Assignment on Life Cycle of Data

For business data management organizations are having challenging situations nowadays. It can be noted that before managing the important data there is a need to understand the life cycle of the data. Through the help of understanding the life cycle of the data, it will become quite simple to manage the data properly. Particularly, healthcare data management sciences are highly important to make advance decisions and ensure appropriate treatment for patients.     

The Business Problem of Life Cycle of Data

Healthcare centers, pharmaceutical companies and hospitals are required to provide proper solutions to the human being for the betterment of health and control of diseases. However, without proper databases and available information pools, they cannot make a prediction about human health and future requirements of the healthcare industry. For instance, “who many individuals may suffer from fever and other winter health problems in this winter?” question is fully relying on previous historical information collected in previous winter seasons. Apart from seasonal diseases, without having statistical data and records of all patients for a health issue or diseases, pharmaceutical companies cannot produce the required amount of medicine. Additionally, medical researchers cannot identify unaddressed health issues to conduct research and find treatment. The business problem is how healthcare industries can eliminate such problems and work properly to meet their requirements. What methods and strategies they need to follow for improved outcomes?    

Data Collection Methodology of Life Cycle of Data

To investigate and address this business problem a secondary research study is conducted on healthcare data management sciences. Several research articles, website articles, and books are reviewed to find out a solution for this research problem. The results and outcomes are drawn based on the secondary data extracted from these creditable resources of literature review.  

Results of Life Cycle of Data

In accordance with the research findings, various healthcare data management strategies can be used for record-keeping and collection of quantitative data about healthcare issues from the selected geographical segment. Share healthcare databases can work as pools of information for healthcare industries to understand the market demand for a particular health issue treatment, medicine, and further research. Conclusively, appropriately collected and managed data can resolve this issue and assist the healthcare industry to meet with future demand easily. However, for this solution data life cycle is need to be followed properly.

Data capture is the first stage of the data life cycle. The data capture stage is about experiencing data pass from the firewalls of relevant enterprises. In this stage healthcare industries, can acquire and capture data by 3 ways which include data acquisition, data entry, and signal reception. In the data acquisition method, healthcare organizations can capture data from the enterprise and outside the organizations (Bloomberg.com, 2015). Inside information such as patient profiles and recording keeping on databases can provide authentic information about current healthcare issues in a country or society.

Data collected and captured from and outside healthcare centers are required to be maintained at this stage for further processing and use. However, in this processing activities, data values cannot be driven to ensure appropriate completion of this data maintenance stage. In this stage, the most critical task is to monitor data supply and data synthesis.

 In the data synthesis stage, inductive logic can be used to define and create data values. However, in this process, other data extracted from external and internal resources of the healthcare centre can be used as input for data synthesis. Data synthesis can be used in the data life cycle to develop models for analytics such as decision-making model, healthcare risk models and statistical model of current healthcare issues. Then, data usage stage with developed data values supports the functions of healthcare centres by enabling them to use this data to run and manage different operations of the healthcare industry. Here businesses should ensure that data is legal and it does not contain sensitive or personal information about patients (Hakikur Rahman, 2013).

In the next data publication stage, single data set value can be sent in the external environment of the healthcare centre for publication. In this stage, key challenges regarding data breaches and data thefts of managed data need to control. Published data and information from hospitals and healthcare centers will create a pool for information that will benefit the whole healthcare industry to understand requirements and current condition of healthcare issues. Then in archive stage, the healthcare industry will archive all relevant data values by copying important data and information regarding the environment to make sure its availability on digital platforms such as healthcare databases (Bhatnagar, 2013).    

Outcomes/conclusion of Life Cycle of Data

The healthcare industry issue regarding the unavailability of proper information about current healthcare issues and forecasting about future healthcare issues is quite critical for the better health of the human being. Healthcare centers and pharmaceutical industries are supposed to meet the requirements of healthcare issues properly to ensure health safety and treatment of patients. According to research outcomes, data lifecycle management in health sciences can enable healthcare industries to meet with requirements and ensure better performance outcomes for human health safety. By following discussed data life cycle healthcare centres can create a pool of information and healthcare database which will support researchers to identify gaps for further medical research on healthcare issues as well as assist pharmaceutical industries to produce require medicine in predicted volume. Conclusively, research suggests that business problem identified in this research project can be resolved through the following data life cycle stages properly and accurately while developing a healthcare database or digital information pool.   

References of Life Cycle of Data

Bhatnagar, V. (2013). Data Mining in Dynamic Social Networks and Fuzzy Systems. IGI Global.

Bloomberg.com. (2015). 7 phases of a data life cycle. Retrieved from www.bloomberg.com: https://www.bloomberg.com/professional/blog/7-phases-of-a-data-life-cycle/

Hakikur Rahman, I. R. (2013). Ethical Data Mining Applications for Socio-Economic Development. Idea Group Inc (IGI).