COIT 20253 Creating a Big Data Strategy - Free Samples to Students
2. Organisational and cultural issues of the ‘Data-driven’ organisation;
3. Leveraging big Data for enhancing decision making and creating new businessmodels
4. Social networks for exploiting knowledge or creatingintelligence;
2. Identify and align business initiatives, objectives and tasks with the developed Business Strategy.
3. Identify and discuss the required Technology Stack
4. Discussion on Data Analytics and MDM to support DS&BI
5. Discuss support of NoSQL for Big Data Analytics.
6. Discussion on different NoSQL Databases and its use in Big Data use case you have chosen.
7. Role of Social media in organisation's decision making process
8. Discussion on Big Data Value creation process.
Answer:
Introduction
Big data is a term that has been used to describe vast amount of unstructured and structured data that drives the operations of a business or organization on daily basis. What is of importance is how the data is used and not the amount of data because an institution may have a large amount of data but it has no relevance to their operations and decision-making process. Big data is often analyzed to get a deeper insight on trends to make strategic business moves and decisions. In health sector, a lot of data is collected every day from diagnostic images, mobile applications, EHRs, and social media. Most of this data is not utilized to enhance the operations of the health sector. For instance, Roski, Bo-Linn and Andrews (2014, p. 1119) have argued that less than 20% of the data collected through electronic health records can be converted into structured data to enable analysis by employing the native analysis and retrieval techniques.
In addition, big data makes it easy to connect different data and phenomenon to get a specific solution to medical, research, or operation questions. This report will focus on identifying, creating, and discussing big data use case business strategy. Secondly, it seeks to find and align business objectives, initiatives, and tasks with business strategy created, determine and expound on the technology stack required, discussion on how to support DS and BI using MDM and data analytics, discussion on how NoSQL supports big data analytics, description of the different NoSQL databases and how it is employed in big data, the purpose of human elements and social media in decision making process, and finally the process of creating value using big data.
Business Strategy for Big Data in Health Sector
Currently, in health sector, the success and impact of big data use cases are on the rise. Big data have raised some major concern in health sector even though its benefits are undoubtedly great. Many medical institutions have implemented big Data strategies in their different operational sectors but some are still struggling to identify a better way to incorporate it (Shaw, 2015, p. 19). Adopting big data business strategy in heath sector requires in-depth analysis of several aspects such as; it is important to determine the requirements of the institution, that is, what you want. Business objectives and goals have the greatest effects on the shape of the overall strategy. It is important to determine whether you want to improve the operational efficiency, improve patient/customer relationships, improve medical diagnostics, improve marketing, or increas
e revenues. The goals and objectives should be specific, measurable, achievable, relevant, and time (SMART) as stated by Catlett (2013). Additionally, the objective should be certain, precise, and direct.
Secondly, go for a big data strategy that has been proven workable. There exists four ways that has been proven to develop a workable big data strategy. Depending on data availability and the end objective, a medical institution can employ the use of either of the following strategies to achieve successful outcome: performance management- involves using medical transactional data to make decisions related to operational supremacy and management (Dhar, 2014, p. 55). The data available within the health sector gives more knowledge and insights into phenomenon for long-term and short-term decisions. Since there is massive information and data available for the health sector leveraging this strategy would not be a problem.
Thirdly, is the exploration of big data available. The use of data mining is heavy in this approach with the aim of finding correlations and solutions that cannot be identified easily by use of in-house data. health sector institutions are currently using this strategy to get knowledge on prospects behavior in different medical cases. It aids in identifying new data segments and show the insights concerning the preferences and behaviors of a customer.
Social analytics is another strategy that uses non-transactional data on various social platforms such as Twitter, Facebook, and Google+ (Zhang and Yue, 2014, p. 65). It relies on the various reviews and conversations that come up on such platforms. Social analytics brings out three major analytics: engagement, awareness, and word-of-mouth. In such cases, in-stream data analysis for instance, analysis of sentiments has shown to be effective. It gives deeper understanding about various opinions by people on various health subjects
Finally, is the decision science which uses experiments and examination of non-transactional data such as patient data generated from EHR and diagnostic images. Decision science focuses more on finding the possibilities rather than measurement of already known goals. It uses sentiment and text analysis extensively to understand the opinions and comments of the customers related to service delivery.
Health sector can combine the use of the four big data strategies to meet the various objectives and goals required to improve service delivery.
Another factor that should be considered in big data identifying the infrastructural changes required to leverage big data. Before implementing big data strategy, sometimes it is necessary for the institution to make several infrastructural changes in order to accommodate the complex analysis and algorithms required by big data. Additionally, the various departments in health sector may require integration to gather and streamline the data and convert it to the format that is more usable (Zwitter, 2014, p. 42). Departmental integration is the key to achieving changes at scale.
Catlett (2013) states that data reservoir is necessary in generating growth statistics, collecting information, and general measurements in order to achieve KPIs. The recent expansion in new connection statistics and KPIs is a never-ending process that aims at analyzing corresponding and crude data to determining the way in which upstream data distribution is done on regular premises.
Aligning Business Objectives with Business strategies
The health sector is experiencing rapid changes and the management is seeking to find new strategies to enhance the patient experience, improve results, and reduce costs. The health sector has shifted to patient-centered, value-based care from volume-driven and fee-based systems to drive the health sector operations into unchartered territory regardless of the responsibilities everyone plays in healthcare. Clinical initiatives are required in order to address these changes by aligning them with the business strategies. Healthcare institutions are integrating business principles into strategies target towards patients in all the care settings while maintaining competitiveness and financial viability (Faghmous and Kumar, 2014, p. 159).
In order to align the business goal and objectives with business strategies, it is essential to prioritize clinical initiatives to determine the areal in health sector that requires improvement. Thus, the healthcare leaders should critically evaluate the sectors or departments to focus the limited human and financial resources to make a positive impact on the institutional strategic objectives. Administrative and clinical leaders should find out several techniques to select clinical initiatives to prioritize. Big data dashboards are used in the health industry to select strategic initiatives which are based on core metrics of the CMS like value-based purchasing and safety metric which have a great impact on the patient experience and hospital’s bottom line (Ohlhorst, 2013).
It is equally important and crucial to involve clinicians in the process of decision making. Big data analytics alone would not be successful if the health care professionals are not involved to ascertain the findings. Business strategies will not be in-line with the business objectives if the roadblocks to success are not removed. One of the major barriers is the decision on which strategy to implement first. There exist several strategies that the health sector can adopt but without the relevant data, it would be a big challenge to decide which strategy is the best making it difficult to prioritize them. If the health sector leaders focus more on one strategy, they may face a big risk or threat by ignoring other strategies. Other barriers include pre-acute and post-acute care partners because they may be unaware of the priorities of the health industry even if they are key to future success.
Strategic alignment also entails mutual agreements between the clinicians and executive management on strategic alignment to enhance trust and transparency between the leadership and staff. It is essential to clearly articulate the objective for implementing a particular strategy. Employees and other health care workers are more likely to buy the strategy if they know that it is vital for the institution’s success. Also, it is important to ensure employees understand their roles in aligning the business objectives with business strategies. Big data plays a vital role in ensuring that business objectives with business strategies.
Needed Technology Stack
Currently, record undertaking in data centers are constrained by vast amount of unstructured data. As such, there are several new open supply options and advertisements. Technology stack refers to the underlying mobile or web application elements. They include languages, frameworks, and programs that anything else depend on. Health institutions requires sophisticated technology to be able to analyze available data and give relevant results that can be interpreted and used for decision making (Mayer-SchoÌnberger and Cukier, 2013). For instance, there is need to build a web application using the framework and language Ruby on Rails that will need to access the vast amount of data stored in a server.
Understanding technology stack is very critical because it has a big impact on how the application will work now and in the future. For instance, some big data applications have been designed for high-read processes and thus, they are not efficient when it comes to high-write traffic. High-read systems are recommended for the health sector because when analyzing big data only read operation is executed because data that has been stored in the database is accessed by the application (Mutzel, 2015, p. 216). This is very essential so as to increase the efficiency of the read processes.
Technology stack also affects the product scalability. Scalability of the software for the health sectors utilizing big data is very crucial because of the vast amount of data that keeps on increasing every day. As such, big data analytics software requires to be scalable enough to meet the increasing data. being familiar with the weaknesses and strengths of the tech stack is vital because the application will benefits form the strengths and make it easier to mitigate the weaknesses. Making decisions of the tech stack to be used is necessary before starting to build the application because it may be costly if you have started building the application and you need to change the tech stack (Prajapati, 2013). Big data will help in making an informed decision on the best tech stack.
When choosing a tech stack, it is important that the people involved have experience in the health sector. Additionally, working with developers who have worked in similar project may bring in invaluable benefits and skills. Also, it is important to consider the prices of the tech stacks because other may be free but expensive to maintain and others require high initial investment while it cheap to maintain (Schmarzo, 2013). Flexibility of the tech stack should not be overlooked because some functional changes may be necessary and the tech stack should be flexible enough to allow for the changes to be made.
The technology stack should be easy to integrate with data sources enables better management of data. The technology stack should be able to integrate with tools like SQL and query languages. Additionally, it should support transformation of data within the system. Health industry depends majorly on the information available to make critical decisions that sometimes involve life and death. Big data has the role to better decision-making process. Data should be maintained and stored for future referrals. Currently, in health industry, there is drastic rise in parallelism, scanning big files, workload analyzing and handling, and handling cardinality issues.
Big data enables the health industry to examine data and optimize medical processes and operations to increase patient and stakeholder satisfaction. The biggest challenged faced by tech stack is the ability to fetch real data and feeding in to the system. Moreover, the tech stack chosen should be able to analyze data in real-time making it another bigger challenge because collection, integration, and analysis of the data has to be performed. The tech stack should be able to integrate the existing data using immediate behavior channel. Big data being “big”, the tech stack should be able to load without disruption and maintain a high uptime. Health sector requires high level availability because of the critical decisions that have to be made frequently.
Data Analytics and MDM to support DS&BI
Master Data Management (MDM) combines information technology and business work to foster for accuracy, accountability, uniformity, semantic consistency, and stewardship of the master data sets that are shared. It concentrates on entities with the highest value within an institution. There are many architectures and configurations of implementing MDM solutions including application hubs. When it comes to master data ownerships in health sector placement, functions, management, governance, and stewardship are very critical (Furlow, 2011, p. 34).
In Business intelligent systems (BI), master data is represented as dimensions and is not linked with transactional facts. BI systems are impacted positively by employing the use master data management systems within the health sector. Data definitions and data names attributes in MDM systems are used to describe the master data. Master data definitions can also be referred as shared business vocabulary for the institution, that is, SBV is the metadata of master data. BI and DS systems can take advantage of SBV of the master data to enforce data reuse in all cubes, dimensional models, and business intelligent tools (Kirkpatrick, 2013, p. 3). Reports in BI and DS systems can be better understood by adopting the use of shared business vocabulary. Additionally, it contributes to increased perception of trusted DS and BI and compliance demand.
Data analytics is another aspect that can be used to support DS and BI. Data analysis is the basis of making crucial and important decisions in health sector. Institutions in the health sector need BI systems in order to provided critical information to the clinicians and executive. Data analytics is very essential in business intelligence because BI system cannot give intelligent information without data analytics. A typical BI system has the following components: BI tools, data warehouse, and users with relevant skills. These components greatly depend on data analytics to perform their normal processes. Data analytics is also essential for the DS systems because it’s through analysis of data that the DS systems are able to generate relevant reports. it can be concluded that BI and DS greatly depend on data analytics.
NoSQL for Big Data Analytics
NoSQL are systems that non-relations and distributed which have been designed for high-performance, massively parallel, and large-scale data storage and data processing across several commodity servers. NoSQL is on the rise because of the need of performance, agility, scalability, and supports a variety of use cases like real-time predictive and explanatory analytics (Aggarwal and Sonika, 2016, p. 121). NoSQL can only be scaled horizontally and can span across millions and billions of users reading and writing to the databases at the same time. In Big Data analytics NoSQL comes in handy when carrying out online advertisement, social applications, and data Archiving. This is because NoSQL allows reading and writing to the database simultaneously while maintaining database integrity (accuracy and completeness) to ensure that data is in update real-time (Shen, Yu, Wang, Nie and Kou, 2014, p. 1799).
Several operations that can be done on a relational database (SQL) and almost impossible in the non-relational database (NoSQL) and get worse as it is scaled out (Lomotey and Deters, 2015, p. 23). NoSQL well-fits big data analytics because of the ability to facilitate real-time applications to interface well with external entities to the organization.
NoSQL Databases and its use in Big Data
In health sector, NoSQL can be very critical in the operational needs. This is because NoSQL supports databases that are column oriented making it easy to analyze the available medical data to make important decisions. Secondly, NoSQL is able to operate on unrelated and unstructured data which is a common attribute in health sector data. To facilitate horizontal scalability and improve speed NoSQL databases dropped some traditional database features (Lourenço, Cabral, Carreiro, Vieira and Bernardino, 2015, p. 403). Horizontal scalability and speed are two vital aspects on health sectors to allow different institutions and professionals in health sector to share, read, and write data regardless of the locations. Additionally, it is safer, cheaper, and flexible to allow addition of new functionalities to the existing program (Sharma, et al., 2015, p. 201).
Role of Social media in organization’s decision-making process
In today’s generation, social networking has risen to be part of our daily lives. The social networking sites such as Twitter, Facebook, Instagram, and LinkedIn have a great impact on the customer choices and decisions (Deans, 2012). Businesses are now shifting to social media marketing because it is the cheapest way to reach to a wider audience simultaneously around the world. The penetration of social media in our daily lives and work is increasing and thus, has an impact on business and individual decision-making process (Welles, 2014, p. 52). Social media greatly affects how organizations are making decisions. Organizations are joining the various social media platforms for the purpose of interacting with their target audience/customers and also promote their brands and services (Peña, 2016, p. 740). Social media has disrupted the traditional process of making decisions and several businesses are now making decisions based on the data collected from the social media networks.
Social media have had a great impact on how organizations carry out their daily operations and interact with their audience. It is a platform where experiences, ideologies, and exchange of knowledge occurs (Power and Phillips-Wren, 2011, p. 250). Business to business and business to customer relationships can be enhanced by social media.
Value Creation Procedure of Big Data
The following factors can be taken by organizations and businesses to create value by harnessing big data: data contextualization, democratization, experimentation, and execution.
Data democratization- is employed to integrate data in the organization and allow workers to access and get better insights of the data when needed.
Data contextualization- is employed to give a meaning to the interpreted data which can be used to execute an action. Organizations collates massive amount of data from different sources such as behaviors and preferences of the customers, market demand, and shifting customer needs. The capacity to point out contextual clues to get an overall customer opinion is linked to better value creation.
Data experimentation- enables organizations and businesses to employ ‘trial and error’ and experiment continuously using the data and monitoring changes. Coupling data accessibility and trial and error in organizations increases the chances of transforming big data to get better value.
Data execution- is employed to convert data insights into actions that improves customer experience and identify new opportunities, thus, creating value. Value creation can further be enhanced in organizations by not only focusing on internal data but also sourcing external data collaboratively where dynamic and diversified knowledge base are rendered to be heterogeneous network resource which makes it challenging and rare for competitors to copy.
Reflection and Conclusion
Institutions in health sectors can progressively implement big data solutions by first stating how users will analyze and use information and how institutions will create value from big data. Big data has had a major impact on the decisions that are being made by institutions and businesses. Additionally, big data analysis is very crucial identifying customer preference and behaviors to allow organizations to restructure in order to meet these needs. According to me, I believe that health institutions and leaders need to do away with the traditional mindset and adopt the new techniques and methodologies to overcome the challenges to enhance sharing of data with proper security features and work together towards attaining the objective of delivering better services at a lower cost.
References List
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Catlett, C. (2013). Cloud computing and big data. Amsterdam: IOS Press.
Deans, P. (2012). Integration of Study Abroad with Social Media Technologies and Decision-Making Applications. Decision Sciences Journal of Innovative Education, 10(3), pp.299-336.
Dhar, V. (2014). Why Big Data = Big Deal. Big Data, 2(2), pp.55-56.
Faghmous, J. and Kumar, V. (2014). A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science. Big Data, 2(3), pp.155-163.
Furlow, G. (2011). The case for building a data warehouse. IT Professional, 3(4), pp.31-34.
Kirkpatrick, R. (2013). Big Data for Development. Big Data, 1(1), pp.3-4.
Lomotey, R. and Deters, R. (2015). Terms analytics service for CouchDB: a document-based NoSQL. International Journal of Big Data Intelligence, 2(1), p.23.
Lourenço, J., Cabral, B., Carreiro, P., Vieira, M. and Bernardino, J. (2015). Choosing the right NoSQL database for the job: a quality attribute evaluation. Journal of Big Data, 2(1), pp.399-407.
Mayer-Schönberger, V. and Cukier, K. (2013). Big data. Boston: Houghton Mifflin Harcourt.
Mutzel, S. (2015). Facing Big Data: Making sociology relevant. Big Data & Society, 2(2), pp.212-217.
Ohlhorst, F. (2013). Big data analytics. Hoboken, N.J.: John Wiley & Sons.
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Power, D. and Phillips-Wren, G. (2011). Impact of Social Media and Web 2.0 on Decision-Making. Journal of Decision Systems, 20(3), pp.249-261.
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Schmarzo, B. (2013). Big Data. Hoboken: Wiley.
Sharma, S., Tim, U., Gadia, S., Wong, J., Shandilya, R. and Peddoju, S. (2015). Classification and comparison of NoSQL big data models. International Journal of Big Data Intelligence, 2(3), p.201.
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Zwitter, A. (2014). Big Data ethics. Big Data & Society, 1(2), pp.20-53.
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