D.J., 2014).

3.0 Importance of Big Data in Health Care

3.1 Arrangement of driven administrations to patients

To convey faster guide to the patients by giving sign related medication recognizing indications and infections at the earlier stages that rely upon the clinical data possible, decreasing painkiller measurements to lessen result, and giving successful drug made on heritable makeup. These advantages in diminishing readmission degrees accordingly diminishing the rate for the patients.

3.2 Identifying spreading infections prior

Calculating the viral sicknesses preceding earlier dispersing made on the live examination. This can be perceived by assessing the local area logs of the patients upsetting from an ailment in a particular spot. This guides the medical care experts to coordinate the victims by having fundamental guarded systems. Noticing the clinic’s quality: To check whether the facilities are organized according to principles given by the Indian restorative gathering. It benefits the organization in checking fundamental activities in the logical inconsistency of restricting facilities.

3.3 Adjusting the treatment methods

The registration of the changed casualty tells the outcomes of meds continually and by these examinations amounts of prescriptions can be modified for fast outcomes. By checking patient’s enthusiastic signs to offer dynamic precautionary measures to patients, making an examination on the reports created by the patients who recently experienced comparable signs, helps the expert to convey genuine tablets to different casualties. Large information is progressively applying a significant impact on worldwide creation, flow, dispersion, utilization exercises, financial activity component, social way of life, and public administration limit (Guo and Chen, 2019)

4.0 Value Creation of Big Data in Health Care

  4.1 Structured Data

The structured data in this dataset contains the patient’s data such as age, gender, and life habits. For doctors impervious to organized information catch, a crossbreed approach should be built up that mixes the capacity to catch required organized information components and gives doctors the adaptability to record in their very own way. This will expand doctor appropriation while meeting information detailing and information trade needs (Guest Blogger, 2011).

                      Figure 1:  Structural data Sources of Big Data in Health Care

                      Source: (Bing.com, 2021)

4.2 Unstructured Data

The unstructured data consists of physician notes, x-ray images, diseases onset prediction, medical documentation, accuracy. Clinical diaries can be perused by machines to extricate the most significant data to be made accessible for suppliers. Contact focus specialist notes can be broke down to recognize drivers of positive or negative patient estimations and for distinguishing openings for decreasing call taking care of time, call volume, intelligent voice reaction nonconformist, and Repeat Calls. Doctor notes can be dug for readmission forecast, infection beginning expectation, clinical documentation exactness, and the sky is the limit from there. (Journal Of AHIMA, 2018)

4.3 low latency data

Low inactivity is requesting quick, predicable and deterministic reaction time as a business need. Monetary firms still presumably have the best requirement for ultra-low inertness, yet low inactivity is currently significant for some organizations, regardless of market. Associations are progressively getting enormous volumes of information across their organization and doing so rapidly and effectively is basic. Huge information and low inactivity are intensely connected. The expected worth of enormous information is colossal however it relies upon having the option to break down and get understanding from it progressively. The effect and significance of dormancy relies upon the particular application and accomplishing the least conceivable inactivity requires a compromise between other organization attributes. Having identified the significance of low dormancy in our large information foundation that offers exposed metal force for speed and execution, we made it a necessary piece of our contribution (Bigstep, 2013).

4.4 Predictive analysis

The expression “Predictive analysis” portrays a strategy of getting an understanding into the conceivable future occasions dependent on the accessible information and measurable investigation, addressing the inquiry “What may occur?”

Figure 3: Health Care Provider Value chain

Source: (Hubspotusercontent30.net, 2021)

4.5 Practicing  telemedicine

Telemedicine has been demonstrated to be particularly valuable in underserved networks where there is a lack of nonappearance of sufficient clinical consideration, for example, in far-off territories. Interestingly, in created and agricultural nations the same, demonstrated, dependable, and savvy telemedicine and telehealth administrations are accessible at scale. Subsequently, because of the vigorous empowering innovation foundation, the incredible guarantee of telemedicine has at last shown up. Maybe then move the patient to the clinical subject matter expert, it is currently ordinary to saddle the force of innovation to communicate the information on the expert right to the patient out of luck (Nittari et al., 2020).

Figure 2: Working Principle of NLP-based AI system used in the massive data retention analysis in linguistic

Source: (Dash et al., 2019)

5.0 Porter Five Process Model for Health Care Industry

5.1 Competitors and New Entrant threat

Different healthcare companies lead in terms of margin over other companies from other health care companies showing fierce competition. Industries can gain a lot of benefits because of the competition as they can switch the cost of the medicine. An individual person can choose only one health care policy so they will use only one policy and not be able to switch between policies (Adamkasi, 2017).

5.1 Buyers Bargaining Power

Customers hold a weak bargaining power so they have a weak position to bargain. For availing of the health care service, a certain price has to be paid. Customers are bound to take the health insurance and pay the price because they can’t afford to pay the cost of life-saving operations if they get injured. So, according to Porter, Value generation is more important than price. This is the reason people choose high-standard health care facilities they can afford and the industry on the other hand gains margin from a value-based competition. Hence, the ultimate goal of the industry is to create enough value and establish a stable economic environment (Adamkasi, 2017).

5.2 Suppliers Bargaining Power

Suppliers stand a high position for bargaining as they are the ones who totally supply the goods to the health industry, being fewer in number than the consumers, they got enough demands from the people as well as the government sector (Adamkasi, 2017).

5.3 6Substitution of Existing Products

Customers always try to find the substitute of any products in terms of cost and quality, they prefer cheaper medicines and prescriptions. They visit different medical stores where they can find cheaper drugs however substitutes for complete health coverage plans and the situation is less alarming for future prospects (Adamkasi, 2017).

Customers at present like to adapt “do it yourself strategy” in this digital world most people want to adopt all the trends and want to be connected with the technology so they keep most of the medical equipment at home (Adamkasi, 2017).

Part B

6.0 Dataset Classification

The chosen dataset contains 14 columns and 304 rows of data information about health status report about heart attack analysis and prediction analysis. The dataset contains information about Age, sex, Chest pain and type of chest pain, resting blood pressure in mm hg, cholesterol level in the blood in mm hg, cholesterol in mg/dl fetched via Body Mass Index (BMI)of a person, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise included angina, and previous peak (Rashik Rahman, 2016)

7.0  Metadata of the chosen Dataset

The metadata for this dataset is provenance and the source is online got through the method crawling and the owner of the dataset is Rashmik Rahman where he updates the data annually. The dataset was created on 2021-3-22 and is version 2 of the analysis and the file format is in .csv format (Rashik Rahman, 2016).

8.0 Business Opportunities of the chosen Dataset

8.1 Detection of Heart attack through wireless

Cell phones and sensors can distinguish and send different wellbeing information. A wristwatch has been planned as Heart Attack Detection hardware utilized every day to demonstrate a heart condition, distinguish respiratory failure, and call for crisis help. Planned particularly for patients with coronary illness, it can diminish grimness and mortality as well as handicap too. The ECG is amazingly significant as a device for distinguishing coronary failure. ECG is an electrical chronicle of heart action and can be used in the examination of coronary illness. The wristwatch contains an ECG hardware unit that catches strange heartbeat signals from the patient. The microcontroller on the watch at that point runs a coronary episode calculation and the Bluetooth crisis calling framework dials clinical help during the hour of coronary episode. There will be two biosensors worn on the patient’s wrist which conveys the ECG message to the simple ECG hardware.

The enhanced and sifted simple yield of the hardware is made an interpretation of from simple to advanced sign and afterward communicated to the unit on the strolling watch.  The ECG hardware unit, the A/D converter, and the transmitter are worn on one of the patient’s wrists. The watch is remote, giving the client more opportunity to move by keeping away from wires between the watch and the wrist. The patient wearing the watch gets an advanced ECG signal, and the microcontroller runs a respiratory failure calculation to recognize potential coronary episode indications. Assuming any side effect of a respiratory failure is identified, the danger level ascents. When a patient’s danger level arrives at the crisis mode, the Bluetooth module actuates the client’s cell phone to call 911 for clinical assistance. This can be a good scope of business as this is the current scenario most phones with these kinds of models are going for a good sell amount (Alexander and Wang, 2017).

8.2 Disease Forecasting and developing medicines using the Internet of Things

The Internet of Things (IoT) is a trendsetting innovation that exploits a few strengths like sensor advancement, the information obtained, the executives and handling, and correspondence and organizing were subjects (for example objects, individuals) with interesting attributes that can connect to a far-off worker and structure nearby organizations. Since the network in IoT-based frameworks grants objects to exchange, what’s more, combine information to acquire extensive information about their usefulness and characteristics of the adjoining conditions, it offers prevalent, canny, and efficient administrations. IoT innovations offer an improved personal satisfaction for people through nonstop (i.e., all day, every day) distant checking frameworks which is one of the essential highlights of this innovation.

Distant wellbeing checking turns out to be much more significant being taken care of by older patients because of the expanded feebleness also, vulnerability to different illnesses (for example intense and ongoing sicknesses) of mature age. Not exclusively does distant wellbeing observing improve the personal satisfaction of old patients, distinguishes and advises guardians and suppliers of crises, lessens nursing care needs and emergency clinic stays (for example medical services cost decrease), it can foresee and follow infection cycles, for example, respiratory failures (Alexander and Wang, 2017).


Ayasdi is one such large seller which centers around ML-based strategies to basically furnish machine insight stage alongside an application structure with attempted and tried Endeavor adaptability. It gives different applications to medical care examination, for instance, to comprehend and oversee clinical variety, and to change clinical consideration costs. It is equipped for breaking down and overseeing how medical clinics are coordinated, a discussion between specialists, hazard situated choices by specialists for therapy, and the consideration they convey to patients. It additionally gives an application to the appraisal and the board of populace wellbeing, a proactive procedure that goes past conventional danger investigation techniques. It utilizes ML insight for anticipating future danger directions, distinguishing hazard drivers, and giving answers for best results (Dash et al., 2019).

                                                   Figure 2: Intelligent Application Suite

                                                   Source: (Dash et al., 2019)

9.0 Conclusion

This report clarifies the importance of big data in the healthcare industry we have also studied the datasets and identified, what constraints were used to classify the data and to predict the future disease control and prevention methods and technologies that may help the upcoming generation to be alert on their health conditions. We have come to know the importance of big data in the health care industry how it could change the lives of people. This report contains the most recent data on Big Data examination in medical services, foreseeing cardiovascular failure, also, fitting clinical treatment to the person. The outcomes will control suppliers, medical care associations, attendants, what’s more, other treatment suppliers in utilizing Big Data advances to foresee and oversee cardiovascular failure just as what protection concerns face the utilization of Big Data examination in medical care. Viable and customized clinical treatment can be created utilizing these advancements. we can also know different business models and business values that could make this sector a big possibility in the future. Consequently, to permit the proficient administration and utilization of the archives, strategies have been initiated, and contemplates have been directed. In this paper, a technique for removing acquainted component data utilizing text mining from wellbeing large information was proposed.


Adamkasi (2017). Porter Five Forces Model of Health Care Industry|Porter Analysis. [online] Porter Analysis.

Alexander, C.A. and Wang, L., 2017. Big data analytics in heart attack prediction. J Nurs Care6(393), pp.2167-1168.

Bigstep. (2013). Low latency and big data.

Bing.com. (2021). value chain in big data in health care – Bing.

‌Dash, S., Shakyawar, S.K., Sharma, M. and Kaushik, S., 2019. Big data in healthcare: management, analysis, and future prospects. Journal of Big Data6(1), pp.1-25.

‌Galetsi, P., Katsaliaki, K. and Kumar, S., 2020. Big data analytics in the health sector: Theoretical framework, techniques, and prospects. International Journal of Information Management50, pp.206-216.

Guest Blogger (2011). The importance of structured data elements in EHRs. [online] Computerworld.

‌Guo, C. and Chen, J. (2019). Big Data Analytics in Healthcare: Data-Driven Methods for Typical Treatment Pattern Mining. Journal of Systems Science and Systems Engineering, [online] 28(6), pp.694–714

Journal Of AHIMA. (2018). Unstructured Data: An Important Piece of the Healthcare Puzzle | Journal Of AHIMA

‌Ko, I. and Chang, H. (2017). Interactive Visualization of Healthcare Data Using Tableau. Healthcare Informatics Research, [online] 23(4), p.349.

‌‌Kulkarni, A.J., Siarry, P., Singh, P.K., Abraham, A., Zhang, M., Zomaya, A. and Baki, F. eds., 2020. Big Data Analytics in Healthcare. Springer.

Nittari, G., Khuman, R., Baldoni, S., Pallotta, G., Battineni, G., Sirignano, A., Amenta, F. and Ricci, G. (2020). Telemedicine Practice: Review of the Current Ethical and Legal Challenges. Telemedicine and e-Health, 26(12).

Rashik Rahman (2016). Heart Attack Analysis & Prediction Dataset. [online] Kaggle.com.

Skiba, D.J., 2014. The connected age: big data & data visualization. Nursing Education Perspectives35(4), pp.267-269.

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