COIT 20253 Business Intelligence using Big Data Strategy Assignment

COIT 20253 Business Intelligence using Big Data Strategy Assignment

COIT 20253 Business Intelligence using Big Data Strategy Assignment

1. Introduction

Big data has largely changed the scenario of world business by managing, analysing and leveraging the huge amount of data within an industry. One of the most effective areas of Big data is implemented to make changes in the organisation. This study has developed the extensive ideas on Big data through the applications healthcare sector. This study has also discussed the methods of selecting the proper Big data application, in regards to the purpose of the businesses or services offered.

COIT 20253 Business Intelligence using Big Data Strategy Assignment

2. Difference between online and Offline Data

Big Data is recognised as a buzzword in the modern era of business. It uses to cover a wide spectrum of business across the world. However, the primary objective of Big Data is to determine the best strategies for the organisations. According to Chen et al.(2014, p.182), enhancing the ability to define the data management strategy is often liable to describe an organisation as the winner in the market. Big data simply refers to the data that is massive in volume. In most of the cases, the Big data is found with high complexity, which also ensure an in-depth knowledge. Big data is not meant to be controlled by the traditional software tools, as it always involves several new technologies. The highest accepted technologies in the Big data structure are creation, storage, retrieval and the analysis of data. In the words of Minelli et al.(2012, p.34), Big data technology is usually defined by two classes, such as Online Data Technologies and Offline Data Technologies.

Online data technologies in Big data use to offer the major operational capabilities for the real-time. It also indicates at the interactive workloads, where the maximum amount of data is stored and ingested. Online data can be formed in a structure of bulk data and stored for a long while, depending on the validity of them. However, in some cases, Big data is also liable to ensure a permanent storage of needful data for the organisation. The most frequent example of online big data includes the news feeds on the social networking sites followed by the real-time ad servers, analytics tools, and CRM applications. It is highly prioritised in the healthcare workplaces, where a bulk data is needed to be stored. The care users often seek the previous data of their users in order to enable the best possible services to them.

Offline Big data technology, in contrast, provides the analytical capabilities for a retrospective of the data structure. It is a continuous process and meant to be kept in chronological order. Offline Big data system also refers to the sophisticated analysis of the obtained data, which are liable to be accessed from diverse avenues. It is also prioritised in the healthcare workplaces in order to access the short-term data of the users or other participants in it. As opined by Murdoch, T.B. and Detsky (2013, p.1351), both the data technologies are recommended in the healthcare sector and none of them can be put over the necessity of the other.

3. Strategy to select right Big Data application

The fundamental objective of Big data is to describe the primary tracks of operations by the organisations. In this modern era of business, both the sources and the volumes of the obtained data are meant to be exploded. The Big data technologies have made possible for the health organisations to collect and store the click-stream data, which have been found efficient enough for the process of healthcare workplaces. As stated by Wu et al.(2014, p.102), the selection of appropriate Big data application makes the half job done for the organisation. Thus, companies are always recommended to be definite to select the right Big data application, in regards to the nature of their business.

Big Data Implementation Technique

The best strategy to select the appropriate most Big data application is considered through two major dimensions. The first dimension is considered as the labelled business objective, whereas, the other one is labelled data type. According to Barrett et al.(2013, p.172), companies use this technique to measure or experiment the effectiveness of the strategies in the first dimension. However, the other dimensions evaluate the normal course of functioning on the given sector. The first dimension takes the values of the measures under consideration, while the second dimension is involved with collecting the transactional data. Transactional data refers to the development of the unstructured data into real-time figures. However, companies use to treat the questions as a hypothesis, while verifying the scientific methods. This combination of the database is usually formed in a quadrant structure and represents diverse strategies to the organisations.

In the healthcare sector, the management cares for the transactional strategies more to adopt the best big data application. They are always found prioritising the social analytics and decision science methods above all while selecting the Big data applications. They are in ways to improve their performance management always. They ensure the best performance management to be implicated and continued with a developing process. Data exploration is another continuous process that is meant to be run as per the submission of the other factors in the quadrant.

4. Listed desired outcome from Big Data Solution

The application of Big data is mostly relative and varied in nature from one sector to another. The outcome of the big data applications is usually expected to be justified with volume, velocity, variety, variability and complexity. These are the major factors of Big\data technologies, which are liable to ensure the effective outcome of the application.

Electronic Health Records are considered as the most widespread application of Big data, which have served the best outcomes for the healthcare sectors. This application refers to the proper maintenance of digital records of the patients (Minelli et al. 2012, p.31). Each patient is covered by this application with all sort of needful data and represents them to the service providers to increase better understanding.

The digital records of the patients usually include their medical history along with their family backgrounds. It also intends to store a large amount of data contained in their demographics, allergies, and laboratory test results.

Under this Big data application, the information is remained safe and secure. In most of the cases, the records are comprised of at least one modifiable file. The Big data application also encourages the doctors to implement prior changes in the collected measures and data of the patients.

5. Discussion on Technologies used in Big data solutions

The healthcare sector needs to manage the huge amount of data and the inefficient system of managing the data is creating issues. This problem is created for lack of electronic data management system. However, problems arise due to malfunction and lack of knowledge using of big data. Healthcare platform needs to manage the huge amount of data that are coming from various sections, like lab results, billing data, sensors data from genomics, images etc. These kinds of data are various like unstructured data from dictation, photos and transcription etc.

Big data analytics is a talking point for most of the healthcare sectors. Big data refers to the vast quantities of data that must be included in the vast records.

UPMC’s Analytics Platforms:

University of Pittsburgh Medical Centre (UPMC) is an organisation that uses the genomic data to have a personalised solution. This data warehouse is kept clinical and genomic information for more than 140 breast cancer patients. They have been largely working on the breast cancer data storing mainly pre and post-menopausal issues in breast cancer.

Explorys:

Explorys is a Cleveland Clinic spinoff company that is currently working on the big data. This company has been experimenting with the big data to give the tools for clinical support for the risky patient in population management. They are providing big data analytics for the cost of care management. Explorys has innovated the world's one of the largest healthcare databases that can apply more than 100 billion data points in the database management. This database technology helps the clinicians to describe troves of data from various sources, like Electronic Health Records (EHR) and financial data. This Explorys analytics data helps the providers finding the variations among treatments and patients (Explorys.com, 2016).

NextBio:

NextBio utilises the data for the human genome to help the users in making the personalised medical decisions. This big data analysis uses both public and genomic data. Clinical information can be saved through this technology in clinical information for the individual patients that have been uploaded by the providers. NextBio has been looking for clinical data to invent the biomarkers to predict the metastases in brain cancer among young children.

6. Business impact of Big Data

Big Data has been helping the world to make it a better place and possibly the healthcare business is the strong example of it. The last decade can be a good example for advancement in healthcare organisation related to the big data. It is just beyond the making of profit for the organisation, it has been used for the stopping of epidemics, improve the quality of life and cure diseases. With the increasing of the population in the world, everyone is living a longer life. It is possible because of the lifestyle of the people. In healthcare more than 3 trillion dollars are spent in hospital billing, in this scenario, Big data can be used (Ohlhorst, 2013, p.15).

Big data is helping the healthcare industry to look for the idea of prevention is better than cure. It can start from the smartphones of the people and it helps the people with so many apps like pedometers, FitBit, Jawbone etc. In health care sectors, it allows the organisation to state the health of the general public in diagnosing the diseases and the reason behind the disease. The organisation can easily find out the numbers of people who are suffering from the same diseases. The healthcare organisation prepares now for the remedies in medicinal or educational problems in advance. In healthcare business, medical professional are trying to make a partnership with the data professionals for the potential success in identifying the issues of the patients. The healthcare sectors are finding different types of data from the big data analysis, such as insurance, medical records, genetic data, wearable sensors, social media use. In the healthcare market, studying Flu, Bronchitis and Asthma are going easy with the discovery of Headop technologies in analysing the respiratory issues. It helps in linking the geographic areas with expected asthma cases globally. This big data analysis helps to predict the outbreaks of the diseases. IBM made a team with the University of California in identifying the outbreaks of malaria and dengue. They have used open-source modelling application for Spatio Temporal Epidemiological Modeler (STEM) for using the data quickly correlating with diseases (Sparkblog.emc.com, 2016).

7. Organisational impact of Big Data

Some of the hospitals have been using Big Data solutions in the UK in order to mitigate some issues in health care. Mount Sinai Medical Center has been teaming with the Big Data technologies in order to lower the cost of the healthcare and prevent the hospitalisation. This organisation is in the US and they have joined over more than 250 doctors, hospitals and nurses for making their data procedure (Icahn.mssm.edu, 2016) . They are making this process for advising more sick patients and they can use the data for the better purpose.

Healthcare organisations use mainly the Big data for human and plant genomics, patients community analysis and decision support (Hoffman and Podgurski, 2013, p.55). This kind of data helps the patients in order to take the active role on health issues, like exercise, diet. Data can be improved with reducing the errors in healthcare and the doctors can use the big data tools in personalised and specific patients. These data help the patients to have the better care in the assessment of the care. Moreover, innovation has been made in eliminating the waste, fraud and abuse in healthcare. In addition, healthcare organisations can use the data for safety utilisation, development of health and analyse the trends.

8. Conclusion

 In recent time, healthcare centres are using the big data for negotiating the huge amount of data. Big data in healthcare has been creating phenomena and revolution with increasing of supply the patient's information. This data-driven policy in health care optimises the betterment of the health of patients. This high quality of care reduces the cost in a long run for the patients and the provider. Financial outcomes for the healthcare organisations and meeting the big data demand have been increasing in healthcare. EHR needs to be used in a meaningful way to provide the solution as there is no danger in replication of a file.

Reference List

Books:

Minelli, M., Chambers, M. and Dhiraj, A., (2012). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. US: John Wiley & Sons.

Minelli, M., Chambers, M. and Dhiraj, A., (2012). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. US: John Wiley & Sons.

Ohlhorst, F. (2013). Big data analytics. Hoboken, New Jersey: John Wiley & Sons.

Zikopoulos, P. and Eaton, C., (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. New York: McGraw-Hill Osborne Media.

Journals:

Barrett, M.A., Humblet, O., Hiatt, R.A. and Adler, N.E., (2013). Big data and disease prevention: From quantified self to quantified communities. Big data, 1(3), pp.168-175.

Chen, M., Mao, S. and Liu, Y., (2014). Big data: a survey. Mobile Networks and Applications, 19(2), pp.171-209.

Chute, C.G., Ullman-Cullere, M., Wood, G.M., Lin, S.M., He, M. and Pathak, J., (2013). Some experiences and opportunities for big data in translational research. Genetics in Medicine, 15(10), pp.802-809.

Hoffman, S. and Podgurski, A., (2013). Big bad data: law, public health, and biomedical databases. The Journal of Law, Medicine & Ethics, 41(1), pp.56-60.

Murdoch, T.B. and Detsky, A.S., (2013). The inevitable application of big data to health care. Jama, 309(13), pp.1351-1352.

Murdoch, T.B. and Detsky, A.S., (2013). The inevitable application of big data to health care. Jama, 309(13), pp.1351-1352.

Wu, X., Zhu, X., Wu, G.Q. and Ding, W., (2014). Data mining with big data.IEEE transactions on knowledge and data engineering, 26(1), pp.97-107.

Websites:

Icahn School of Medicine at Mount Sinai. (2016). Icahn School of Medicine at Mount Sinai - New York City | Icahn School of Medicine. Available at: http://icahn.mssm.edu [Accessed on: 16 Aug. 2016].

Sparkblog.emc.com(2016). Available at: http://sparkblog.emc.com/2014/05/big-data-healthcare-using-health-innovation-accelerate-value/ [Accessed on: 16 Aug. 2016].

Explorys - Healthcare Big Data Analytics. (2016). Welcome to Explorys. Available at: https://www.explorys.com/ [Accessed on: 16 Aug. 2016].